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z9KDNMrYahS`>!gRja!i_s*O8;D)PeZLKQv1JxCSRz#Twip0!?3{(NuyOV)3@<{zkA zItAO0GlpDup*L9f^W(j&D<R-Ps)HWgWOK-=(ET zI`#P&Lo(H9&gK}4X{1{xUeB*ysWi^m*zQwTSK6-qg#>@=v4Z?gL8a!ai delta 64 zcmca`is`~BrVTBOhPjDV7HJl_>H6j=#s3*?8N}M)f9jL diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index 203dae244d2ad8d818ffffc805f09e5a7c73f405..cb712ff8bbdde1dec0872280d9bd1f310c05d7ea 100644 GIT binary patch delta 63 zcmca|oAJtR#tn-Z4NLN}^GXWiEA>-M3{otBFfB3B)FR0&*~}!_Jk>18+|0=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -142,10 +142,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:39.546763Z", - "iopub.status.busy": "2024-05-02T13:37:39.546284Z", - "iopub.status.idle": "2024-05-02T13:37:39.564144Z", - "shell.execute_reply": "2024-05-02T13:37:39.563693Z" + "iopub.execute_input": "2024-05-03T22:20:49.720716Z", + "iopub.status.busy": "2024-05-03T22:20:49.720228Z", + "iopub.status.idle": "2024-05-03T22:20:49.739927Z", + "shell.execute_reply": "2024-05-03T22:20:49.739408Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:39.566210Z", - "iopub.status.busy": "2024-05-02T13:37:39.565866Z", - "iopub.status.idle": "2024-05-02T13:37:39.568803Z", - "shell.execute_reply": "2024-05-02T13:37:39.568375Z" + "iopub.execute_input": "2024-05-03T22:20:49.742704Z", + "iopub.status.busy": "2024-05-03T22:20:49.742139Z", + "iopub.status.idle": "2024-05-03T22:20:49.745441Z", + "shell.execute_reply": "2024-05-03T22:20:49.744874Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:39.570761Z", - "iopub.status.busy": "2024-05-02T13:37:39.570457Z", - "iopub.status.idle": "2024-05-02T13:37:39.659272Z", - "shell.execute_reply": "2024-05-02T13:37:39.658831Z" + "iopub.execute_input": "2024-05-03T22:20:49.747634Z", + "iopub.status.busy": "2024-05-03T22:20:49.747320Z", + "iopub.status.idle": "2024-05-03T22:20:49.806186Z", + "shell.execute_reply": "2024-05-03T22:20:49.805597Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:39.661380Z", - "iopub.status.busy": "2024-05-02T13:37:39.661046Z", - "iopub.status.idle": "2024-05-02T13:37:39.837557Z", - "shell.execute_reply": "2024-05-02T13:37:39.837059Z" + "iopub.execute_input": "2024-05-03T22:20:49.808922Z", + "iopub.status.busy": "2024-05-03T22:20:49.808402Z", + "iopub.status.idle": "2024-05-03T22:20:49.999289Z", + "shell.execute_reply": "2024-05-03T22:20:49.998695Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:39.839579Z", - "iopub.status.busy": "2024-05-02T13:37:39.839400Z", - "iopub.status.idle": "2024-05-02T13:37:40.080704Z", - "shell.execute_reply": "2024-05-02T13:37:40.080115Z" + "iopub.execute_input": "2024-05-03T22:20:50.001853Z", + "iopub.status.busy": "2024-05-03T22:20:50.001474Z", + "iopub.status.idle": "2024-05-03T22:20:50.256968Z", + "shell.execute_reply": "2024-05-03T22:20:50.256329Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:40.083030Z", - "iopub.status.busy": "2024-05-02T13:37:40.082712Z", - "iopub.status.idle": "2024-05-02T13:37:40.087047Z", - "shell.execute_reply": "2024-05-02T13:37:40.086575Z" + "iopub.execute_input": "2024-05-03T22:20:50.259220Z", + "iopub.status.busy": "2024-05-03T22:20:50.258872Z", + "iopub.status.idle": "2024-05-03T22:20:50.263472Z", + "shell.execute_reply": "2024-05-03T22:20:50.262965Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:40.089042Z", - "iopub.status.busy": "2024-05-02T13:37:40.088636Z", - "iopub.status.idle": "2024-05-02T13:37:40.094738Z", - "shell.execute_reply": "2024-05-02T13:37:40.094308Z" + "iopub.execute_input": "2024-05-03T22:20:50.265646Z", + "iopub.status.busy": "2024-05-03T22:20:50.265295Z", + "iopub.status.idle": "2024-05-03T22:20:50.271613Z", + "shell.execute_reply": "2024-05-03T22:20:50.271155Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:40.096709Z", - "iopub.status.busy": "2024-05-02T13:37:40.096404Z", - "iopub.status.idle": "2024-05-02T13:37:40.098892Z", - "shell.execute_reply": "2024-05-02T13:37:40.098467Z" + "iopub.execute_input": "2024-05-03T22:20:50.273894Z", + "iopub.status.busy": "2024-05-03T22:20:50.273553Z", + "iopub.status.idle": "2024-05-03T22:20:50.276357Z", + "shell.execute_reply": "2024-05-03T22:20:50.275889Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:40.100883Z", - "iopub.status.busy": "2024-05-02T13:37:40.100570Z", - "iopub.status.idle": "2024-05-02T13:37:48.280445Z", - "shell.execute_reply": "2024-05-02T13:37:48.279822Z" + "iopub.execute_input": "2024-05-03T22:20:50.278464Z", + "iopub.status.busy": "2024-05-03T22:20:50.278132Z", + "iopub.status.idle": "2024-05-03T22:20:58.837552Z", + "shell.execute_reply": "2024-05-03T22:20:58.836915Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:48.283163Z", - "iopub.status.busy": "2024-05-02T13:37:48.282636Z", - "iopub.status.idle": "2024-05-02T13:37:48.289498Z", - "shell.execute_reply": "2024-05-02T13:37:48.288979Z" + "iopub.execute_input": "2024-05-03T22:20:58.840267Z", + "iopub.status.busy": "2024-05-03T22:20:58.839876Z", + "iopub.status.idle": "2024-05-03T22:20:58.847049Z", + "shell.execute_reply": "2024-05-03T22:20:58.846527Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:48.291518Z", - "iopub.status.busy": "2024-05-02T13:37:48.291340Z", - "iopub.status.idle": "2024-05-02T13:37:48.295047Z", - "shell.execute_reply": "2024-05-02T13:37:48.294605Z" + "iopub.execute_input": "2024-05-03T22:20:58.849050Z", + "iopub.status.busy": "2024-05-03T22:20:58.848866Z", + "iopub.status.idle": "2024-05-03T22:20:58.852924Z", + "shell.execute_reply": "2024-05-03T22:20:58.852358Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:48.296793Z", - "iopub.status.busy": "2024-05-02T13:37:48.296621Z", - "iopub.status.idle": "2024-05-02T13:37:48.300060Z", - "shell.execute_reply": "2024-05-02T13:37:48.299610Z" + "iopub.execute_input": "2024-05-03T22:20:58.855016Z", + "iopub.status.busy": "2024-05-03T22:20:58.854711Z", + "iopub.status.idle": "2024-05-03T22:20:58.858006Z", + "shell.execute_reply": "2024-05-03T22:20:58.857501Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:48.301880Z", - "iopub.status.busy": "2024-05-02T13:37:48.301712Z", - "iopub.status.idle": "2024-05-02T13:37:48.304699Z", - "shell.execute_reply": "2024-05-02T13:37:48.304258Z" + "iopub.execute_input": "2024-05-03T22:20:58.860256Z", + "iopub.status.busy": "2024-05-03T22:20:58.859804Z", + "iopub.status.idle": "2024-05-03T22:20:58.863171Z", + "shell.execute_reply": "2024-05-03T22:20:58.862732Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:48.306442Z", - "iopub.status.busy": "2024-05-02T13:37:48.306271Z", - "iopub.status.idle": "2024-05-02T13:37:48.314058Z", - "shell.execute_reply": "2024-05-02T13:37:48.313603Z" + "iopub.execute_input": "2024-05-03T22:20:58.865244Z", + "iopub.status.busy": "2024-05-03T22:20:58.864931Z", + "iopub.status.idle": "2024-05-03T22:20:58.873645Z", + "shell.execute_reply": "2024-05-03T22:20:58.873063Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:48.315848Z", - "iopub.status.busy": "2024-05-02T13:37:48.315678Z", - "iopub.status.idle": "2024-05-02T13:37:48.318297Z", - "shell.execute_reply": "2024-05-02T13:37:48.317836Z" + "iopub.execute_input": "2024-05-03T22:20:58.875848Z", + "iopub.status.busy": "2024-05-03T22:20:58.875637Z", + "iopub.status.idle": "2024-05-03T22:20:58.878803Z", + "shell.execute_reply": "2024-05-03T22:20:58.878298Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:48.320093Z", - "iopub.status.busy": "2024-05-02T13:37:48.319925Z", - "iopub.status.idle": "2024-05-02T13:37:48.441214Z", - "shell.execute_reply": "2024-05-02T13:37:48.440667Z" + "iopub.execute_input": "2024-05-03T22:20:58.881152Z", + "iopub.status.busy": "2024-05-03T22:20:58.880794Z", + "iopub.status.idle": "2024-05-03T22:20:59.014729Z", + "shell.execute_reply": "2024-05-03T22:20:59.014156Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:48.443331Z", - "iopub.status.busy": "2024-05-02T13:37:48.443015Z", - "iopub.status.idle": "2024-05-02T13:37:48.545728Z", - "shell.execute_reply": "2024-05-02T13:37:48.545193Z" + "iopub.execute_input": "2024-05-03T22:20:59.017194Z", + "iopub.status.busy": "2024-05-03T22:20:59.016781Z", + "iopub.status.idle": "2024-05-03T22:20:59.123045Z", + "shell.execute_reply": "2024-05-03T22:20:59.122411Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:48.548016Z", - "iopub.status.busy": "2024-05-02T13:37:48.547588Z", - "iopub.status.idle": "2024-05-02T13:37:49.040465Z", - "shell.execute_reply": "2024-05-02T13:37:49.039932Z" + "iopub.execute_input": "2024-05-03T22:20:59.125601Z", + "iopub.status.busy": "2024-05-03T22:20:59.125265Z", + "iopub.status.idle": "2024-05-03T22:20:59.623879Z", + "shell.execute_reply": "2024-05-03T22:20:59.623233Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:49.043117Z", - "iopub.status.busy": "2024-05-02T13:37:49.042657Z", - "iopub.status.idle": "2024-05-02T13:37:49.131459Z", - "shell.execute_reply": "2024-05-02T13:37:49.130787Z" + "iopub.execute_input": "2024-05-03T22:20:59.626604Z", + "iopub.status.busy": "2024-05-03T22:20:59.626200Z", + "iopub.status.idle": "2024-05-03T22:20:59.735511Z", + "shell.execute_reply": "2024-05-03T22:20:59.734904Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:49.133661Z", - "iopub.status.busy": "2024-05-02T13:37:49.133339Z", - "iopub.status.idle": "2024-05-02T13:37:49.141955Z", - "shell.execute_reply": "2024-05-02T13:37:49.141411Z" + "iopub.execute_input": "2024-05-03T22:20:59.737975Z", + "iopub.status.busy": "2024-05-03T22:20:59.737507Z", + "iopub.status.idle": "2024-05-03T22:20:59.746497Z", + "shell.execute_reply": "2024-05-03T22:20:59.746018Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:49.143844Z", - "iopub.status.busy": "2024-05-02T13:37:49.143670Z", - "iopub.status.idle": "2024-05-02T13:37:49.146220Z", - "shell.execute_reply": "2024-05-02T13:37:49.145783Z" + "iopub.execute_input": "2024-05-03T22:20:59.748549Z", + "iopub.status.busy": "2024-05-03T22:20:59.748357Z", + "iopub.status.idle": "2024-05-03T22:20:59.751198Z", + "shell.execute_reply": "2024-05-03T22:20:59.750759Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:49.148144Z", - "iopub.status.busy": "2024-05-02T13:37:49.147850Z", - "iopub.status.idle": "2024-05-02T13:37:54.645951Z", - "shell.execute_reply": "2024-05-02T13:37:54.645348Z" + "iopub.execute_input": "2024-05-03T22:20:59.753223Z", + "iopub.status.busy": "2024-05-03T22:20:59.753038Z", + "iopub.status.idle": "2024-05-03T22:21:05.304818Z", + "shell.execute_reply": "2024-05-03T22:21:05.304193Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:54.648455Z", - "iopub.status.busy": "2024-05-02T13:37:54.648005Z", - "iopub.status.idle": "2024-05-02T13:37:54.656640Z", - "shell.execute_reply": "2024-05-02T13:37:54.656181Z" + "iopub.execute_input": "2024-05-03T22:21:05.307121Z", + "iopub.status.busy": "2024-05-03T22:21:05.306897Z", + "iopub.status.idle": "2024-05-03T22:21:05.316181Z", + "shell.execute_reply": "2024-05-03T22:21:05.315729Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:54.658648Z", - "iopub.status.busy": "2024-05-02T13:37:54.658337Z", - "iopub.status.idle": "2024-05-02T13:37:54.723492Z", - "shell.execute_reply": "2024-05-02T13:37:54.722863Z" + "iopub.execute_input": "2024-05-03T22:21:05.318230Z", + "iopub.status.busy": "2024-05-03T22:21:05.318047Z", + "iopub.status.idle": "2024-05-03T22:21:05.387573Z", + "shell.execute_reply": "2024-05-03T22:21:05.387062Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 58774a79b..caa3f2c41 100644 --- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:57.804859Z", - "iopub.status.busy": "2024-05-02T13:37:57.804392Z", - "iopub.status.idle": "2024-05-02T13:37:59.142610Z", - "shell.execute_reply": "2024-05-02T13:37:59.141972Z" + "iopub.execute_input": "2024-05-03T22:21:09.354623Z", + "iopub.status.busy": "2024-05-03T22:21:09.354237Z", + "iopub.status.idle": "2024-05-03T22:21:10.777441Z", + "shell.execute_reply": "2024-05-03T22:21:10.776744Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:59.144920Z", - "iopub.status.busy": "2024-05-02T13:37:59.144750Z", - "iopub.status.idle": "2024-05-02T13:38:40.069504Z", - "shell.execute_reply": "2024-05-02T13:38:40.068821Z" + "iopub.execute_input": "2024-05-03T22:21:10.780051Z", + "iopub.status.busy": "2024-05-03T22:21:10.779824Z", + "iopub.status.idle": "2024-05-03T22:21:54.904003Z", + "shell.execute_reply": "2024-05-03T22:21:54.903350Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:38:40.072006Z", - "iopub.status.busy": "2024-05-02T13:38:40.071649Z", - "iopub.status.idle": "2024-05-02T13:38:41.144536Z", - "shell.execute_reply": "2024-05-02T13:38:41.143998Z" + "iopub.execute_input": "2024-05-03T22:21:54.906583Z", + "iopub.status.busy": "2024-05-03T22:21:54.906244Z", + "iopub.status.idle": "2024-05-03T22:21:56.059453Z", + "shell.execute_reply": "2024-05-03T22:21:56.058793Z" }, "nbsphinx": "hidden" }, @@ -111,7 +111,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -137,10 +137,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:38:41.147135Z", - "iopub.status.busy": "2024-05-02T13:38:41.146624Z", - "iopub.status.idle": "2024-05-02T13:38:41.149860Z", - "shell.execute_reply": "2024-05-02T13:38:41.149339Z" + "iopub.execute_input": "2024-05-03T22:21:56.062349Z", + "iopub.status.busy": "2024-05-03T22:21:56.061885Z", + "iopub.status.idle": "2024-05-03T22:21:56.065288Z", + "shell.execute_reply": "2024-05-03T22:21:56.064811Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:38:41.151955Z", - "iopub.status.busy": "2024-05-02T13:38:41.151564Z", - "iopub.status.idle": "2024-05-02T13:38:41.155359Z", - "shell.execute_reply": "2024-05-02T13:38:41.154844Z" + "iopub.execute_input": "2024-05-03T22:21:56.067491Z", + "iopub.status.busy": "2024-05-03T22:21:56.067170Z", + 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"_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_804b56b5e24a4e37a77b275cc294d902", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2128f699ffc84b23a0f4b345b242b0a4", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index 5890cf2c4..fbe7fe68d 100644 --- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:18.838890Z", - "iopub.status.busy": "2024-05-02T13:40:18.838736Z", - "iopub.status.idle": "2024-05-02T13:40:20.303449Z", - "shell.execute_reply": "2024-05-02T13:40:20.302883Z" + "iopub.execute_input": "2024-05-03T22:23:37.109560Z", + "iopub.status.busy": "2024-05-03T22:23:37.109386Z", + "iopub.status.idle": "2024-05-03T22:23:38.354224Z", + "shell.execute_reply": "2024-05-03T22:23:38.353645Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-05-02 13:40:18-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-05-03 22:23:37-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,14 +94,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.251, 2400:52e0:1a00::894:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.251|:443... connected.\r\n" + "185.93.1.251, 2400:52e0:1a00::1068:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.251|:443... " ] }, { "name": "stdout", "output_type": "stream", "text": [ + "connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -122,10 +123,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 90%[=================> ] 865.67K 4.10MB/s \r", - "conll2003.zip 100%[===================>] 959.94K 4.49MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-05-02 13:40:19 (4.49 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-05-03 22:23:37 (6.53 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -137,14 +137,7 @@ "Archive: conll2003.zip\r\n", " inflating: data/metadata \r\n", " inflating: data/test.txt \r\n", - " inflating: data/train.txt " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", + " inflating: data/train.txt \r\n", " inflating: data/valid.txt \r\n" ] }, @@ -152,9 +145,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-05-02 13:40:19-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.101.65, 3.5.28.252, 52.216.177.115, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.101.65|:443... connected.\r\n", + "--2024-05-03 22:23:37-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.230.89, 54.231.198.169, 3.5.20.164, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.230.89|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -177,7 +170,7 @@ "\r", "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", "\r\n", - "2024-05-02 13:40:20 (120 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-05-03 22:23:38 (151 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -194,10 +187,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:20.305771Z", - "iopub.status.busy": "2024-05-02T13:40:20.305463Z", - "iopub.status.idle": "2024-05-02T13:40:21.515797Z", - "shell.execute_reply": "2024-05-02T13:40:21.515245Z" + "iopub.execute_input": "2024-05-03T22:23:38.356845Z", + "iopub.status.busy": "2024-05-03T22:23:38.356625Z", + "iopub.status.idle": "2024-05-03T22:23:39.676466Z", + "shell.execute_reply": "2024-05-03T22:23:39.675901Z" }, "nbsphinx": "hidden" }, @@ -208,7 +201,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -234,10 +227,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:21.518227Z", - "iopub.status.busy": "2024-05-02T13:40:21.517942Z", - "iopub.status.idle": "2024-05-02T13:40:21.521185Z", - "shell.execute_reply": "2024-05-02T13:40:21.520768Z" + "iopub.execute_input": "2024-05-03T22:23:39.678961Z", + "iopub.status.busy": "2024-05-03T22:23:39.678662Z", + "iopub.status.idle": "2024-05-03T22:23:39.682231Z", + "shell.execute_reply": "2024-05-03T22:23:39.681754Z" } }, "outputs": [], @@ -287,10 +280,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:21.523266Z", - "iopub.status.busy": "2024-05-02T13:40:21.522960Z", - "iopub.status.idle": "2024-05-02T13:40:21.526363Z", - "shell.execute_reply": "2024-05-02T13:40:21.525926Z" + "iopub.execute_input": "2024-05-03T22:23:39.684500Z", + "iopub.status.busy": "2024-05-03T22:23:39.684094Z", + "iopub.status.idle": "2024-05-03T22:23:39.687145Z", + "shell.execute_reply": "2024-05-03T22:23:39.686710Z" }, "nbsphinx": "hidden" }, @@ -308,10 +301,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:21.528351Z", - "iopub.status.busy": "2024-05-02T13:40:21.528032Z", - "iopub.status.idle": "2024-05-02T13:40:30.505208Z", - "shell.execute_reply": "2024-05-02T13:40:30.504621Z" + "iopub.execute_input": "2024-05-03T22:23:39.689097Z", + "iopub.status.busy": "2024-05-03T22:23:39.688919Z", + "iopub.status.idle": "2024-05-03T22:23:48.757440Z", + "shell.execute_reply": "2024-05-03T22:23:48.756894Z" } }, "outputs": [], @@ -385,10 +378,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:30.507896Z", - "iopub.status.busy": "2024-05-02T13:40:30.507445Z", - "iopub.status.idle": "2024-05-02T13:40:30.512858Z", - "shell.execute_reply": "2024-05-02T13:40:30.512422Z" + "iopub.execute_input": "2024-05-03T22:23:48.759866Z", + "iopub.status.busy": "2024-05-03T22:23:48.759658Z", + "iopub.status.idle": "2024-05-03T22:23:48.765417Z", + "shell.execute_reply": "2024-05-03T22:23:48.764899Z" }, "nbsphinx": "hidden" }, @@ -428,10 +421,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:30.514973Z", - "iopub.status.busy": "2024-05-02T13:40:30.514562Z", - "iopub.status.idle": "2024-05-02T13:40:30.847546Z", - "shell.execute_reply": "2024-05-02T13:40:30.847003Z" + "iopub.execute_input": "2024-05-03T22:23:48.767520Z", + "iopub.status.busy": "2024-05-03T22:23:48.767327Z", + "iopub.status.idle": "2024-05-03T22:23:49.131868Z", + "shell.execute_reply": "2024-05-03T22:23:49.131295Z" } }, "outputs": [], @@ -468,10 +461,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:30.849872Z", - "iopub.status.busy": "2024-05-02T13:40:30.849552Z", - "iopub.status.idle": "2024-05-02T13:40:30.853912Z", - "shell.execute_reply": "2024-05-02T13:40:30.853373Z" + "iopub.execute_input": "2024-05-03T22:23:49.134505Z", + "iopub.status.busy": "2024-05-03T22:23:49.134179Z", + "iopub.status.idle": "2024-05-03T22:23:49.138593Z", + "shell.execute_reply": "2024-05-03T22:23:49.138045Z" } }, "outputs": [ @@ -543,10 +536,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:30.855948Z", - "iopub.status.busy": "2024-05-02T13:40:30.855548Z", - "iopub.status.idle": "2024-05-02T13:40:33.093365Z", - "shell.execute_reply": "2024-05-02T13:40:33.092608Z" + "iopub.execute_input": "2024-05-03T22:23:49.140885Z", + "iopub.status.busy": "2024-05-03T22:23:49.140596Z", + "iopub.status.idle": "2024-05-03T22:23:51.622613Z", + "shell.execute_reply": "2024-05-03T22:23:51.621823Z" } }, "outputs": [], @@ -568,10 +561,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:33.096362Z", - "iopub.status.busy": "2024-05-02T13:40:33.095810Z", - "iopub.status.idle": "2024-05-02T13:40:33.099757Z", - "shell.execute_reply": "2024-05-02T13:40:33.099252Z" + "iopub.execute_input": "2024-05-03T22:23:51.625864Z", + "iopub.status.busy": "2024-05-03T22:23:51.625130Z", + "iopub.status.idle": "2024-05-03T22:23:51.629398Z", + "shell.execute_reply": "2024-05-03T22:23:51.628892Z" } }, "outputs": [ @@ -607,10 +600,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:33.101776Z", - "iopub.status.busy": "2024-05-02T13:40:33.101453Z", - "iopub.status.idle": "2024-05-02T13:40:33.106479Z", - "shell.execute_reply": "2024-05-02T13:40:33.105927Z" + "iopub.execute_input": "2024-05-03T22:23:51.631367Z", + "iopub.status.busy": "2024-05-03T22:23:51.631188Z", + "iopub.status.idle": "2024-05-03T22:23:51.636936Z", + "shell.execute_reply": "2024-05-03T22:23:51.636445Z" } }, "outputs": [ @@ -788,10 +781,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:33.108502Z", - "iopub.status.busy": "2024-05-02T13:40:33.108179Z", - "iopub.status.idle": "2024-05-02T13:40:33.133976Z", - "shell.execute_reply": "2024-05-02T13:40:33.133529Z" + "iopub.execute_input": "2024-05-03T22:23:51.638922Z", + "iopub.status.busy": "2024-05-03T22:23:51.638726Z", + "iopub.status.idle": "2024-05-03T22:23:51.667029Z", + "shell.execute_reply": "2024-05-03T22:23:51.666455Z" } }, "outputs": [ @@ -893,10 +886,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:33.135992Z", - "iopub.status.busy": "2024-05-02T13:40:33.135668Z", - "iopub.status.idle": "2024-05-02T13:40:33.139857Z", - "shell.execute_reply": "2024-05-02T13:40:33.139427Z" + "iopub.execute_input": "2024-05-03T22:23:51.669153Z", + "iopub.status.busy": "2024-05-03T22:23:51.668968Z", + "iopub.status.idle": "2024-05-03T22:23:51.674198Z", + "shell.execute_reply": "2024-05-03T22:23:51.673668Z" } }, "outputs": [ @@ -970,10 +963,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:33.141842Z", - "iopub.status.busy": "2024-05-02T13:40:33.141534Z", - "iopub.status.idle": "2024-05-02T13:40:34.486384Z", - "shell.execute_reply": "2024-05-02T13:40:34.485763Z" + "iopub.execute_input": "2024-05-03T22:23:51.676452Z", + "iopub.status.busy": "2024-05-03T22:23:51.676059Z", + "iopub.status.idle": "2024-05-03T22:23:53.102278Z", + "shell.execute_reply": "2024-05-03T22:23:53.101726Z" } }, "outputs": [ @@ -1145,10 +1138,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:34.488408Z", - "iopub.status.busy": "2024-05-02T13:40:34.488226Z", - "iopub.status.idle": "2024-05-02T13:40:34.492323Z", - "shell.execute_reply": "2024-05-02T13:40:34.491867Z" + "iopub.execute_input": 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zcmbRJglp;(t_>$R4RaH#EYd7;)Ah|$j17#F49pD8Oe|89ER4*6(Ad<-C@s~}AjR0k zz%a?sATcf3c=J!r-=^}WtPBh`1x2ZLT)MhO21X{j2Bx|OMhb?;Rwf2kh8B|*y+bFj za#dosur!&x(A#CQUHGQS$K3e!O!Q1FeVz1+6@VZ!Pr*RXsF;fj(*R2a4Uo#o2Oa-# z!A-TCY!JCn)6h)M(85U1(7;g7%v49gSkF|?$U@J^)JR9c)JV?=ByDc4XJ}|Td7^U! zFN*5P@lksxfA!SmhZ$*PprB!BWH8y#%VDy}Z9gn01|iA9T-2f)-#<3asX5Gy3?R@d&A3#WX%hgd CvqAv? diff --git a/master/_modules/cleanlab/dataset.html b/master/_modules/cleanlab/dataset.html index 1abcabe98..dd1fc0d7c 100644 --- a/master/_modules/cleanlab/dataset.html +++ b/master/_modules/cleanlab/dataset.html @@ -604,7 +604,9 @@

Source code for cleanlab.dataset

 from typing import Optional, cast
 import numpy as np
 import pandas as pd
+
 from cleanlab.count import estimate_joint, num_label_issues
+from cleanlab.internal.constants import EPSILON
 
 
 
[docs]def rank_classes_by_label_quality(
@@ -680,8 +682,12 @@ 
Source code for cleanlab.dataset
         num_examples = _get_num_examples(labels=labels)
     given_label_noise = joint.sum(axis=1) - joint.diagonal()  # p(s=k) - p(s=k,y=k) = p(y!=k, s=k)
     true_label_noise = joint.sum(axis=0) - joint.diagonal()  # p(y=k) - p(s=k,y=k) = p(s!=k,y=k)
-    given_conditional_noise = given_label_noise / joint.sum(axis=1)  # p(y!=k, s=k) / p(s=k)
-    true_conditional_noise = true_label_noise / joint.sum(axis=0)  # p(s!=k, y=k) / p(y=k)
+    given_conditional_noise = given_label_noise / np.clip(
+        joint.sum(axis=1), a_min=EPSILON, a_max=None
+    )  # p(y!=k, s=k) / p(s=k) , avoiding division by 0
+    true_conditional_noise = true_label_noise / np.clip(
+        joint.sum(axis=0), a_min=EPSILON, a_max=None
+    )  # p(s!=k, y=k) / p(y=k) , avoiding division by 0
     df = pd.DataFrame(
         {
             "Class Index": np.arange(len(joint)),
diff --git a/master/_modules/cleanlab/internal/outlier.html b/master/_modules/cleanlab/internal/outlier.html
index f5a4abb17..bdf359d74 100644
--- a/master/_modules/cleanlab/internal/outlier.html
+++ b/master/_modules/cleanlab/internal/outlier.html
@@ -599,9 +599,10 @@ 
Source code for cleanlab.internal.outlier
 """
 
 from typing import Optional
-
 import numpy as np
 
+from cleanlab.internal.constants import EPSILON
+
 
 
[docs]def transform_distances_to_scores(
     avg_distances: np.ndarray, t: int, scaling_factor: float
@@ -649,7 +650,7 @@ 
Source code for cleanlab.internal.outlier
     array([0.88988177, 0.80519832])
     """
     # Map ood_features_scores to range 0-1 with 0 = most concerning
-    return np.exp(-1 * avg_distances / scaling_factor * t)

+    return np.exp(-t * avg_distances / max(scaling_factor, EPSILON))

 
 
 
[docs]def correct_precision_errors(
diff --git a/master/_modules/cleanlab/object_detection/rank.html b/master/_modules/cleanlab/object_detection/rank.html
index 29ec56688..fc9e32ec4 100644
--- a/master/_modules/cleanlab/object_detection/rank.html
+++ b/master/_modules/cleanlab/object_detection/rank.html
@@ -597,13 +597,17 @@ 
Source code for cleanlab.object_detection.rank
"""Methods to rank and score images in an object detection dataset (object detection data), based on how likely they
 are to contain label errors. """
 
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, TypeVar
 import warnings
+import copy
+import numpy as np
 
 from cleanlab.internal.constants import (
     ALPHA,
     CUSTOM_SCORE_WEIGHT_BADLOC,
     CUSTOM_SCORE_WEIGHT_OVERLOOKED,
     CUSTOM_SCORE_WEIGHT_SWAP,
+    EPSILON,
     EUC_FACTOR,
     HIGH_PROBABILITY_THRESHOLD,
     LOW_PROBABILITY_THRESHOLD,
@@ -612,18 +616,13 @@ 
Source code for cleanlab.object_detection.rank
TEMPERATURE,
     LABEL_OVERLAP_THRESHOLD,
 )
-
-
-import copy
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, TypeVar
-
-import numpy as np
 from cleanlab.internal.object_detection_utils import (
     softmin1d,
     assert_valid_aggregation_weights,
     assert_valid_inputs,
 )
 
+
 if TYPE_CHECKING:  # pragma: no cover
     from typing import TypedDict
 
@@ -958,7 +957,9 @@ 
Source code for cleanlab.object_detection.rank
# compute the intersection over union by taking the intersection
     # area and dividing it by the sum of prediction + ground-truth
     # areas - the interesection area
-    iou = intersection_area / float(bb1_area + bb2_area - intersection_area)
+    iou = intersection_area / np.clip(
+        float(bb1_area + bb2_area - intersection_area), a_min=EPSILON, a_max=None
+    )  # avoid division by 0
     # There are some hyper-parameters here like consider tile area/object area
     return iou
 
diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index a5c09cbec..b3de2d406 100644
--- a/master/_sources/tutorials/clean_learning/tabular.ipynb
+++ b/master/_sources/tutorials/clean_learning/tabular.ipynb
@@ -120,7 +120,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/clean_learning/text.ipynb b/master/_sources/tutorials/clean_learning/text.ipynb
index 7d7d0593d..9a95724c2 100644
--- a/master/_sources/tutorials/clean_learning/text.ipynb
+++ b/master/_sources/tutorials/clean_learning/text.ipynb
@@ -129,7 +129,7 @@
     "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"  # disable parallelism to avoid deadlocks with huggingface\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/audio.ipynb b/master/_sources/tutorials/datalab/audio.ipynb
index 50c2925d8..1157ef346 100644
--- a/master/_sources/tutorials/datalab/audio.ipynb
+++ b/master/_sources/tutorials/datalab/audio.ipynb
@@ -91,7 +91,7 @@
     "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/data_monitor.ipynb b/master/_sources/tutorials/datalab/data_monitor.ipynb
index 325da7f8a..f9a14925d 100644
--- a/master/_sources/tutorials/datalab/data_monitor.ipynb
+++ b/master/_sources/tutorials/datalab/data_monitor.ipynb
@@ -83,7 +83,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]  # TODO: make sure this list is updated\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_advanced.ipynb b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
index ce001d8b5..b29f75c07 100644
--- a/master/_sources/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
@@ -87,7 +87,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]  # TODO: make sure this list is updated\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
index bb3ea4551..857d8a3e2 100644
--- a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
@@ -85,7 +85,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]  # TODO: make sure this list is updated\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb
index 7b3061799..5cfb34f64 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -80,7 +80,7 @@
     "dependencies = [\"cleanlab\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb
index bd1b28173..342228da8 100644
--- a/master/_sources/tutorials/datalab/text.ipynb
+++ b/master/_sources/tutorials/datalab/text.ipynb
@@ -90,7 +90,7 @@
     "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"  # disable parallelism to avoid deadlocks with huggingface\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb
index 7a02c6ec2..92fb1a9d0 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -79,7 +79,7 @@
     "dependencies = [\"cleanlab\", \"requests\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/indepth_overview.ipynb b/master/_sources/tutorials/indepth_overview.ipynb
index f1ed904af..e9d200666 100644
--- a/master/_sources/tutorials/indepth_overview.ipynb
+++ b/master/_sources/tutorials/indepth_overview.ipynb
@@ -62,7 +62,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb
index d760243f2..cb2d80066 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -95,7 +95,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/multilabel_classification.ipynb b/master/_sources/tutorials/multilabel_classification.ipynb
index 1985a563e..22c5f8449 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -73,7 +73,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb
index bf5463b83..f03299c15 100644
--- a/master/_sources/tutorials/object_detection.ipynb
+++ b/master/_sources/tutorials/object_detection.ipynb
@@ -77,7 +77,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/outliers.ipynb b/master/_sources/tutorials/outliers.ipynb
index 51eccf8ff..dc3d4117a 100644
--- a/master/_sources/tutorials/outliers.ipynb
+++ b/master/_sources/tutorials/outliers.ipynb
@@ -119,7 +119,7 @@
     "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb
index 520d19641..775ec429e 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -110,7 +110,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb
index 5d3ad5ba9..b604e8bc0 100644
--- a/master/_sources/tutorials/segmentation.ipynb
+++ b/master/_sources/tutorials/segmentation.ipynb
@@ -91,7 +91,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/token_classification.ipynb b/master/_sources/tutorials/token_classification.ipynb
index 378ffa94b..afe09a75f 100644
--- a/master/_sources/tutorials/token_classification.ipynb
+++ b/master/_sources/tutorials/token_classification.ipynb
@@ -95,7 +95,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/searchindex.js b/master/searchindex.js
index d225e7143..cb433166e 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
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Install required dependencies": [[82, "1.-Install-required-dependencies"], [83, "1.-Install-required-dependencies"], [90, "1.-Install-required-dependencies"], [91, "1.-Install-required-dependencies"], [101, "1.-Install-required-dependencies"]], "2. Load and process the data": [[82, "2.-Load-and-process-the-data"], [90, "2.-Load-and-process-the-data"], [101, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[82, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [90, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[82, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[82, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[83, "Text-Classification-with-Noisy-Labels"]], "2. 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Use cleanlab to find issues in your dataset": [[91, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[91, "Non-IID-issues-(data-drift)"]], "Understanding Dataset-level Labeling Issues": [[92, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[92, "Install-dependencies-and-import-them"], [94, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[92, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[92, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[93, "FAQ"]], "What data can cleanlab detect issues in?": [[93, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[93, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[93, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[93, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[93, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[93, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[93, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[93, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[93, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[93, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[93, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[93, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[93, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[93, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[94, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[94, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[94, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[94, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[94, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[94, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[94, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[94, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[94, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[94, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[94, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[94, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[94, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[94, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[94, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[94, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[94, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[94, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[94, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[94, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[94, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[94, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[95, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[96, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[96, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[96, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[96, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[96, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[96, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[96, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[96, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[96, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[97, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[97, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[97, "2.-Format-data,-labels,-and-model-predictions"], [98, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[97, "3.-Use-cleanlab-to-find-label-issues"], [98, "3.-Use-cleanlab-to-find-label-issues"], [102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[97, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[97, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[97, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[97, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[97, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[98, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[98, "1.-Install-required-dependencies-and-download-data"], [102, "1.-Install-required-dependencies-and-download-data"], [103, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[98, "Get-label-quality-scores"], [102, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[98, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[98, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[98, "Other-uses-of-visualize"]], "Exploratory data analysis": [[98, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[99, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[99, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[99, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[99, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[99, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[99, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[100, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[100, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[100, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[101, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[101, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[101, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[102, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[102, "2.-Get-data,-labels,-and-pred_probs"], [103, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[102, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[102, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[102, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[103, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[103, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[103, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[103, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[103, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"classification": [[2, "module-cleanlab.classification"]], "count": [[3, "module-cleanlab.count"]], "data_valuation": [[4, "module-cleanlab.data_valuation"], [18, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [11, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[7, "creating-your-own-issues-manager"]], "Prerequisites": [[7, "prerequisites"]], "Implementing IssueManagers": [[7, "implementing-issuemanagers"]], "Basic Issue Check": [[7, "basic-issue-check"]], "Intermediate Issue Check": [[7, "intermediate-issue-check"]], "Advanced Issue Check": [[7, "advanced-issue-check"]], "Use with Datalab": [[7, "use-with-datalab"]], "Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue 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"task"]], "dataset": [[36, "module-cleanlab.dataset"], [58, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[37, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[38, "module-cleanlab.experimental.coteaching"]], "experimental": [[39, "experimental"]], "label_issues_batched": [[40, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[41, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[42, "module-cleanlab.experimental.span_classification"]], "filter": [[43, "module-cleanlab.filter"], [59, "module-cleanlab.multilabel_classification.filter"], [62, "filter"], [71, "filter"], [75, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[45, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[46, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[47, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[48, 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"module-cleanlab.regression.learn"]], "regression.rank": [[70, "module-cleanlab.regression.rank"]], "segmentation": [[72, "segmentation"]], "token_classification": [[76, "token-classification"]], "cleanlab open-source documentation": [[79, "cleanlab-open-source-documentation"]], "Quickstart": [[79, "quickstart"]], "1. Install cleanlab": [[79, "install-cleanlab"]], "2. Find common issues in your data": [[79, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[79, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[79, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[79, "improve-your-data-via-many-other-techniques"]], "Contributing": [[79, "contributing"]], "Easy Mode": [[79, "easy-mode"], [88, "Easy-Mode"], [90, "Easy-Mode"], [91, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[80, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[80, "function-and-class-name-changes"]], "Module name changes": [[80, "module-name-changes"]], "New modules": [[80, "new-modules"]], "Removed modules": [[80, "removed-modules"]], "Common argument and variable name changes": [[80, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[81, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[82, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[82, "1.-Install-required-dependencies"], [83, "1.-Install-required-dependencies"], [90, "1.-Install-required-dependencies"], [91, "1.-Install-required-dependencies"], [101, "1.-Install-required-dependencies"]], "2. Load and process the data": [[82, "2.-Load-and-process-the-data"], [90, "2.-Load-and-process-the-data"], [101, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[82, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [90, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[82, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[82, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[83, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[83, "2.-Load-and-format-the-text-dataset"], [91, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[83, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[83, "4.-Train-a-more-robust-model-from-noisy-labels"], [101, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[84, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[84, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[84, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[84, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[84, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[84, "5.-Use-cleanlab-to-find-label-issues"], [90, "5.-Use-cleanlab-to-find-label-issues"]], "DataMonitor: Leverage statistics from Datalab to audit new data": [[85, "DataMonitor:-Leverage-statistics-from-Datalab-to-audit-new-data"]], "1. Install and import required dependencies": [[85, "1.-Install-and-import-required-dependencies"], [87, "1.-Install-and-import-required-dependencies"], [88, "1.-Install-and-import-required-dependencies"], [96, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[85, "2.-Create-and-load-the-data-(can-skip-these-details)"], [87, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[85, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"], [87, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[85, "4.-Use-Datalab-to-find-issues-in-the-dataset"], [87, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Use DataMonitor to find issues in new data": [[85, "5.-Use-DataMonitor-to-find-issues-in-new-data"]], "6. Learn more about the issues in the additional data": [[85, "6.-Learn-more-about-the-issues-in-the-additional-data"]], "7. Finding outliers in new data": [[85, "7.-Finding-outliers-in-new-data"]], "8. Looking for both label issues and outliers": [[85, "8.-Looking-for-both-label-issues-and-outliers"]], "Datalab: Advanced workflows to audit your data": [[86, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[86, "Install-and-import-required-dependencies"]], "Create and load the data": [[86, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[86, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[86, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[86, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[86, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[86, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[86, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[87, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "5. Learn more about the issues in your dataset": [[87, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[87, "Get-additional-information"]], "Near duplicate issues": [[87, "Near-duplicate-issues"], [88, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[88, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[88, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[88, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[88, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[88, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[88, "7.-Use-cleanlab-to-find-issues"]], "View report": [[88, "View-report"]], "Label issues": [[88, "Label-issues"], [90, "Label-issues"], [91, "Label-issues"]], "View most likely examples with label errors": [[88, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[88, "Outlier-issues"], [90, "Outlier-issues"], [91, "Outlier-issues"]], "View most severe outliers": [[88, "View-most-severe-outliers"]], "View sets of near duplicate images": [[88, "View-sets-of-near-duplicate-images"]], "Dark images": [[88, "Dark-images"]], "View top examples of dark images": [[88, "View-top-examples-of-dark-images"]], "Low information images": [[88, "Low-information-images"]], "Datalab Tutorials": [[89, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[90, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[90, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[90, "Near-duplicate-issues"], [91, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[91, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[91, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[91, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[91, "Non-IID-issues-(data-drift)"]], "Understanding Dataset-level Labeling Issues": [[92, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[92, "Install-dependencies-and-import-them"], [94, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[92, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[92, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[93, "FAQ"]], "What data can cleanlab detect issues in?": [[93, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[93, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[93, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[93, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[93, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[93, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[93, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[93, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[93, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[93, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[93, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[93, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[93, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[93, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[94, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[94, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[94, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[94, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[94, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[94, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[94, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[94, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[94, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[94, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[94, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[94, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[94, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[94, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[94, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[94, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[94, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[94, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[94, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[94, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[94, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[94, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[95, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[96, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[96, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[96, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[96, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[96, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[96, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[96, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[96, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[96, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[97, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[97, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[97, "2.-Format-data,-labels,-and-model-predictions"], [98, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[97, "3.-Use-cleanlab-to-find-label-issues"], [98, "3.-Use-cleanlab-to-find-label-issues"], [102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[97, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[97, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[97, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[97, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[97, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[98, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[98, "1.-Install-required-dependencies-and-download-data"], [102, "1.-Install-required-dependencies-and-download-data"], [103, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[98, "Get-label-quality-scores"], [102, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[98, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[98, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[98, "Other-uses-of-visualize"]], "Exploratory data analysis": [[98, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[99, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[99, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[99, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[99, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[99, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[99, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[100, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[100, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[100, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[101, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[101, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[101, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[102, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[102, "2.-Get-data,-labels,-and-pred_probs"], [103, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[102, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[102, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[102, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[103, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[103, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[103, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[103, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[103, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], 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"module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[50, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[50, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[51, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[52, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[53, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[55, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[56, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[56, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[56, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[57, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[58, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[59, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[59, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[59, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[60, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[61, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[61, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[61, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[62, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[62, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[63, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[64, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[65, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[66, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[66, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[66, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[66, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[66, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[67, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[67, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[67, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[68, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[69, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[69, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[69, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[70, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[70, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[71, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[71, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[72, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[73, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[73, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[73, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[74, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[74, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[74, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[74, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[75, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[75, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[76, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[77, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[77, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[77, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[78, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[78, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[78, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[78, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb
index f989c8b29..d74c47e6b 100644
--- a/master/tutorials/clean_learning/tabular.ipynb
+++ b/master/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-02T13:29:06.062330Z",
-     "iopub.status.busy": "2024-05-02T13:29:06.061839Z",
-     "iopub.status.idle": "2024-05-02T13:29:07.284109Z",
-     "shell.execute_reply": "2024-05-02T13:29:07.283537Z"
+     "iopub.execute_input": "2024-05-03T22:12:02.903369Z",
+     "iopub.status.busy": "2024-05-03T22:12:02.903188Z",
+     "iopub.status.idle": "2024-05-03T22:12:04.158635Z",
+     "shell.execute_reply": "2024-05-03T22:12:04.158072Z"
     },
     "nbsphinx": "hidden"
    },
@@ -126,7 +126,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -151,10 +151,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-02T13:29:07.286661Z",
-     "iopub.status.busy": "2024-05-02T13:29:07.286343Z",
-     "iopub.status.idle": "2024-05-02T13:29:07.306325Z",
-     "shell.execute_reply": "2024-05-02T13:29:07.305824Z"
+     "iopub.execute_input": "2024-05-03T22:12:04.161366Z",
+     "iopub.status.busy": "2024-05-03T22:12:04.161017Z",
+     "iopub.status.idle": "2024-05-03T22:12:04.183368Z",
+     "shell.execute_reply": "2024-05-03T22:12:04.182724Z"
     }
    },
    "outputs": [],
@@ -195,10 +195,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-02T13:29:07.308781Z",
-     "iopub.status.busy": "2024-05-02T13:29:07.308494Z",
-     "iopub.status.idle": "2024-05-02T13:29:07.489147Z",
-     "shell.execute_reply": "2024-05-02T13:29:07.488577Z"
+     "iopub.execute_input": "2024-05-03T22:12:04.186099Z",
+     "iopub.status.busy": "2024-05-03T22:12:04.185796Z",
+     "iopub.status.idle": "2024-05-03T22:12:04.421059Z",
+     "shell.execute_reply": "2024-05-03T22:12:04.420441Z"
     }
    },
    "outputs": [
@@ -305,10 +305,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-02T13:29:07.520255Z",
-     "iopub.status.busy": "2024-05-02T13:29:07.519854Z",
-     "iopub.status.idle": "2024-05-02T13:29:07.523599Z",
-     "shell.execute_reply": "2024-05-02T13:29:07.523134Z"
+     "iopub.execute_input": "2024-05-03T22:12:04.452917Z",
+     "iopub.status.busy": "2024-05-03T22:12:04.452369Z",
+     "iopub.status.idle": "2024-05-03T22:12:04.456478Z",
+     "shell.execute_reply": "2024-05-03T22:12:04.455879Z"
     }
    },
    "outputs": [],
@@ -329,10 +329,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-02T13:29:07.525838Z",
-     "iopub.status.busy": "2024-05-02T13:29:07.525467Z",
-     "iopub.status.idle": "2024-05-02T13:29:07.534237Z",
-     "shell.execute_reply": "2024-05-02T13:29:07.533771Z"
+     "iopub.execute_input": "2024-05-03T22:12:04.458916Z",
+     "iopub.status.busy": "2024-05-03T22:12:04.458523Z",
+     "iopub.status.idle": "2024-05-03T22:12:04.467652Z",
+     "shell.execute_reply": "2024-05-03T22:12:04.467062Z"
     }
    },
    "outputs": [],
@@ -384,10 +384,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2024-05-02T13:29:07.538456Z"
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@@ -409,10 +409,10 @@
    "execution_count": 7,
    "metadata": {
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-     "shell.execute_reply": "2024-05-02T13:29:08.066797Z"
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@@ -446,10 +446,10 @@
    "execution_count": 8,
    "metadata": {
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-     "shell.execute_reply": "2024-05-02T13:29:09.769241Z"
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@@ -481,10 +481,10 @@
    "execution_count": 9,
    "metadata": {
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-     "shell.execute_reply": "2024-05-02T13:29:09.781613Z"
+     "iopub.execute_input": "2024-05-03T22:12:06.777316Z",
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@@ -605,10 +605,10 @@
    "execution_count": 10,
    "metadata": {
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-     "shell.execute_reply": "2024-05-02T13:29:09.787788Z"
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@@ -633,10 +633,10 @@
    "execution_count": 11,
    "metadata": {
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-     "shell.execute_reply": "2024-05-02T13:29:09.796641Z"
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@@ -658,10 +658,10 @@
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-     "shell.execute_reply": "2024-05-02T13:29:09.911963Z"
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@@ -691,10 +691,10 @@
    "execution_count": 13,
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-     "shell.execute_reply": "2024-05-02T13:29:09.916870Z"
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@@ -715,10 +715,10 @@
    "execution_count": 14,
    "metadata": {
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-     "shell.execute_reply": "2024-05-02T13:29:11.947689Z"
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@@ -738,10 +738,10 @@
    "execution_count": 15,
    "metadata": {
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-     "iopub.status.idle": "2024-05-02T13:29:11.962551Z",
-     "shell.execute_reply": "2024-05-02T13:29:11.962050Z"
+     "iopub.execute_input": "2024-05-03T22:12:08.978181Z",
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@@ -771,10 +771,10 @@
    "execution_count": 16,
    "metadata": {
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-     "iopub.execute_input": "2024-05-02T13:29:11.964636Z",
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-     "shell.execute_reply": "2024-05-02T13:29:12.009308Z"
+     "iopub.execute_input": "2024-05-03T22:12:08.991513Z",
+     "iopub.status.busy": "2024-05-03T22:12:08.991091Z",
+     "iopub.status.idle": "2024-05-03T22:12:09.022498Z",
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diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index eac85e157..b41807348 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -783,7 +783,7 @@ 
2. Load and format the text dataset
 
 This dataset has 10 classes.
-Classes: {'apple_pay_or_google_pay', 'cancel_transfer', 'card_about_to_expire', 'getting_spare_card', 'visa_or_mastercard', 'change_pin', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'beneficiary_not_allowed'}
+Classes: {'card_about_to_expire', 'change_pin', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'getting_spare_card', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'card_payment_fee_charged', 'cancel_transfer'}
 

 

 
Let’s print the first example in the train set.

@@ -846,43 +846,43 @@ 
2. Load and format the text dataset
 

 

-

+

 

 

 

 

 

-

+

 

 

 

 

 

-

+

 

 

 

 

 

-

+
-
+
-
+
-
+
@@ -1181,7 +1181,7 @@

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"2024-05-02T13:29:14.958522Z", - "iopub.status.idle": "2024-05-02T13:29:18.230067Z", - "shell.execute_reply": "2024-05-02T13:29:18.229458Z" + "iopub.execute_input": "2024-05-03T22:12:12.994064Z", + "iopub.status.busy": "2024-05-03T22:12:12.993799Z", + "iopub.status.idle": "2024-05-03T22:12:16.105416Z", + "shell.execute_reply": "2024-05-03T22:12:16.104839Z" }, "nbsphinx": "hidden" }, @@ -135,7 +135,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -160,10 +160,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:18.232892Z", - "iopub.status.busy": "2024-05-02T13:29:18.232410Z", - "iopub.status.idle": "2024-05-02T13:29:18.235983Z", - "shell.execute_reply": "2024-05-02T13:29:18.235492Z" + "iopub.execute_input": "2024-05-03T22:12:16.108131Z", + "iopub.status.busy": "2024-05-03T22:12:16.107621Z", + "iopub.status.idle": "2024-05-03T22:12:16.111053Z", + "shell.execute_reply": "2024-05-03T22:12:16.110600Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:18.238174Z", - "iopub.status.busy": "2024-05-02T13:29:18.237734Z", - "iopub.status.idle": "2024-05-02T13:29:18.241017Z", - "shell.execute_reply": "2024-05-02T13:29:18.240460Z" + "iopub.execute_input": "2024-05-03T22:12:16.113105Z", + "iopub.status.busy": "2024-05-03T22:12:16.112757Z", + "iopub.status.idle": "2024-05-03T22:12:16.115858Z", + "shell.execute_reply": "2024-05-03T22:12:16.115408Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:18.243203Z", - "iopub.status.busy": "2024-05-02T13:29:18.242869Z", - "iopub.status.idle": "2024-05-02T13:29:18.300079Z", - "shell.execute_reply": "2024-05-02T13:29:18.299520Z" + "iopub.execute_input": "2024-05-03T22:12:16.117968Z", + "iopub.status.busy": "2024-05-03T22:12:16.117620Z", + "iopub.status.idle": "2024-05-03T22:12:16.149926Z", + "shell.execute_reply": "2024-05-03T22:12:16.149356Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:18.302419Z", - "iopub.status.busy": "2024-05-02T13:29:18.302063Z", - "iopub.status.idle": "2024-05-02T13:29:18.305669Z", - "shell.execute_reply": "2024-05-02T13:29:18.305198Z" + "iopub.execute_input": "2024-05-03T22:12:16.152165Z", + "iopub.status.busy": "2024-05-03T22:12:16.151785Z", + "iopub.status.idle": "2024-05-03T22:12:16.155533Z", + "shell.execute_reply": "2024-05-03T22:12:16.155075Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:18.307785Z", - "iopub.status.busy": "2024-05-02T13:29:18.307453Z", - "iopub.status.idle": "2024-05-02T13:29:18.311024Z", - "shell.execute_reply": "2024-05-02T13:29:18.310554Z" + "iopub.execute_input": "2024-05-03T22:12:16.157866Z", + "iopub.status.busy": "2024-05-03T22:12:16.157478Z", + "iopub.status.idle": "2024-05-03T22:12:16.160959Z", + "shell.execute_reply": "2024-05-03T22:12:16.160410Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'apple_pay_or_google_pay', 'cancel_transfer', 'card_about_to_expire', 'getting_spare_card', 'visa_or_mastercard', 'change_pin', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'beneficiary_not_allowed'}\n" + "Classes: {'card_about_to_expire', 'change_pin', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'getting_spare_card', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'card_payment_fee_charged', 'cancel_transfer'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:18.313057Z", - "iopub.status.busy": "2024-05-02T13:29:18.312749Z", - "iopub.status.idle": "2024-05-02T13:29:18.315876Z", - "shell.execute_reply": "2024-05-02T13:29:18.315352Z" + "iopub.execute_input": "2024-05-03T22:12:16.163177Z", + "iopub.status.busy": "2024-05-03T22:12:16.162815Z", + "iopub.status.idle": "2024-05-03T22:12:16.166010Z", + "shell.execute_reply": "2024-05-03T22:12:16.165484Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:18.318112Z", - "iopub.status.busy": "2024-05-02T13:29:18.317673Z", - "iopub.status.idle": "2024-05-02T13:29:18.321179Z", - 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["IPY_MODEL_d892be8d113c44a9a7bc1a69bf6c0704", "IPY_MODEL_34b0747fbdb04404bb0c7cdd38c9323e", "IPY_MODEL_3fd269b89e5d443fb5cd9f2e668dfe76"], "layout": "IPY_MODEL_b16e7cd5a4f7421fad1e2494fbc19678", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/audio.ipynb b/master/tutorials/datalab/audio.ipynb index 4b3011c4b..ceec1180a 100644 --- a/master/tutorials/datalab/audio.ipynb +++ b/master/tutorials/datalab/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:28.661262Z", - "iopub.status.busy": "2024-05-02T13:29:28.660899Z", - "iopub.status.idle": "2024-05-02T13:29:33.829066Z", - "shell.execute_reply": "2024-05-02T13:29:33.828505Z" + "iopub.execute_input": "2024-05-03T22:12:28.966803Z", + "iopub.status.busy": "2024-05-03T22:12:28.966626Z", + "iopub.status.idle": "2024-05-03T22:12:33.710567Z", + "shell.execute_reply": "2024-05-03T22:12:33.710000Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:33.831726Z", - "iopub.status.busy": "2024-05-02T13:29:33.831342Z", - "iopub.status.idle": "2024-05-02T13:29:33.834711Z", - "shell.execute_reply": "2024-05-02T13:29:33.834268Z" + "iopub.execute_input": "2024-05-03T22:12:33.713559Z", + "iopub.status.busy": "2024-05-03T22:12:33.712818Z", + "iopub.status.idle": "2024-05-03T22:12:33.716438Z", + "shell.execute_reply": "2024-05-03T22:12:33.715858Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:33.836717Z", - "iopub.status.busy": "2024-05-02T13:29:33.836397Z", - "iopub.status.idle": "2024-05-02T13:29:33.841206Z", - "shell.execute_reply": "2024-05-02T13:29:33.840665Z" + "iopub.execute_input": "2024-05-03T22:12:33.718588Z", + "iopub.status.busy": "2024-05-03T22:12:33.718252Z", + "iopub.status.idle": "2024-05-03T22:12:33.723001Z", + "shell.execute_reply": "2024-05-03T22:12:33.722439Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-05-02T13:29:33.843487Z", - "iopub.status.busy": "2024-05-02T13:29:33.843079Z", - "iopub.status.idle": "2024-05-02T13:29:35.476731Z", - "shell.execute_reply": "2024-05-02T13:29:35.475945Z" + "iopub.execute_input": "2024-05-03T22:12:33.725366Z", + "iopub.status.busy": "2024-05-03T22:12:33.725036Z", + "iopub.status.idle": "2024-05-03T22:12:35.207006Z", + "shell.execute_reply": "2024-05-03T22:12:35.206383Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-05-02T13:29:35.479519Z", - "iopub.status.busy": "2024-05-02T13:29:35.479159Z", - "iopub.status.idle": "2024-05-02T13:29:35.489722Z", - "shell.execute_reply": "2024-05-02T13:29:35.489190Z" + "iopub.execute_input": "2024-05-03T22:12:35.209709Z", + "iopub.status.busy": "2024-05-03T22:12:35.209321Z", + "iopub.status.idle": "2024-05-03T22:12:35.220147Z", + "shell.execute_reply": "2024-05-03T22:12:35.219664Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:35.492228Z", - "iopub.status.busy": "2024-05-02T13:29:35.491786Z", - "iopub.status.idle": "2024-05-02T13:29:35.497492Z", - "shell.execute_reply": "2024-05-02T13:29:35.496950Z" + "iopub.execute_input": "2024-05-03T22:12:35.222357Z", + "iopub.status.busy": "2024-05-03T22:12:35.222039Z", + "iopub.status.idle": "2024-05-03T22:12:35.227717Z", + "shell.execute_reply": "2024-05-03T22:12:35.227150Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-05-02T13:29:35.499568Z", - "iopub.status.busy": "2024-05-02T13:29:35.499261Z", - "iopub.status.idle": "2024-05-02T13:29:35.975715Z", - "shell.execute_reply": "2024-05-02T13:29:35.975122Z" + "iopub.execute_input": "2024-05-03T22:12:35.229818Z", + "iopub.status.busy": "2024-05-03T22:12:35.229492Z", + "iopub.status.idle": "2024-05-03T22:12:35.710657Z", + "shell.execute_reply": "2024-05-03T22:12:35.710131Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:35.978081Z", - "iopub.status.busy": "2024-05-02T13:29:35.977695Z", - "iopub.status.idle": "2024-05-02T13:29:37.103971Z", - "shell.execute_reply": "2024-05-02T13:29:37.103474Z" + "iopub.execute_input": "2024-05-03T22:12:35.712897Z", + "iopub.status.busy": "2024-05-03T22:12:35.712574Z", + "iopub.status.idle": "2024-05-03T22:12:37.213776Z", + "shell.execute_reply": "2024-05-03T22:12:37.213155Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-05-02T13:29:37.106454Z", - "iopub.status.busy": "2024-05-02T13:29:37.106160Z", - "iopub.status.idle": "2024-05-02T13:29:37.125425Z", - "shell.execute_reply": "2024-05-02T13:29:37.124942Z" + "iopub.execute_input": "2024-05-03T22:12:37.216214Z", + "iopub.status.busy": "2024-05-03T22:12:37.215986Z", + "iopub.status.idle": "2024-05-03T22:12:37.235204Z", + "shell.execute_reply": "2024-05-03T22:12:37.234619Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:37.127581Z", - "iopub.status.busy": "2024-05-02T13:29:37.127221Z", - "iopub.status.idle": "2024-05-02T13:29:37.130454Z", - "shell.execute_reply": "2024-05-02T13:29:37.129997Z" + "iopub.execute_input": "2024-05-03T22:12:37.237414Z", + "iopub.status.busy": "2024-05-03T22:12:37.237008Z", + "iopub.status.idle": "2024-05-03T22:12:37.240342Z", + "shell.execute_reply": "2024-05-03T22:12:37.239772Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:37.132398Z", - "iopub.status.busy": "2024-05-02T13:29:37.132025Z", - "iopub.status.idle": "2024-05-02T13:29:51.331545Z", - "shell.execute_reply": "2024-05-02T13:29:51.330927Z" + "iopub.execute_input": "2024-05-03T22:12:37.242542Z", + "iopub.status.busy": "2024-05-03T22:12:37.242138Z", + "iopub.status.idle": "2024-05-03T22:12:52.929337Z", + "shell.execute_reply": "2024-05-03T22:12:52.928708Z" }, "id": "2FSQ2GR9R_YA" }, @@ -627,10 +627,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-05-02T13:29:51.334609Z", - "iopub.status.busy": "2024-05-02T13:29:51.334115Z", - "iopub.status.idle": "2024-05-02T13:29:51.338019Z", - "shell.execute_reply": "2024-05-02T13:29:51.337442Z" + "iopub.execute_input": "2024-05-03T22:12:52.932499Z", + "iopub.status.busy": "2024-05-03T22:12:52.932067Z", + "iopub.status.idle": "2024-05-03T22:12:52.936366Z", + "shell.execute_reply": "2024-05-03T22:12:52.935789Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:51.340201Z", - "iopub.status.busy": "2024-05-02T13:29:51.339786Z", - "iopub.status.idle": "2024-05-02T13:29:52.054419Z", - "shell.execute_reply": 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1. Install and import required dependenciesdependencies = ["cleanlab", "matplotlib", "datasets"] # TODO: make sure this list is updated if "google.colab" in str(get_ipython()): # Check if it's running in Google Colab - %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b + %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219 cmd = ' '.join([dep for dep in dependencies if dep != "cleanlab"]) %pip install $cmd else: @@ -1154,7 +1154,7 @@

5. Use DataMonitor to find issues in new data

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5. Use DataMonitor to find issues in new data

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7. Finding outliers in new data

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7. Finding outliers in new data

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8. Looking for both label issues and outliers

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8. Looking for both label issues and outliers

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null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "815e6ada51f24c99be7da7ca074b329a": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_ac1d282d90f6429da4e617b67ce0dc48", "IPY_MODEL_eca8d55f69d0406683329cde5d664b1d", "IPY_MODEL_0c423708f12e4df598b3aa07ccfc9569"], "layout": "IPY_MODEL_90a4922d1e30420f8f90dc0dea9cd858", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/data_monitor.ipynb b/master/tutorials/datalab/data_monitor.ipynb index 2765c25bb..d81061bf7 100644 --- a/master/tutorials/datalab/data_monitor.ipynb +++ b/master/tutorials/datalab/data_monitor.ipynb @@ -5,10 +5,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:55.986486Z", - "iopub.status.busy": "2024-05-02T13:29:55.986079Z", - "iopub.status.idle": "2024-05-02T13:29:55.996835Z", - "shell.execute_reply": "2024-05-02T13:29:55.996413Z" + "iopub.execute_input": "2024-05-03T22:12:57.745893Z", + "iopub.status.busy": "2024-05-03T22:12:57.745543Z", + "iopub.status.idle": "2024-05-03T22:12:57.756222Z", + "shell.execute_reply": "2024-05-03T22:12:57.755688Z" } }, "outputs": [], @@ -85,10 +85,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:55.999014Z", - "iopub.status.busy": "2024-05-02T13:29:55.998704Z", - "iopub.status.idle": "2024-05-02T13:29:57.135034Z", - "shell.execute_reply": "2024-05-02T13:29:57.134480Z" + "iopub.execute_input": "2024-05-03T22:12:57.758767Z", + "iopub.status.busy": "2024-05-03T22:12:57.758427Z", + "iopub.status.idle": "2024-05-03T22:12:58.950128Z", + "shell.execute_reply": "2024-05-03T22:12:58.949568Z" } }, "outputs": [], @@ -97,7 +97,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -122,10 +122,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:57.137731Z", - "iopub.status.busy": "2024-05-02T13:29:57.137283Z", - "iopub.status.idle": "2024-05-02T13:29:57.155860Z", - "shell.execute_reply": "2024-05-02T13:29:57.155404Z" + "iopub.execute_input": "2024-05-03T22:12:58.952798Z", + "iopub.status.busy": "2024-05-03T22:12:58.952315Z", + "iopub.status.idle": "2024-05-03T22:12:58.970675Z", + "shell.execute_reply": "2024-05-03T22:12:58.970194Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:57.157948Z", - "iopub.status.busy": "2024-05-02T13:29:57.157639Z", - "iopub.status.idle": "2024-05-02T13:29:57.177589Z", - "shell.execute_reply": "2024-05-02T13:29:57.177050Z" + "iopub.execute_input": "2024-05-03T22:12:58.973068Z", + "iopub.status.busy": "2024-05-03T22:12:58.972699Z", + "iopub.status.idle": "2024-05-03T22:12:58.994342Z", + "shell.execute_reply": "2024-05-03T22:12:58.993714Z" } }, "outputs": [], @@ -353,10 +353,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:57.179763Z", - "iopub.status.busy": "2024-05-02T13:29:57.179475Z", - "iopub.status.idle": "2024-05-02T13:29:57.194323Z", - "shell.execute_reply": "2024-05-02T13:29:57.193876Z" + "iopub.execute_input": "2024-05-03T22:12:58.997047Z", + "iopub.status.busy": "2024-05-03T22:12:58.996512Z", + "iopub.status.idle": "2024-05-03T22:12:59.013775Z", + "shell.execute_reply": "2024-05-03T22:12:59.013309Z" } }, "outputs": [], @@ -369,10 +369,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:57.196312Z", - "iopub.status.busy": "2024-05-02T13:29:57.196005Z", - "iopub.status.idle": "2024-05-02T13:29:57.209139Z", - "shell.execute_reply": "2024-05-02T13:29:57.208581Z" + "iopub.execute_input": "2024-05-03T22:12:59.016239Z", + "iopub.status.busy": "2024-05-03T22:12:59.015763Z", + "iopub.status.idle": "2024-05-03T22:12:59.031928Z", + "shell.execute_reply": "2024-05-03T22:12:59.031349Z" } }, "outputs": [], @@ -450,10 +450,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:57.211235Z", - "iopub.status.busy": "2024-05-02T13:29:57.210932Z", - "iopub.status.idle": "2024-05-02T13:29:57.402349Z", - "shell.execute_reply": "2024-05-02T13:29:57.401779Z" + "iopub.execute_input": "2024-05-03T22:12:59.034386Z", + "iopub.status.busy": "2024-05-03T22:12:59.033993Z", + "iopub.status.idle": "2024-05-03T22:12:59.234172Z", + "shell.execute_reply": "2024-05-03T22:12:59.233687Z" } }, "outputs": [], @@ -507,10 +507,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:57.404722Z", - "iopub.status.busy": "2024-05-02T13:29:57.404343Z", - "iopub.status.idle": "2024-05-02T13:29:57.762216Z", - "shell.execute_reply": "2024-05-02T13:29:57.761640Z" + "iopub.execute_input": "2024-05-03T22:12:59.236560Z", + "iopub.status.busy": "2024-05-03T22:12:59.236228Z", + "iopub.status.idle": "2024-05-03T22:12:59.545463Z", + "shell.execute_reply": "2024-05-03T22:12:59.544896Z" } }, "outputs": [ @@ -553,10 +553,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:57.764437Z", - "iopub.status.busy": "2024-05-02T13:29:57.764103Z", - "iopub.status.idle": "2024-05-02T13:29:57.800553Z", - "shell.execute_reply": "2024-05-02T13:29:57.800135Z" + "iopub.execute_input": "2024-05-03T22:12:59.547580Z", + "iopub.status.busy": "2024-05-03T22:12:59.547396Z", + "iopub.status.idle": "2024-05-03T22:12:59.585458Z", + "shell.execute_reply": "2024-05-03T22:12:59.584987Z" } }, "outputs": [], @@ -581,10 +581,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:57.802542Z", - "iopub.status.busy": "2024-05-02T13:29:57.802239Z", - "iopub.status.idle": "2024-05-02T13:29:59.411961Z", - "shell.execute_reply": "2024-05-02T13:29:59.411375Z" + "iopub.execute_input": "2024-05-03T22:12:59.587931Z", + "iopub.status.busy": "2024-05-03T22:12:59.587601Z", + "iopub.status.idle": "2024-05-03T22:13:01.338488Z", + "shell.execute_reply": "2024-05-03T22:13:01.337923Z" } }, "outputs": [ @@ -667,10 +667,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:59.414528Z", - "iopub.status.busy": "2024-05-02T13:29:59.414061Z", - "iopub.status.idle": "2024-05-02T13:29:59.441498Z", - "shell.execute_reply": "2024-05-02T13:29:59.441030Z" + "iopub.execute_input": "2024-05-03T22:13:01.341018Z", + "iopub.status.busy": "2024-05-03T22:13:01.340483Z", + "iopub.status.idle": "2024-05-03T22:13:01.369849Z", + "shell.execute_reply": "2024-05-03T22:13:01.369381Z" } }, "outputs": [], @@ -701,10 +701,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:59.443703Z", - "iopub.status.busy": "2024-05-02T13:29:59.443314Z", - "iopub.status.idle": "2024-05-02T13:29:59.474203Z", - "shell.execute_reply": "2024-05-02T13:29:59.473699Z" + "iopub.execute_input": "2024-05-03T22:13:01.372131Z", + "iopub.status.busy": "2024-05-03T22:13:01.371910Z", + "iopub.status.idle": "2024-05-03T22:13:01.412479Z", + "shell.execute_reply": "2024-05-03T22:13:01.411992Z" } }, "outputs": [], @@ -741,17 +741,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:29:59.476263Z", - "iopub.status.busy": "2024-05-02T13:29:59.475934Z", - "iopub.status.idle": "2024-05-02T13:30:04.569737Z", - "shell.execute_reply": "2024-05-02T13:30:04.569132Z" + "iopub.execute_input": "2024-05-03T22:13:01.414921Z", + "iopub.status.busy": "2024-05-03T22:13:01.414555Z", + "iopub.status.idle": "2024-05-03T22:13:06.524626Z", + "shell.execute_reply": "2024-05-03T22:13:06.524041Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fb22d0c4b8294355ba4c419934662f2c", + "model_id": "99dcec0d20b64392b3419a4d8ac05477", "version_major": 2, "version_minor": 0 }, @@ -811,17 +811,17 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:04.572100Z", - "iopub.status.busy": "2024-05-02T13:30:04.571771Z", - "iopub.status.idle": "2024-05-02T13:30:09.891827Z", - "shell.execute_reply": "2024-05-02T13:30:09.891225Z" + "iopub.execute_input": "2024-05-03T22:13:06.526891Z", + "iopub.status.busy": "2024-05-03T22:13:06.526707Z", + "iopub.status.idle": "2024-05-03T22:13:11.849098Z", + "shell.execute_reply": "2024-05-03T22:13:11.848488Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "73e5430004ad4dbc8fdb91dfc1fd9163", + "model_id": "81ace9b9dff64dc69e61339fa55e12be", "version_major": 2, "version_minor": 0 }, @@ -949,10 +949,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:09.894070Z", - "iopub.status.busy": "2024-05-02T13:30:09.893753Z", - "iopub.status.idle": "2024-05-02T13:30:09.928376Z", - "shell.execute_reply": "2024-05-02T13:30:09.927936Z" + "iopub.execute_input": "2024-05-03T22:13:11.851187Z", + "iopub.status.busy": "2024-05-03T22:13:11.851009Z", + "iopub.status.idle": "2024-05-03T22:13:11.886546Z", + "shell.execute_reply": "2024-05-03T22:13:11.885970Z" } }, "outputs": [ @@ -1185,10 +1185,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:09.930364Z", - "iopub.status.busy": "2024-05-02T13:30:09.930037Z", - "iopub.status.idle": "2024-05-02T13:30:09.956439Z", - "shell.execute_reply": "2024-05-02T13:30:09.955901Z" + "iopub.execute_input": "2024-05-03T22:13:11.888808Z", + "iopub.status.busy": "2024-05-03T22:13:11.888442Z", + "iopub.status.idle": "2024-05-03T22:13:11.918070Z", + "shell.execute_reply": "2024-05-03T22:13:11.917562Z" } }, "outputs": [ @@ -1258,10 +1258,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:09.958638Z", - "iopub.status.busy": "2024-05-02T13:30:09.958242Z", - "iopub.status.idle": "2024-05-02T13:30:09.996679Z", - "shell.execute_reply": "2024-05-02T13:30:09.996254Z" + "iopub.execute_input": "2024-05-03T22:13:11.920293Z", + "iopub.status.busy": "2024-05-03T22:13:11.919934Z", + "iopub.status.idle": "2024-05-03T22:13:11.964630Z", + "shell.execute_reply": "2024-05-03T22:13:11.964014Z" } }, "outputs": [ @@ -1314,10 +1314,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:09.998633Z", - "iopub.status.busy": "2024-05-02T13:30:09.998328Z", - "iopub.status.idle": "2024-05-02T13:30:10.020426Z", - "shell.execute_reply": "2024-05-02T13:30:10.019875Z" + "iopub.execute_input": "2024-05-03T22:13:11.966782Z", + "iopub.status.busy": "2024-05-03T22:13:11.966587Z", + "iopub.status.idle": "2024-05-03T22:13:11.997461Z", + "shell.execute_reply": "2024-05-03T22:13:11.996934Z" } }, "outputs": [], @@ -1331,10 +1331,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:10.022627Z", - "iopub.status.busy": "2024-05-02T13:30:10.022313Z", - "iopub.status.idle": "2024-05-02T13:30:10.045264Z", - "shell.execute_reply": "2024-05-02T13:30:10.044711Z" + "iopub.execute_input": "2024-05-03T22:13:12.000152Z", + "iopub.status.busy": "2024-05-03T22:13:11.999580Z", + "iopub.status.idle": "2024-05-03T22:13:12.027158Z", + "shell.execute_reply": "2024-05-03T22:13:12.026696Z" } }, "outputs": [], @@ -1363,17 +1363,17 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:10.047238Z", - "iopub.status.busy": "2024-05-02T13:30:10.046927Z", - "iopub.status.idle": "2024-05-02T13:30:20.456799Z", - "shell.execute_reply": "2024-05-02T13:30:20.456186Z" + "iopub.execute_input": "2024-05-03T22:13:12.029289Z", + "iopub.status.busy": "2024-05-03T22:13:12.029103Z", + "iopub.status.idle": "2024-05-03T22:13:22.462772Z", + "shell.execute_reply": "2024-05-03T22:13:22.462192Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "78fce4adaae247cdbfcbc05371109a2a", + "model_id": "cf6083ee4aa0422f83db4a7657537a8a", "version_major": 2, "version_minor": 0 }, @@ -1397,7 +1397,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b51af869891c4eeea7a188b683aaaa69", + "model_id": "0d6e03b6808f435c910913801daa0ad7", "version_major": 2, "version_minor": 0 }, @@ -1463,10 +1463,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:20.459186Z", - "iopub.status.busy": "2024-05-02T13:30:20.458846Z", - "iopub.status.idle": "2024-05-02T13:30:20.541228Z", - "shell.execute_reply": "2024-05-02T13:30:20.540650Z" + "iopub.execute_input": "2024-05-03T22:13:22.464883Z", + "iopub.status.busy": "2024-05-03T22:13:22.464697Z", + "iopub.status.idle": "2024-05-03T22:13:22.554428Z", + "shell.execute_reply": "2024-05-03T22:13:22.553781Z" } }, "outputs": [ @@ -1546,10 +1546,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:20.543571Z", - "iopub.status.busy": "2024-05-02T13:30:20.543283Z", - "iopub.status.idle": 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"version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb index b7fc47d4d..b5a83caf8 100644 --- a/master/tutorials/datalab/datalab_advanced.ipynb +++ b/master/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:33.659327Z", - "iopub.status.busy": "2024-05-02T13:30:33.659160Z", - "iopub.status.idle": "2024-05-02T13:30:34.814742Z", - "shell.execute_reply": "2024-05-02T13:30:34.814188Z" + "iopub.execute_input": "2024-05-03T22:13:35.683756Z", + "iopub.status.busy": "2024-05-03T22:13:35.683268Z", + "iopub.status.idle": "2024-05-03T22:13:36.884885Z", + "shell.execute_reply": "2024-05-03T22:13:36.884275Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -118,10 +118,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:34.817322Z", - "iopub.status.busy": "2024-05-02T13:30:34.816830Z", - "iopub.status.idle": "2024-05-02T13:30:34.820055Z", - "shell.execute_reply": "2024-05-02T13:30:34.819567Z" + "iopub.execute_input": "2024-05-03T22:13:36.887519Z", + "iopub.status.busy": "2024-05-03T22:13:36.887046Z", + "iopub.status.idle": "2024-05-03T22:13:36.890172Z", + "shell.execute_reply": "2024-05-03T22:13:36.889722Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:34.822292Z", - "iopub.status.busy": "2024-05-02T13:30:34.821923Z", - "iopub.status.idle": "2024-05-02T13:30:34.830961Z", - "shell.execute_reply": "2024-05-02T13:30:34.830393Z" + "iopub.execute_input": "2024-05-03T22:13:36.892309Z", + "iopub.status.busy": "2024-05-03T22:13:36.891981Z", + "iopub.status.idle": "2024-05-03T22:13:36.901183Z", + "shell.execute_reply": "2024-05-03T22:13:36.900720Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:34.832995Z", - "iopub.status.busy": "2024-05-02T13:30:34.832662Z", - "iopub.status.idle": "2024-05-02T13:30:34.837643Z", - "shell.execute_reply": "2024-05-02T13:30:34.837097Z" + "iopub.execute_input": "2024-05-03T22:13:36.903368Z", + "iopub.status.busy": "2024-05-03T22:13:36.902935Z", + "iopub.status.idle": "2024-05-03T22:13:36.907913Z", + "shell.execute_reply": "2024-05-03T22:13:36.907474Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:34.839764Z", - "iopub.status.busy": "2024-05-02T13:30:34.839584Z", - "iopub.status.idle": "2024-05-02T13:30:35.024974Z", - "shell.execute_reply": "2024-05-02T13:30:35.024373Z" + "iopub.execute_input": "2024-05-03T22:13:36.909999Z", + "iopub.status.busy": "2024-05-03T22:13:36.909706Z", + "iopub.status.idle": "2024-05-03T22:13:37.105159Z", + "shell.execute_reply": "2024-05-03T22:13:37.104641Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:35.027498Z", - "iopub.status.busy": "2024-05-02T13:30:35.027314Z", - "iopub.status.idle": "2024-05-02T13:30:35.399865Z", - "shell.execute_reply": "2024-05-02T13:30:35.399289Z" + "iopub.execute_input": "2024-05-03T22:13:37.107446Z", + "iopub.status.busy": "2024-05-03T22:13:37.107225Z", + "iopub.status.idle": "2024-05-03T22:13:37.484941Z", + "shell.execute_reply": 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from issue manager OutlierIssueManager.\n", + "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:348: UserWarning: Overwriting columns ['outlier_score', 'is_outlier_issue'] in self.issues with columns from issue manager OutlierIssueManager.\n", " warnings.warn(\n", "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:378: UserWarning: Overwriting row in self.issue_summary with row from issue manager OutlierIssueManager.\n", " warnings.warn(\n", @@ -936,10 +936,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:37.217075Z", - "iopub.status.busy": "2024-05-02T13:30:37.216892Z", - "iopub.status.idle": "2024-05-02T13:30:37.232220Z", - "shell.execute_reply": "2024-05-02T13:30:37.231729Z" + "iopub.execute_input": "2024-05-03T22:13:39.261201Z", + "iopub.status.busy": "2024-05-03T22:13:39.261010Z", + "iopub.status.idle": "2024-05-03T22:13:39.276618Z", + "shell.execute_reply": 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"_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } } }, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index 558bc7cb5..9f7d1a639 100644 --- a/master/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:40.396346Z", - "iopub.status.busy": "2024-05-02T13:30:40.396169Z", - "iopub.status.idle": "2024-05-02T13:30:41.552811Z", - "shell.execute_reply": "2024-05-02T13:30:41.552214Z" + "iopub.execute_input": "2024-05-03T22:13:42.159190Z", + "iopub.status.busy": "2024-05-03T22:13:42.159030Z", + "iopub.status.idle": "2024-05-03T22:13:43.372881Z", + "shell.execute_reply": "2024-05-03T22:13:43.372247Z" }, "nbsphinx": "hidden" }, @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -116,10 +116,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:41.555688Z", - "iopub.status.busy": "2024-05-02T13:30:41.555253Z", - "iopub.status.idle": "2024-05-02T13:30:41.558191Z", - "shell.execute_reply": "2024-05-02T13:30:41.557735Z" + "iopub.execute_input": "2024-05-03T22:13:43.375571Z", + "iopub.status.busy": "2024-05-03T22:13:43.375153Z", + "iopub.status.idle": "2024-05-03T22:13:43.378364Z", + "shell.execute_reply": "2024-05-03T22:13:43.377899Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:41.560267Z", - "iopub.status.busy": "2024-05-02T13:30:41.560011Z", - "iopub.status.idle": "2024-05-02T13:30:41.569438Z", - "shell.execute_reply": "2024-05-02T13:30:41.568924Z" + "iopub.execute_input": "2024-05-03T22:13:43.380593Z", + "iopub.status.busy": "2024-05-03T22:13:43.380248Z", + "iopub.status.idle": "2024-05-03T22:13:43.390435Z", + "shell.execute_reply": "2024-05-03T22:13:43.389937Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:41.571432Z", - "iopub.status.busy": "2024-05-02T13:30:41.571138Z", - "iopub.status.idle": "2024-05-02T13:30:41.575850Z", - "shell.execute_reply": "2024-05-02T13:30:41.575447Z" + "iopub.execute_input": "2024-05-03T22:13:43.392535Z", + "iopub.status.busy": "2024-05-03T22:13:43.392332Z", + "iopub.status.idle": "2024-05-03T22:13:43.397278Z", + "shell.execute_reply": "2024-05-03T22:13:43.396693Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:41.578008Z", - "iopub.status.busy": "2024-05-02T13:30:41.577600Z", - "iopub.status.idle": "2024-05-02T13:30:41.759726Z", - "shell.execute_reply": "2024-05-02T13:30:41.759110Z" + "iopub.execute_input": "2024-05-03T22:13:43.399728Z", + "iopub.status.busy": "2024-05-03T22:13:43.399406Z", + "iopub.status.idle": "2024-05-03T22:13:43.591087Z", + "shell.execute_reply": "2024-05-03T22:13:43.590496Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:41.762092Z", - "iopub.status.busy": "2024-05-02T13:30:41.761877Z", - "iopub.status.idle": "2024-05-02T13:30:42.134415Z", - "shell.execute_reply": "2024-05-02T13:30:42.133783Z" + "iopub.execute_input": "2024-05-03T22:13:43.593686Z", + "iopub.status.busy": "2024-05-03T22:13:43.593359Z", + "iopub.status.idle": "2024-05-03T22:13:43.974277Z", + "shell.execute_reply": "2024-05-03T22:13:43.973669Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:42.136801Z", - "iopub.status.busy": "2024-05-02T13:30:42.136385Z", - "iopub.status.idle": "2024-05-02T13:30:42.139247Z", - "shell.execute_reply": "2024-05-02T13:30:42.138786Z" + "iopub.execute_input": "2024-05-03T22:13:43.976649Z", + "iopub.status.busy": "2024-05-03T22:13:43.976176Z", + "iopub.status.idle": "2024-05-03T22:13:43.979025Z", + "shell.execute_reply": "2024-05-03T22:13:43.978582Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:42.141211Z", - "iopub.status.busy": "2024-05-02T13:30:42.140908Z", - "iopub.status.idle": "2024-05-02T13:30:42.177094Z", - "shell.execute_reply": "2024-05-02T13:30:42.176488Z" + "iopub.execute_input": "2024-05-03T22:13:43.981223Z", + "iopub.status.busy": "2024-05-03T22:13:43.980831Z", + "iopub.status.idle": "2024-05-03T22:13:44.018110Z", + "shell.execute_reply": "2024-05-03T22:13:44.017433Z" } }, "outputs": [ @@ -647,10 +647,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:42.179348Z", - "iopub.status.busy": "2024-05-02T13:30:42.178966Z", - "iopub.status.idle": "2024-05-02T13:30:43.845842Z", - "shell.execute_reply": "2024-05-02T13:30:43.845214Z" + "iopub.execute_input": "2024-05-03T22:13:44.020362Z", + "iopub.status.busy": "2024-05-03T22:13:44.020024Z", + "iopub.status.idle": "2024-05-03T22:13:45.797484Z", + "shell.execute_reply": "2024-05-03T22:13:45.796749Z" } }, "outputs": [ @@ -711,10 +711,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:43.848207Z", - "iopub.status.busy": "2024-05-02T13:30:43.847811Z", - "iopub.status.idle": "2024-05-02T13:30:43.866539Z", - "shell.execute_reply": "2024-05-02T13:30:43.865996Z" + "iopub.execute_input": "2024-05-03T22:13:45.799975Z", + "iopub.status.busy": "2024-05-03T22:13:45.799550Z", + "iopub.status.idle": "2024-05-03T22:13:45.819430Z", + "shell.execute_reply": "2024-05-03T22:13:45.818870Z" } }, "outputs": [ @@ -842,10 +842,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:43.868706Z", - "iopub.status.busy": "2024-05-02T13:30:43.868401Z", - "iopub.status.idle": "2024-05-02T13:30:43.874878Z", - "shell.execute_reply": "2024-05-02T13:30:43.874430Z" + "iopub.execute_input": "2024-05-03T22:13:45.821745Z", + "iopub.status.busy": "2024-05-03T22:13:45.821391Z", + "iopub.status.idle": "2024-05-03T22:13:45.829079Z", + "shell.execute_reply": "2024-05-03T22:13:45.828588Z" } }, "outputs": [ @@ -956,10 +956,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:43.876944Z", - "iopub.status.busy": "2024-05-02T13:30:43.876606Z", - "iopub.status.idle": "2024-05-02T13:30:43.882380Z", - "shell.execute_reply": "2024-05-02T13:30:43.881827Z" + "iopub.execute_input": "2024-05-03T22:13:45.831249Z", + "iopub.status.busy": "2024-05-03T22:13:45.830883Z", + "iopub.status.idle": "2024-05-03T22:13:45.837158Z", + "shell.execute_reply": "2024-05-03T22:13:45.836691Z" } }, "outputs": [ @@ -1026,10 +1026,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:43.884669Z", - "iopub.status.busy": "2024-05-02T13:30:43.884236Z", - "iopub.status.idle": "2024-05-02T13:30:43.894796Z", - "shell.execute_reply": "2024-05-02T13:30:43.894319Z" + "iopub.execute_input": "2024-05-03T22:13:45.839290Z", + "iopub.status.busy": "2024-05-03T22:13:45.838952Z", + "iopub.status.idle": "2024-05-03T22:13:45.849793Z", + "shell.execute_reply": "2024-05-03T22:13:45.849319Z" } }, "outputs": [ @@ -1221,10 +1221,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:43.896940Z", - "iopub.status.busy": "2024-05-02T13:30:43.896520Z", - "iopub.status.idle": "2024-05-02T13:30:43.905889Z", - "shell.execute_reply": "2024-05-02T13:30:43.905341Z" + "iopub.execute_input": "2024-05-03T22:13:45.851948Z", + "iopub.status.busy": "2024-05-03T22:13:45.851621Z", + "iopub.status.idle": "2024-05-03T22:13:45.860761Z", + "shell.execute_reply": "2024-05-03T22:13:45.860249Z" } }, "outputs": [ @@ -1340,10 +1340,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:43.908062Z", - "iopub.status.busy": "2024-05-02T13:30:43.907682Z", - "iopub.status.idle": "2024-05-02T13:30:43.914529Z", - "shell.execute_reply": "2024-05-02T13:30:43.913996Z" + "iopub.execute_input": "2024-05-03T22:13:45.862844Z", + "iopub.status.busy": "2024-05-03T22:13:45.862509Z", + "iopub.status.idle": "2024-05-03T22:13:45.869557Z", + "shell.execute_reply": "2024-05-03T22:13:45.868991Z" }, "scrolled": true }, @@ -1468,10 +1468,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:43.916476Z", - "iopub.status.busy": "2024-05-02T13:30:43.916205Z", - "iopub.status.idle": "2024-05-02T13:30:43.925505Z", - "shell.execute_reply": "2024-05-02T13:30:43.925064Z" + "iopub.execute_input": "2024-05-03T22:13:45.871677Z", + "iopub.status.busy": "2024-05-03T22:13:45.871352Z", + "iopub.status.idle": "2024-05-03T22:13:45.880934Z", + "shell.execute_reply": "2024-05-03T22:13:45.880344Z" } }, "outputs": [ diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index 88d70f0d4..40bf813c7 100644 --- a/master/tutorials/datalab/image.html +++ b/master/tutorials/datalab/image.html @@ -693,25 +693,25 @@

2. Fetch and normalize the Fashion-MNIST dataset

-
+
-
+
-
+
-
+
[3]:
@@ -766,7 +766,7 @@ 
2. Fetch and normalize the Fashion-MNIST dataset
-
+

Convert the transformed dataset to a torch dataset. Torch datasets are more efficient with dataloading in practice.

@@ -1015,8 +1015,8 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
 
 Training on fold: 1 ...
-epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.424
-epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 5.063
+epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.051
+epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.866
 Computing feature embeddings ...

@@ -1024,7 +1024,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1038,7 +1038,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings

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@@ -1047,8 +1047,8 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
 
 Training on fold: 2 ...
-epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.183
-epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 5.010
+epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.210
+epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.912
 Computing feature embeddings ...

@@ -1056,7 +1056,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
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+
@@ -1070,7 +1070,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings

-
+
@@ -1079,8 +1079,8 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
 
 Training on fold: 3 ...
-epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.198
-epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.970
+epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.290
+epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.904
 Computing feature embeddings ...

@@ -1088,7 +1088,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
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+
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5. Compute out-of-sample predicted probabilities and feature embeddings

-
+
@@ -1174,7 +1174,7 @@

7. Use cleanlab to find issues

-
+
@@ -2062,7 +2062,7 @@

Easy ModeCleanlab Studio which will automatically produce one for you. Super easy to use, Cleanlab Studio is no-code platform for data-centric AI that automatically: detects data issues (more types of issues than this cleanlab package), helps you quickly correct these data issues, confidently labels large subsets of an unlabeled dataset, and provides other smart metadata about each of your data points – all powered by a system that automatically trains/deploys the best ML model for your data. Try it for free!

diff --git a/master/tutorials/datalab/image.ipynb b/master/tutorials/datalab/image.ipynb index 0717c3bf8..e7f705656 100644 --- a/master/tutorials/datalab/image.ipynb +++ b/master/tutorials/datalab/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:46.614969Z", - "iopub.status.busy": "2024-05-02T13:30:46.614501Z", - "iopub.status.idle": "2024-05-02T13:30:49.589754Z", - "shell.execute_reply": "2024-05-02T13:30:49.589185Z" + "iopub.execute_input": "2024-05-03T22:13:48.807091Z", + "iopub.status.busy": "2024-05-03T22:13:48.806909Z", + "iopub.status.idle": "2024-05-03T22:13:51.824622Z", + "shell.execute_reply": "2024-05-03T22:13:51.824036Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:49.592547Z", - "iopub.status.busy": "2024-05-02T13:30:49.592039Z", - "iopub.status.idle": "2024-05-02T13:30:49.595654Z", - "shell.execute_reply": "2024-05-02T13:30:49.595210Z" + "iopub.execute_input": "2024-05-03T22:13:51.827572Z", + "iopub.status.busy": "2024-05-03T22:13:51.826951Z", + "iopub.status.idle": "2024-05-03T22:13:51.830830Z", + "shell.execute_reply": "2024-05-03T22:13:51.830275Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:49.597602Z", - "iopub.status.busy": "2024-05-02T13:30:49.597414Z", - "iopub.status.idle": "2024-05-02T13:30:51.576760Z", - "shell.execute_reply": "2024-05-02T13:30:51.576255Z" + "iopub.execute_input": "2024-05-03T22:13:51.833240Z", + "iopub.status.busy": "2024-05-03T22:13:51.832814Z", + "iopub.status.idle": "2024-05-03T22:13:53.852142Z", + "shell.execute_reply": "2024-05-03T22:13:53.851572Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dfc89fc7fc4d4d86b2ba5eeb2c973868", + "model_id": "d31981130221497cbb1d76b50904779e", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6d901677af154369baf845188fc2abec", + "model_id": "12a11d4b9bc94ec29a4c4635d6c9e01a", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bde28428ead04314bc1eafb4f6911475", + "model_id": "a8e090733a5443938ab0dddf7303203b", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d1b676b40e8843c4a55fd5d6aab589a0", + "model_id": "8624def2f3914edebe3b0df0a9197833", "version_major": 2, "version_minor": 0 }, @@ -246,10 +246,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:51.579186Z", - "iopub.status.busy": "2024-05-02T13:30:51.578693Z", - "iopub.status.idle": "2024-05-02T13:30:51.582755Z", - "shell.execute_reply": "2024-05-02T13:30:51.582177Z" + "iopub.execute_input": "2024-05-03T22:13:53.854543Z", + "iopub.status.busy": "2024-05-03T22:13:53.854166Z", + "iopub.status.idle": "2024-05-03T22:13:53.858146Z", + "shell.execute_reply": "2024-05-03T22:13:53.857640Z" } }, "outputs": [ @@ -274,17 +274,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:30:51.584846Z", - "iopub.status.busy": "2024-05-02T13:30:51.584523Z", - "iopub.status.idle": "2024-05-02T13:31:03.216128Z", - "shell.execute_reply": "2024-05-02T13:31:03.215570Z" + "iopub.execute_input": "2024-05-03T22:13:53.860306Z", + "iopub.status.busy": "2024-05-03T22:13:53.859964Z", + "iopub.status.idle": "2024-05-03T22:14:05.434733Z", + "shell.execute_reply": "2024-05-03T22:14:05.434201Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "90697b7a34fa4cf3a5f142da0006eeb4", + "model_id": "e6de2c216183488bbf9f27cd5d5a8e98", "version_major": 2, "version_minor": 0 }, @@ -322,10 +322,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:31:03.218705Z", - "iopub.status.busy": "2024-05-02T13:31:03.218461Z", - "iopub.status.idle": "2024-05-02T13:31:22.891153Z", - "shell.execute_reply": "2024-05-02T13:31:22.890592Z" + "iopub.execute_input": "2024-05-03T22:14:05.437196Z", + "iopub.status.busy": "2024-05-03T22:14:05.436968Z", + "iopub.status.idle": "2024-05-03T22:14:23.738759Z", + "shell.execute_reply": "2024-05-03T22:14:23.738130Z" } }, "outputs": [], @@ -358,10 +358,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:31:22.893902Z", - "iopub.status.busy": "2024-05-02T13:31:22.893482Z", - "iopub.status.idle": "2024-05-02T13:31:22.899665Z", - "shell.execute_reply": "2024-05-02T13:31:22.899163Z" + "iopub.execute_input": "2024-05-03T22:14:23.741480Z", + "iopub.status.busy": "2024-05-03T22:14:23.741237Z", + "iopub.status.idle": "2024-05-03T22:14:23.746242Z", + "shell.execute_reply": "2024-05-03T22:14:23.745775Z" } }, "outputs": [], @@ -399,10 +399,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:31:22.901785Z", - "iopub.status.busy": "2024-05-02T13:31:22.901421Z", - "iopub.status.idle": "2024-05-02T13:31:22.905536Z", - "shell.execute_reply": "2024-05-02T13:31:22.905079Z" + "iopub.execute_input": "2024-05-03T22:14:23.748235Z", + "iopub.status.busy": "2024-05-03T22:14:23.748043Z", + "iopub.status.idle": "2024-05-03T22:14:23.752308Z", + "shell.execute_reply": "2024-05-03T22:14:23.751869Z" }, "nbsphinx": "hidden" }, @@ -539,10 +539,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:31:22.907828Z", - "iopub.status.busy": "2024-05-02T13:31:22.907495Z", - "iopub.status.idle": "2024-05-02T13:31:22.916613Z", - "shell.execute_reply": "2024-05-02T13:31:22.916053Z" + "iopub.execute_input": "2024-05-03T22:14:23.754384Z", + "iopub.status.busy": "2024-05-03T22:14:23.754206Z", + "iopub.status.idle": "2024-05-03T22:14:23.763110Z", + "shell.execute_reply": "2024-05-03T22:14:23.762596Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:31:22.918829Z", - "iopub.status.busy": "2024-05-02T13:31:22.918470Z", - "iopub.status.idle": "2024-05-02T13:31:22.945762Z", - "shell.execute_reply": "2024-05-02T13:31:22.945263Z" + "iopub.execute_input": "2024-05-03T22:14:23.765283Z", + "iopub.status.busy": "2024-05-03T22:14:23.765096Z", + "iopub.status.idle": "2024-05-03T22:14:23.793620Z", + "shell.execute_reply": "2024-05-03T22:14:23.793110Z" } }, "outputs": [], @@ -707,10 +707,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:31:22.948317Z", - "iopub.status.busy": "2024-05-02T13:31:22.947957Z", - "iopub.status.idle": "2024-05-02T13:31:58.295904Z", - "shell.execute_reply": "2024-05-02T13:31:58.295292Z" + "iopub.execute_input": "2024-05-03T22:14:23.796002Z", + "iopub.status.busy": "2024-05-03T22:14:23.795655Z", + "iopub.status.idle": "2024-05-03T22:14:58.448850Z", + "shell.execute_reply": "2024-05-03T22:14:58.448223Z" } }, "outputs": [ @@ -726,21 +726,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.424\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.051\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 5.063\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.866\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5eda0fa7acfc4c69a6fd64aa21f86942", + "model_id": "6a2d1ebc02264b968f697dc18c196fec", "version_major": 2, "version_minor": 0 }, @@ -761,7 +761,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b18e7ba801a446028a5665b805c3092b", + "model_id": "1f4438897a134a15846e3092728a96b4", "version_major": 2, "version_minor": 0 }, @@ -784,21 +784,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.183\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.210\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 5.010\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.912\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "04737bf05b0a43c6b74ae63f548eaa56", + "model_id": "a7cc9fa9b61245d1adbfe33c6319db71", "version_major": 2, "version_minor": 0 }, @@ -819,7 +819,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "71aac2f3486246cba247d36a6f978f3e", + "model_id": "d77233602e2e46e0889971aa7e93d2d7", "version_major": 2, "version_minor": 0 }, @@ -842,21 +842,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.198\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.290\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.970\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.904\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "68bb63d4b0f342df9e664f04d954cefe", + "model_id": "e8a29fe7f9744ecc8675be3fb062865a", "version_major": 2, "version_minor": 0 }, @@ -877,7 +877,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "89c09267bf344dc0a3e1732a646a814c", + "model_id": "8e8e9146905941b1ae5a9f3dae938d16", "version_major": 2, "version_minor": 0 }, @@ -956,10 +956,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:31:58.298499Z", - "iopub.status.busy": "2024-05-02T13:31:58.298205Z", - "iopub.status.idle": "2024-05-02T13:31:58.314972Z", - "shell.execute_reply": "2024-05-02T13:31:58.314450Z" + "iopub.execute_input": "2024-05-03T22:14:58.451304Z", + "iopub.status.busy": "2024-05-03T22:14:58.451038Z", + "iopub.status.idle": "2024-05-03T22:14:58.468360Z", + "shell.execute_reply": "2024-05-03T22:14:58.467847Z" } }, "outputs": [], @@ -984,10 +984,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:31:58.317631Z", - "iopub.status.busy": "2024-05-02T13:31:58.317194Z", - "iopub.status.idle": "2024-05-02T13:31:58.823647Z", - "shell.execute_reply": "2024-05-02T13:31:58.822999Z" + "iopub.execute_input": "2024-05-03T22:14:58.475586Z", + "iopub.status.busy": "2024-05-03T22:14:58.475089Z", + "iopub.status.idle": "2024-05-03T22:14:58.955524Z", + "shell.execute_reply": "2024-05-03T22:14:58.954967Z" } }, "outputs": [], @@ -1007,10 +1007,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:31:58.826501Z", - "iopub.status.busy": "2024-05-02T13:31:58.826024Z", - "iopub.status.idle": "2024-05-02T13:35:33.898870Z", - "shell.execute_reply": "2024-05-02T13:35:33.898269Z" + "iopub.execute_input": "2024-05-03T22:14:58.957980Z", + "iopub.status.busy": "2024-05-03T22:14:58.957653Z", + "iopub.status.idle": "2024-05-03T22:18:35.947940Z", + "shell.execute_reply": "2024-05-03T22:18:35.947296Z" } }, "outputs": [ @@ -1058,7 +1058,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6e13650f14a8455391c6e536e8029de6", + "model_id": "31e95bb459fa4fa1a8c73383d5b787d7", "version_major": 2, "version_minor": 0 }, @@ -1097,10 +1097,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:33.901200Z", - "iopub.status.busy": "2024-05-02T13:35:33.900849Z", - "iopub.status.idle": "2024-05-02T13:35:34.345824Z", - "shell.execute_reply": "2024-05-02T13:35:34.345291Z" + "iopub.execute_input": "2024-05-03T22:18:35.950225Z", + "iopub.status.busy": "2024-05-03T22:18:35.949838Z", + "iopub.status.idle": "2024-05-03T22:18:36.413457Z", + "shell.execute_reply": "2024-05-03T22:18:36.412888Z" } }, "outputs": [ @@ -1241,10 +1241,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:34.348623Z", - "iopub.status.busy": "2024-05-02T13:35:34.348101Z", - "iopub.status.idle": "2024-05-02T13:35:34.410398Z", - "shell.execute_reply": "2024-05-02T13:35:34.409847Z" + "iopub.execute_input": "2024-05-03T22:18:36.415760Z", + "iopub.status.busy": "2024-05-03T22:18:36.415398Z", + "iopub.status.idle": "2024-05-03T22:18:36.478939Z", + "shell.execute_reply": "2024-05-03T22:18:36.478367Z" } }, "outputs": [ @@ -1348,10 +1348,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:34.412624Z", - "iopub.status.busy": "2024-05-02T13:35:34.412297Z", - "iopub.status.idle": "2024-05-02T13:35:34.420537Z", - "shell.execute_reply": "2024-05-02T13:35:34.420092Z" + "iopub.execute_input": "2024-05-03T22:18:36.481121Z", + "iopub.status.busy": "2024-05-03T22:18:36.480807Z", + "iopub.status.idle": "2024-05-03T22:18:36.489650Z", + "shell.execute_reply": "2024-05-03T22:18:36.489082Z" } }, "outputs": [ @@ -1481,10 +1481,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:34.422631Z", - "iopub.status.busy": "2024-05-02T13:35:34.422172Z", - "iopub.status.idle": "2024-05-02T13:35:34.427909Z", - "shell.execute_reply": "2024-05-02T13:35:34.427351Z" + "iopub.execute_input": "2024-05-03T22:18:36.491604Z", + "iopub.status.busy": "2024-05-03T22:18:36.491310Z", + "iopub.status.idle": "2024-05-03T22:18:36.495968Z", + "shell.execute_reply": "2024-05-03T22:18:36.495389Z" }, "nbsphinx": "hidden" }, @@ -1530,10 +1530,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:34.429903Z", - "iopub.status.busy": "2024-05-02T13:35:34.429513Z", - "iopub.status.idle": "2024-05-02T13:35:34.929890Z", - "shell.execute_reply": "2024-05-02T13:35:34.929343Z" + "iopub.execute_input": "2024-05-03T22:18:36.498160Z", + "iopub.status.busy": "2024-05-03T22:18:36.497776Z", + "iopub.status.idle": "2024-05-03T22:18:37.017541Z", + "shell.execute_reply": "2024-05-03T22:18:37.016937Z" } }, "outputs": [ @@ -1568,10 +1568,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:34.932064Z", - "iopub.status.busy": "2024-05-02T13:35:34.931755Z", - "iopub.status.idle": "2024-05-02T13:35:34.940068Z", - "shell.execute_reply": "2024-05-02T13:35:34.939616Z" + "iopub.execute_input": "2024-05-03T22:18:37.019891Z", + "iopub.status.busy": "2024-05-03T22:18:37.019553Z", + "iopub.status.idle": "2024-05-03T22:18:37.027896Z", + "shell.execute_reply": "2024-05-03T22:18:37.027396Z" } }, "outputs": [ @@ -1738,10 +1738,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:34.942162Z", - "iopub.status.busy": "2024-05-02T13:35:34.941850Z", - "iopub.status.idle": "2024-05-02T13:35:34.948886Z", - "shell.execute_reply": "2024-05-02T13:35:34.948446Z" + "iopub.execute_input": "2024-05-03T22:18:37.030138Z", + "iopub.status.busy": "2024-05-03T22:18:37.029792Z", + "iopub.status.idle": "2024-05-03T22:18:37.037008Z", + "shell.execute_reply": "2024-05-03T22:18:37.036530Z" }, "nbsphinx": "hidden" }, @@ -1817,10 +1817,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:34.950900Z", - "iopub.status.busy": "2024-05-02T13:35:34.950586Z", - "iopub.status.idle": "2024-05-02T13:35:35.383164Z", - "shell.execute_reply": "2024-05-02T13:35:35.382592Z" + "iopub.execute_input": "2024-05-03T22:18:37.039153Z", + "iopub.status.busy": "2024-05-03T22:18:37.038701Z", + "iopub.status.idle": "2024-05-03T22:18:37.522347Z", + "shell.execute_reply": "2024-05-03T22:18:37.521760Z" } }, "outputs": [ @@ -1857,10 +1857,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:35.385811Z", - "iopub.status.busy": "2024-05-02T13:35:35.385434Z", - "iopub.status.idle": "2024-05-02T13:35:35.400944Z", - "shell.execute_reply": "2024-05-02T13:35:35.400380Z" + "iopub.execute_input": "2024-05-03T22:18:37.524936Z", + "iopub.status.busy": "2024-05-03T22:18:37.524558Z", + "iopub.status.idle": "2024-05-03T22:18:37.540327Z", + "shell.execute_reply": "2024-05-03T22:18:37.539751Z" } }, "outputs": [ @@ -2017,10 +2017,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:35.403101Z", - "iopub.status.busy": "2024-05-02T13:35:35.402768Z", - "iopub.status.idle": "2024-05-02T13:35:35.408327Z", - "shell.execute_reply": "2024-05-02T13:35:35.407768Z" + "iopub.execute_input": "2024-05-03T22:18:37.542768Z", + "iopub.status.busy": "2024-05-03T22:18:37.542327Z", + "iopub.status.idle": "2024-05-03T22:18:37.548048Z", + "shell.execute_reply": "2024-05-03T22:18:37.547605Z" }, "nbsphinx": "hidden" }, @@ -2065,10 +2065,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:35.410595Z", - "iopub.status.busy": "2024-05-02T13:35:35.410279Z", - "iopub.status.idle": "2024-05-02T13:35:35.867212Z", - "shell.execute_reply": "2024-05-02T13:35:35.866444Z" + "iopub.execute_input": "2024-05-03T22:18:37.550113Z", + "iopub.status.busy": "2024-05-03T22:18:37.549725Z", + "iopub.status.idle": "2024-05-03T22:18:38.017099Z", + "shell.execute_reply": "2024-05-03T22:18:38.016510Z" } }, "outputs": [ @@ -2150,10 +2150,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:35.869845Z", - "iopub.status.busy": "2024-05-02T13:35:35.869643Z", - "iopub.status.idle": "2024-05-02T13:35:35.878725Z", - "shell.execute_reply": "2024-05-02T13:35:35.878152Z" + "iopub.execute_input": "2024-05-03T22:18:38.019657Z", + "iopub.status.busy": "2024-05-03T22:18:38.019457Z", + "iopub.status.idle": "2024-05-03T22:18:38.028709Z", + "shell.execute_reply": "2024-05-03T22:18:38.028123Z" } }, "outputs": [ @@ -2281,10 +2281,10 @@ "execution_count": 27, "metadata": { "execution": { - 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"iopub.status.idle": "2024-05-02T13:35:40.518573Z", - "shell.execute_reply": "2024-05-02T13:35:40.518025Z" + "iopub.execute_input": "2024-05-03T22:18:41.865540Z", + "iopub.status.busy": "2024-05-03T22:18:41.865189Z", + "iopub.status.idle": "2024-05-03T22:18:42.992072Z", + "shell.execute_reply": "2024-05-03T22:18:42.991446Z" }, "nbsphinx": "hidden" }, @@ -86,7 +86,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:40.521066Z", - "iopub.status.busy": "2024-05-02T13:35:40.520696Z", - "iopub.status.idle": "2024-05-02T13:35:40.538736Z", - "shell.execute_reply": "2024-05-02T13:35:40.538189Z" + "iopub.execute_input": "2024-05-03T22:18:42.994591Z", + "iopub.status.busy": "2024-05-03T22:18:42.994289Z", + "iopub.status.idle": "2024-05-03T22:18:43.013474Z", + "shell.execute_reply": "2024-05-03T22:18:43.012867Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:40.541010Z", - "iopub.status.busy": "2024-05-02T13:35:40.540537Z", - "iopub.status.idle": "2024-05-02T13:35:40.578774Z", - "shell.execute_reply": "2024-05-02T13:35:40.578218Z" + "iopub.execute_input": "2024-05-03T22:18:43.016275Z", + "iopub.status.busy": "2024-05-03T22:18:43.015700Z", + "iopub.status.idle": "2024-05-03T22:18:43.040335Z", + "shell.execute_reply": "2024-05-03T22:18:43.039749Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:40.580884Z", - "iopub.status.busy": "2024-05-02T13:35:40.580498Z", - "iopub.status.idle": "2024-05-02T13:35:40.583828Z", - "shell.execute_reply": "2024-05-02T13:35:40.583391Z" + "iopub.execute_input": "2024-05-03T22:18:43.042722Z", + "iopub.status.busy": "2024-05-03T22:18:43.042166Z", + "iopub.status.idle": "2024-05-03T22:18:43.045793Z", + "shell.execute_reply": "2024-05-03T22:18:43.045339Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:40.585735Z", - "iopub.status.busy": "2024-05-02T13:35:40.585558Z", - "iopub.status.idle": "2024-05-02T13:35:40.592841Z", - "shell.execute_reply": "2024-05-02T13:35:40.592434Z" + "iopub.execute_input": "2024-05-03T22:18:43.047929Z", + "iopub.status.busy": "2024-05-03T22:18:43.047540Z", + "iopub.status.idle": "2024-05-03T22:18:43.055302Z", + "shell.execute_reply": "2024-05-03T22:18:43.054867Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:40.594914Z", - "iopub.status.busy": "2024-05-02T13:35:40.594736Z", - "iopub.status.idle": "2024-05-02T13:35:40.597217Z", - "shell.execute_reply": "2024-05-02T13:35:40.596783Z" + "iopub.execute_input": "2024-05-03T22:18:43.057460Z", + "iopub.status.busy": "2024-05-03T22:18:43.057131Z", + "iopub.status.idle": "2024-05-03T22:18:43.059612Z", + "shell.execute_reply": "2024-05-03T22:18:43.059181Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:40.599186Z", - "iopub.status.busy": "2024-05-02T13:35:40.598893Z", - "iopub.status.idle": "2024-05-02T13:35:43.570531Z", - "shell.execute_reply": "2024-05-02T13:35:43.569904Z" + "iopub.execute_input": "2024-05-03T22:18:43.061675Z", + "iopub.status.busy": "2024-05-03T22:18:43.061355Z", + "iopub.status.idle": "2024-05-03T22:18:46.014180Z", + "shell.execute_reply": "2024-05-03T22:18:46.013613Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:43.572963Z", - "iopub.status.busy": "2024-05-02T13:35:43.572781Z", - "iopub.status.idle": "2024-05-02T13:35:43.582115Z", - "shell.execute_reply": "2024-05-02T13:35:43.581685Z" + "iopub.execute_input": "2024-05-03T22:18:46.016871Z", + "iopub.status.busy": "2024-05-03T22:18:46.016446Z", + "iopub.status.idle": "2024-05-03T22:18:46.026654Z", + "shell.execute_reply": "2024-05-03T22:18:46.026174Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:43.584040Z", - "iopub.status.busy": "2024-05-02T13:35:43.583870Z", - "iopub.status.idle": "2024-05-02T13:35:45.260343Z", - "shell.execute_reply": "2024-05-02T13:35:45.259730Z" + "iopub.execute_input": "2024-05-03T22:18:46.028995Z", + "iopub.status.busy": "2024-05-03T22:18:46.028625Z", + "iopub.status.idle": "2024-05-03T22:18:47.840477Z", + "shell.execute_reply": "2024-05-03T22:18:47.839694Z" } }, "outputs": [ @@ -484,10 +484,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:45.263214Z", - "iopub.status.busy": "2024-05-02T13:35:45.262626Z", - "iopub.status.idle": "2024-05-02T13:35:45.285315Z", - "shell.execute_reply": "2024-05-02T13:35:45.284826Z" + "iopub.execute_input": "2024-05-03T22:18:47.844321Z", + "iopub.status.busy": "2024-05-03T22:18:47.842999Z", + "iopub.status.idle": "2024-05-03T22:18:47.868606Z", + "shell.execute_reply": "2024-05-03T22:18:47.868063Z" }, "scrolled": true }, @@ -612,10 +612,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:45.287685Z", - "iopub.status.busy": "2024-05-02T13:35:45.287311Z", - "iopub.status.idle": "2024-05-02T13:35:45.296344Z", - "shell.execute_reply": "2024-05-02T13:35:45.295866Z" + "iopub.execute_input": "2024-05-03T22:18:47.872292Z", + "iopub.status.busy": "2024-05-03T22:18:47.871375Z", + "iopub.status.idle": "2024-05-03T22:18:47.883588Z", + "shell.execute_reply": "2024-05-03T22:18:47.883034Z" } }, "outputs": [ @@ -719,10 +719,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:45.298687Z", - "iopub.status.busy": "2024-05-02T13:35:45.298311Z", - "iopub.status.idle": "2024-05-02T13:35:45.308915Z", - "shell.execute_reply": "2024-05-02T13:35:45.308445Z" + "iopub.execute_input": "2024-05-03T22:18:47.887185Z", + "iopub.status.busy": "2024-05-03T22:18:47.886282Z", + "iopub.status.idle": "2024-05-03T22:18:47.899925Z", + "shell.execute_reply": "2024-05-03T22:18:47.899389Z" } }, "outputs": [ @@ -851,10 +851,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:45.312021Z", - "iopub.status.busy": "2024-05-02T13:35:45.311106Z", - "iopub.status.idle": "2024-05-02T13:35:45.322390Z", - "shell.execute_reply": "2024-05-02T13:35:45.321889Z" + "iopub.execute_input": "2024-05-03T22:18:47.903517Z", + "iopub.status.busy": "2024-05-03T22:18:47.902629Z", + "iopub.status.idle": "2024-05-03T22:18:47.914690Z", + "shell.execute_reply": "2024-05-03T22:18:47.914167Z" } }, "outputs": [ @@ -968,10 +968,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:45.325852Z", - "iopub.status.busy": "2024-05-02T13:35:45.324944Z", - "iopub.status.idle": "2024-05-02T13:35:45.337323Z", - "shell.execute_reply": "2024-05-02T13:35:45.336844Z" + "iopub.execute_input": "2024-05-03T22:18:47.918342Z", + "iopub.status.busy": "2024-05-03T22:18:47.917433Z", + "iopub.status.idle": "2024-05-03T22:18:47.928330Z", + "shell.execute_reply": "2024-05-03T22:18:47.927918Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:45.340755Z", - "iopub.status.busy": "2024-05-02T13:35:45.339849Z", - "iopub.status.idle": "2024-05-02T13:35:45.347650Z", - "shell.execute_reply": "2024-05-02T13:35:45.347202Z" + "iopub.execute_input": "2024-05-03T22:18:47.930411Z", + "iopub.status.busy": "2024-05-03T22:18:47.930226Z", + "iopub.status.idle": "2024-05-03T22:18:47.936854Z", + "shell.execute_reply": "2024-05-03T22:18:47.936282Z" } }, "outputs": [ @@ -1169,10 +1169,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:45.349802Z", - "iopub.status.busy": "2024-05-02T13:35:45.349485Z", - "iopub.status.idle": "2024-05-02T13:35:45.355932Z", - "shell.execute_reply": "2024-05-02T13:35:45.355430Z" + "iopub.execute_input": "2024-05-03T22:18:47.938796Z", + "iopub.status.busy": "2024-05-03T22:18:47.938616Z", + "iopub.status.idle": "2024-05-03T22:18:47.945319Z", + "shell.execute_reply": "2024-05-03T22:18:47.944704Z" } }, "outputs": [ @@ -1265,10 +1265,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:45.357944Z", - "iopub.status.busy": "2024-05-02T13:35:45.357626Z", - "iopub.status.idle": "2024-05-02T13:35:45.364133Z", - "shell.execute_reply": "2024-05-02T13:35:45.363705Z" + "iopub.execute_input": "2024-05-03T22:18:47.947381Z", + "iopub.status.busy": "2024-05-03T22:18:47.947195Z", + "iopub.status.idle": "2024-05-03T22:18:47.954553Z", + "shell.execute_reply": "2024-05-03T22:18:47.953973Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 680a1d4e9..cb4782a94 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -757,7 +757,7 @@

2. Load and format the text dataset 
 This dataset has 10 classes.
-Classes: {'beneficiary_not_allowed', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'change_pin', 'card_about_to_expire', 'supported_cards_and_currencies', 'cancel_transfer', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'getting_spare_card'}
+Classes: {'getting_spare_card', 'visa_or_mastercard', 'card_about_to_expire', 'cancel_transfer', 'lost_or_stolen_phone', 'change_pin', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'card_payment_fee_charged'}

Let’s view the i-th example in the dataset:

diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 41e265f67..78ee5bb2f 100644 --- a/master/tutorials/datalab/text.ipynb +++ b/master/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:47.838605Z", - "iopub.status.busy": "2024-05-02T13:35:47.838104Z", - "iopub.status.idle": "2024-05-02T13:35:50.401091Z", - "shell.execute_reply": "2024-05-02T13:35:50.400550Z" + "iopub.execute_input": "2024-05-03T22:18:51.256411Z", + "iopub.status.busy": "2024-05-03T22:18:51.255992Z", + "iopub.status.idle": "2024-05-03T22:18:54.011020Z", + "shell.execute_reply": "2024-05-03T22:18:54.010318Z" }, "nbsphinx": "hidden" }, @@ -96,7 +96,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -121,10 +121,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:50.403683Z", - "iopub.status.busy": "2024-05-02T13:35:50.403220Z", - "iopub.status.idle": "2024-05-02T13:35:50.406478Z", - "shell.execute_reply": "2024-05-02T13:35:50.406005Z" + "iopub.execute_input": "2024-05-03T22:18:54.013954Z", + "iopub.status.busy": "2024-05-03T22:18:54.013577Z", + "iopub.status.idle": "2024-05-03T22:18:54.017174Z", + "shell.execute_reply": "2024-05-03T22:18:54.016602Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:50.408507Z", - "iopub.status.busy": "2024-05-02T13:35:50.408111Z", - "iopub.status.idle": "2024-05-02T13:35:50.411229Z", - "shell.execute_reply": "2024-05-02T13:35:50.410785Z" + "iopub.execute_input": "2024-05-03T22:18:54.019217Z", + "iopub.status.busy": "2024-05-03T22:18:54.018946Z", + "iopub.status.idle": "2024-05-03T22:18:54.022038Z", + "shell.execute_reply": "2024-05-03T22:18:54.021589Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:50.413221Z", - "iopub.status.busy": "2024-05-02T13:35:50.412825Z", - "iopub.status.idle": "2024-05-02T13:35:50.451997Z", - "shell.execute_reply": "2024-05-02T13:35:50.451480Z" + "iopub.execute_input": "2024-05-03T22:18:54.024007Z", + "iopub.status.busy": "2024-05-03T22:18:54.023695Z", + "iopub.status.idle": "2024-05-03T22:18:54.048811Z", + "shell.execute_reply": "2024-05-03T22:18:54.048286Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:50.454081Z", - "iopub.status.busy": "2024-05-02T13:35:50.453649Z", - "iopub.status.idle": "2024-05-02T13:35:50.457334Z", - "shell.execute_reply": "2024-05-02T13:35:50.456813Z" + "iopub.execute_input": "2024-05-03T22:18:54.051314Z", + "iopub.status.busy": "2024-05-03T22:18:54.050743Z", + "iopub.status.idle": "2024-05-03T22:18:54.054516Z", + "shell.execute_reply": "2024-05-03T22:18:54.054065Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'beneficiary_not_allowed', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'change_pin', 'card_about_to_expire', 'supported_cards_and_currencies', 'cancel_transfer', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'getting_spare_card'}\n" + "Classes: {'getting_spare_card', 'visa_or_mastercard', 'card_about_to_expire', 'cancel_transfer', 'lost_or_stolen_phone', 'change_pin', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'card_payment_fee_charged'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:50.459362Z", - "iopub.status.busy": "2024-05-02T13:35:50.459043Z", - "iopub.status.idle": "2024-05-02T13:35:50.462114Z", - "shell.execute_reply": "2024-05-02T13:35:50.461557Z" + "iopub.execute_input": "2024-05-03T22:18:54.056487Z", + "iopub.status.busy": "2024-05-03T22:18:54.056308Z", + "iopub.status.idle": "2024-05-03T22:18:54.059349Z", + "shell.execute_reply": "2024-05-03T22:18:54.058809Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:50.464052Z", - "iopub.status.busy": "2024-05-02T13:35:50.463782Z", - "iopub.status.idle": "2024-05-02T13:35:54.076801Z", - "shell.execute_reply": "2024-05-02T13:35:54.076161Z" + "iopub.execute_input": "2024-05-03T22:18:54.061466Z", + "iopub.status.busy": "2024-05-03T22:18:54.061137Z", + "iopub.status.idle": "2024-05-03T22:18:57.706188Z", + "shell.execute_reply": "2024-05-03T22:18:57.705631Z" } }, "outputs": [ @@ -424,10 +424,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:54.079516Z", - "iopub.status.busy": "2024-05-02T13:35:54.079310Z", - "iopub.status.idle": "2024-05-02T13:35:54.969276Z", - "shell.execute_reply": "2024-05-02T13:35:54.968700Z" + "iopub.execute_input": "2024-05-03T22:18:57.708836Z", + "iopub.status.busy": "2024-05-03T22:18:57.708571Z", + "iopub.status.idle": "2024-05-03T22:18:58.566923Z", + "shell.execute_reply": "2024-05-03T22:18:58.566334Z" }, "scrolled": true }, @@ -459,10 +459,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:54.972281Z", - "iopub.status.busy": "2024-05-02T13:35:54.971874Z", - "iopub.status.idle": "2024-05-02T13:35:54.974799Z", - "shell.execute_reply": "2024-05-02T13:35:54.974304Z" + "iopub.execute_input": "2024-05-03T22:18:58.570713Z", + "iopub.status.busy": "2024-05-03T22:18:58.569608Z", + "iopub.status.idle": "2024-05-03T22:18:58.573844Z", + "shell.execute_reply": "2024-05-03T22:18:58.573346Z" } }, "outputs": [], @@ -482,10 +482,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:54.977235Z", - "iopub.status.busy": "2024-05-02T13:35:54.976847Z", - "iopub.status.idle": "2024-05-02T13:35:56.472356Z", - "shell.execute_reply": "2024-05-02T13:35:56.471709Z" + "iopub.execute_input": "2024-05-03T22:18:58.577414Z", + "iopub.status.busy": "2024-05-03T22:18:58.576460Z", + "iopub.status.idle": "2024-05-03T22:19:00.174840Z", + "shell.execute_reply": "2024-05-03T22:19:00.174008Z" }, "scrolled": true }, @@ -538,10 +538,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:56.475867Z", - "iopub.status.busy": "2024-05-02T13:35:56.475451Z", - "iopub.status.idle": "2024-05-02T13:35:56.500868Z", - "shell.execute_reply": "2024-05-02T13:35:56.500384Z" + "iopub.execute_input": "2024-05-03T22:19:00.178010Z", + "iopub.status.busy": "2024-05-03T22:19:00.177194Z", + "iopub.status.idle": "2024-05-03T22:19:00.201123Z", + "shell.execute_reply": "2024-05-03T22:19:00.200568Z" }, "scrolled": true }, @@ -666,10 +666,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:56.504367Z", - "iopub.status.busy": "2024-05-02T13:35:56.503438Z", - "iopub.status.idle": "2024-05-02T13:35:56.514997Z", - "shell.execute_reply": "2024-05-02T13:35:56.514493Z" + "iopub.execute_input": "2024-05-03T22:19:00.204542Z", + "iopub.status.busy": "2024-05-03T22:19:00.203466Z", + "iopub.status.idle": "2024-05-03T22:19:00.215441Z", + "shell.execute_reply": "2024-05-03T22:19:00.214954Z" }, "scrolled": true }, @@ -779,10 +779,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:56.518488Z", - "iopub.status.busy": "2024-05-02T13:35:56.517551Z", - "iopub.status.idle": "2024-05-02T13:35:56.524044Z", - "shell.execute_reply": "2024-05-02T13:35:56.523521Z" + "iopub.execute_input": "2024-05-03T22:19:00.219081Z", + "iopub.status.busy": "2024-05-03T22:19:00.218012Z", + "iopub.status.idle": "2024-05-03T22:19:00.224835Z", + "shell.execute_reply": "2024-05-03T22:19:00.224292Z" } }, "outputs": [ @@ -820,10 +820,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:56.526263Z", - "iopub.status.busy": "2024-05-02T13:35:56.526088Z", - "iopub.status.idle": "2024-05-02T13:35:56.533124Z", - "shell.execute_reply": "2024-05-02T13:35:56.532490Z" + "iopub.execute_input": "2024-05-03T22:19:00.227048Z", + "iopub.status.busy": "2024-05-03T22:19:00.226749Z", + "iopub.status.idle": "2024-05-03T22:19:00.233483Z", + "shell.execute_reply": "2024-05-03T22:19:00.232989Z" } }, "outputs": [ @@ -940,10 +940,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:56.535246Z", - "iopub.status.busy": "2024-05-02T13:35:56.535071Z", - "iopub.status.idle": "2024-05-02T13:35:56.543216Z", - "shell.execute_reply": "2024-05-02T13:35:56.542787Z" + "iopub.execute_input": "2024-05-03T22:19:00.236235Z", + "iopub.status.busy": "2024-05-03T22:19:00.235388Z", + "iopub.status.idle": "2024-05-03T22:19:00.244560Z", + "shell.execute_reply": "2024-05-03T22:19:00.244065Z" } }, "outputs": [ @@ -1026,10 +1026,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:56.545190Z", - "iopub.status.busy": "2024-05-02T13:35:56.544874Z", - "iopub.status.idle": "2024-05-02T13:35:56.550575Z", - "shell.execute_reply": "2024-05-02T13:35:56.550112Z" + "iopub.execute_input": "2024-05-03T22:19:00.246732Z", + "iopub.status.busy": "2024-05-03T22:19:00.246410Z", + "iopub.status.idle": "2024-05-03T22:19:00.252339Z", + "shell.execute_reply": "2024-05-03T22:19:00.251782Z" } }, "outputs": [ @@ -1137,10 +1137,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:56.552555Z", - "iopub.status.busy": "2024-05-02T13:35:56.552231Z", - "iopub.status.idle": "2024-05-02T13:35:56.560232Z", - "shell.execute_reply": "2024-05-02T13:35:56.559799Z" + "iopub.execute_input": "2024-05-03T22:19:00.254313Z", + "iopub.status.busy": "2024-05-03T22:19:00.254016Z", + "iopub.status.idle": "2024-05-03T22:19:00.262432Z", + "shell.execute_reply": "2024-05-03T22:19:00.261993Z" } }, "outputs": [ @@ -1251,10 +1251,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:56.562062Z", - "iopub.status.busy": "2024-05-02T13:35:56.561875Z", - "iopub.status.idle": "2024-05-02T13:35:56.567294Z", - "shell.execute_reply": "2024-05-02T13:35:56.566845Z" + "iopub.execute_input": "2024-05-03T22:19:00.264497Z", + "iopub.status.busy": "2024-05-03T22:19:00.264190Z", + "iopub.status.idle": "2024-05-03T22:19:00.269570Z", + "shell.execute_reply": "2024-05-03T22:19:00.269010Z" } }, "outputs": [ @@ -1322,10 +1322,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:56.569061Z", - "iopub.status.busy": "2024-05-02T13:35:56.568891Z", - "iopub.status.idle": "2024-05-02T13:35:56.574180Z", - "shell.execute_reply": "2024-05-02T13:35:56.573675Z" + "iopub.execute_input": "2024-05-03T22:19:00.271631Z", + "iopub.status.busy": "2024-05-03T22:19:00.271227Z", + "iopub.status.idle": "2024-05-03T22:19:00.276569Z", + "shell.execute_reply": "2024-05-03T22:19:00.276122Z" } }, "outputs": [ @@ -1404,10 +1404,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:56.576075Z", - "iopub.status.busy": "2024-05-02T13:35:56.575901Z", - "iopub.status.idle": "2024-05-02T13:35:56.579311Z", - "shell.execute_reply": "2024-05-02T13:35:56.578839Z" + "iopub.execute_input": "2024-05-03T22:19:00.278681Z", + "iopub.status.busy": "2024-05-03T22:19:00.278267Z", + "iopub.status.idle": "2024-05-03T22:19:00.281840Z", + "shell.execute_reply": "2024-05-03T22:19:00.281378Z" } }, "outputs": [ @@ -1455,10 +1455,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:56.581207Z", - "iopub.status.busy": "2024-05-02T13:35:56.581037Z", - "iopub.status.idle": "2024-05-02T13:35:56.586171Z", - "shell.execute_reply": "2024-05-02T13:35:56.585696Z" + "iopub.execute_input": "2024-05-03T22:19:00.283954Z", + "iopub.status.busy": "2024-05-03T22:19:00.283568Z", + "iopub.status.idle": "2024-05-03T22:19:00.288853Z", + "shell.execute_reply": "2024-05-03T22:19:00.288377Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index e6574de09..55b5c8e27 100644 --- a/master/tutorials/dataset_health.ipynb +++ b/master/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:35:59.559173Z", - "iopub.status.busy": "2024-05-02T13:35:59.559002Z", - "iopub.status.idle": "2024-05-02T13:36:00.630827Z", - "shell.execute_reply": "2024-05-02T13:36:00.630210Z" + "iopub.execute_input": "2024-05-03T22:19:03.531541Z", + "iopub.status.busy": "2024-05-03T22:19:03.531342Z", + "iopub.status.idle": "2024-05-03T22:19:04.683579Z", + "shell.execute_reply": "2024-05-03T22:19:04.682951Z" }, "nbsphinx": "hidden" }, @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:00.633359Z", - "iopub.status.busy": "2024-05-02T13:36:00.633049Z", - "iopub.status.idle": "2024-05-02T13:36:00.635941Z", - "shell.execute_reply": "2024-05-02T13:36:00.635417Z" + "iopub.execute_input": "2024-05-03T22:19:04.686260Z", + "iopub.status.busy": "2024-05-03T22:19:04.685950Z", + "iopub.status.idle": "2024-05-03T22:19:04.688925Z", + "shell.execute_reply": "2024-05-03T22:19:04.688463Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:00.637996Z", - "iopub.status.busy": "2024-05-02T13:36:00.637812Z", - "iopub.status.idle": "2024-05-02T13:36:00.649479Z", - "shell.execute_reply": "2024-05-02T13:36:00.648965Z" + "iopub.execute_input": "2024-05-03T22:19:04.691132Z", + "iopub.status.busy": "2024-05-03T22:19:04.690809Z", + "iopub.status.idle": "2024-05-03T22:19:04.703045Z", + "shell.execute_reply": "2024-05-03T22:19:04.702563Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:00.651655Z", - "iopub.status.busy": "2024-05-02T13:36:00.651353Z", - "iopub.status.idle": "2024-05-02T13:36:05.782571Z", - "shell.execute_reply": "2024-05-02T13:36:05.781956Z" + "iopub.execute_input": "2024-05-03T22:19:04.705298Z", + "iopub.status.busy": "2024-05-03T22:19:04.704971Z", + "iopub.status.idle": "2024-05-03T22:19:08.788660Z", + "shell.execute_reply": "2024-05-03T22:19:08.787999Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index dbf5fb015..ffc531f5e 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -797,13 +797,13 @@

How can I find label issues in big datasets with limited memory?
-
+
-
+
@@ -1706,7 +1706,7 @@

How to handle near-duplicate data identified by cleanlab? 
-/tmp/ipykernel_7630/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
+/tmp/ipykernel_7821/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
   to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values

@@ -1748,7 +1748,7 @@

Can’t find an answer to your question?new Github issue. Our developers may also provide personalized assistance in our Slack Community.

Professional support and services are also available from our ML experts, learn more by emailing: team@cleanlab.ai

diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index f301725b8..4aa52a47a 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:07.874118Z", - "iopub.status.busy": "2024-05-02T13:36:07.873936Z", - "iopub.status.idle": "2024-05-02T13:36:08.941713Z", - "shell.execute_reply": "2024-05-02T13:36:08.941106Z" + "iopub.execute_input": "2024-05-03T22:19:10.893983Z", + "iopub.status.busy": "2024-05-03T22:19:10.893582Z", + "iopub.status.idle": "2024-05-03T22:19:12.064021Z", + "shell.execute_reply": "2024-05-03T22:19:12.063409Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:08.944769Z", - "iopub.status.busy": "2024-05-02T13:36:08.944303Z", - "iopub.status.idle": "2024-05-02T13:36:08.948166Z", - "shell.execute_reply": "2024-05-02T13:36:08.947647Z" + "iopub.execute_input": "2024-05-03T22:19:12.067106Z", + "iopub.status.busy": "2024-05-03T22:19:12.066459Z", + "iopub.status.idle": "2024-05-03T22:19:12.070230Z", + "shell.execute_reply": "2024-05-03T22:19:12.069765Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:08.950206Z", - "iopub.status.busy": "2024-05-02T13:36:08.950025Z", - "iopub.status.idle": "2024-05-02T13:36:11.819547Z", - "shell.execute_reply": "2024-05-02T13:36:11.818962Z" + "iopub.execute_input": "2024-05-03T22:19:12.072440Z", + "iopub.status.busy": "2024-05-03T22:19:12.072092Z", + "iopub.status.idle": "2024-05-03T22:19:15.260750Z", + "shell.execute_reply": "2024-05-03T22:19:15.260065Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:11.822329Z", - "iopub.status.busy": "2024-05-02T13:36:11.821753Z", - "iopub.status.idle": "2024-05-02T13:36:11.850237Z", - "shell.execute_reply": "2024-05-02T13:36:11.849584Z" + "iopub.execute_input": "2024-05-03T22:19:15.263892Z", + "iopub.status.busy": "2024-05-03T22:19:15.263099Z", + "iopub.status.idle": "2024-05-03T22:19:15.302101Z", + "shell.execute_reply": "2024-05-03T22:19:15.301480Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:11.852881Z", - "iopub.status.busy": "2024-05-02T13:36:11.852508Z", - "iopub.status.idle": "2024-05-02T13:36:11.874844Z", - "shell.execute_reply": "2024-05-02T13:36:11.874250Z" + "iopub.execute_input": "2024-05-03T22:19:15.304888Z", + "iopub.status.busy": "2024-05-03T22:19:15.304531Z", + "iopub.status.idle": "2024-05-03T22:19:15.341360Z", + "shell.execute_reply": "2024-05-03T22:19:15.340592Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:11.877259Z", - "iopub.status.busy": "2024-05-02T13:36:11.876900Z", - "iopub.status.idle": "2024-05-02T13:36:11.879766Z", - "shell.execute_reply": "2024-05-02T13:36:11.879340Z" + "iopub.execute_input": "2024-05-03T22:19:15.344087Z", + "iopub.status.busy": "2024-05-03T22:19:15.343833Z", + "iopub.status.idle": "2024-05-03T22:19:15.347106Z", + "shell.execute_reply": "2024-05-03T22:19:15.346615Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:11.881759Z", - "iopub.status.busy": "2024-05-02T13:36:11.881347Z", - "iopub.status.idle": "2024-05-02T13:36:11.884049Z", - "shell.execute_reply": "2024-05-02T13:36:11.883520Z" + "iopub.execute_input": "2024-05-03T22:19:15.349298Z", + "iopub.status.busy": "2024-05-03T22:19:15.348893Z", + "iopub.status.idle": "2024-05-03T22:19:15.351710Z", + "shell.execute_reply": "2024-05-03T22:19:15.351155Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:11.886130Z", - "iopub.status.busy": "2024-05-02T13:36:11.885737Z", - "iopub.status.idle": "2024-05-02T13:36:11.908622Z", - "shell.execute_reply": "2024-05-02T13:36:11.908088Z" + "iopub.execute_input": "2024-05-03T22:19:15.354043Z", + "iopub.status.busy": "2024-05-03T22:19:15.353734Z", + "iopub.status.idle": "2024-05-03T22:19:15.381052Z", + "shell.execute_reply": "2024-05-03T22:19:15.380464Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c8675f2206254231ac50bfa6c4d67af1", + "model_id": "304fe3999a114c7386eab529b8cc62bb", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b275747ca94c4ec2a097ff438d3c5d0a", + "model_id": "d9da660cfa3e40999076282056565242", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:11.914849Z", - "iopub.status.busy": "2024-05-02T13:36:11.914510Z", - "iopub.status.idle": "2024-05-02T13:36:11.920965Z", - "shell.execute_reply": "2024-05-02T13:36:11.920520Z" + "iopub.execute_input": "2024-05-03T22:19:15.384883Z", + "iopub.status.busy": "2024-05-03T22:19:15.384623Z", + "iopub.status.idle": "2024-05-03T22:19:15.391640Z", + "shell.execute_reply": "2024-05-03T22:19:15.391158Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:11.922990Z", - "iopub.status.busy": "2024-05-02T13:36:11.922683Z", - "iopub.status.idle": "2024-05-02T13:36:11.925910Z", - "shell.execute_reply": "2024-05-02T13:36:11.925489Z" + "iopub.execute_input": "2024-05-03T22:19:15.393858Z", + "iopub.status.busy": "2024-05-03T22:19:15.393512Z", + "iopub.status.idle": "2024-05-03T22:19:15.397055Z", + "shell.execute_reply": "2024-05-03T22:19:15.396580Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:11.927865Z", - "iopub.status.busy": "2024-05-02T13:36:11.927567Z", - "iopub.status.idle": "2024-05-02T13:36:11.933624Z", - "shell.execute_reply": "2024-05-02T13:36:11.933194Z" + "iopub.execute_input": "2024-05-03T22:19:15.399203Z", + "iopub.status.busy": "2024-05-03T22:19:15.398863Z", + "iopub.status.idle": "2024-05-03T22:19:15.405425Z", + "shell.execute_reply": "2024-05-03T22:19:15.404953Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - 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"id": "95f5d735", + "id": "35f77967", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1327,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "42e61293", + "id": "d393b093", "metadata": {}, "source": [ "When detecting underperforming groups in a dataset, Datalab provides the option for passing pre-computed\n", @@ -1340,13 +1340,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "5992f1fe", + "id": "7abfae8f", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:15.302734Z", - "iopub.status.busy": "2024-05-02T13:36:15.302335Z", - "iopub.status.idle": "2024-05-02T13:36:15.411141Z", - "shell.execute_reply": "2024-05-02T13:36:15.410571Z" + "iopub.execute_input": "2024-05-03T22:19:18.808902Z", + "iopub.status.busy": "2024-05-03T22:19:18.808707Z", + "iopub.status.idle": "2024-05-03T22:19:18.913787Z", + "shell.execute_reply": "2024-05-03T22:19:18.913199Z" } }, "outputs": [ @@ -1354,7 +1354,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Finding underperforming_group issues ...\n", + "Finding underperforming_group issues ..." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "\n", "Audit complete. 0 issues found in the dataset.\n" ] @@ -1387,7 +1394,7 @@ }, { "cell_type": "markdown", - "id": "e8754b76", + "id": "0278748e", "metadata": {}, "source": [ "For a tabular dataset, you can alternatively use a categorical column's values as cluster IDs:" @@ -1396,13 +1403,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "f5f28bd3", + "id": "55f84d7c", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:15.413620Z", - "iopub.status.busy": "2024-05-02T13:36:15.413380Z", - "iopub.status.idle": "2024-05-02T13:36:15.480227Z", - "shell.execute_reply": "2024-05-02T13:36:15.479652Z" + "iopub.execute_input": "2024-05-03T22:19:18.916879Z", + "iopub.status.busy": "2024-05-03T22:19:18.916484Z", + "iopub.status.idle": "2024-05-03T22:19:18.983796Z", + "shell.execute_reply": "2024-05-03T22:19:18.983255Z" } }, "outputs": [ @@ -1410,14 +1417,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Finding underperforming_group issues ..." - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", + "Finding underperforming_group issues ...\n", "\n", "Audit complete. 0 issues found in the dataset.\n" ] @@ -1445,7 +1445,7 @@ }, { "cell_type": "markdown", - "id": "dc916c1f", + "id": "066461e0", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by cleanlab?\n", @@ -1456,13 +1456,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "166ac0a2", + "id": "7c609c21", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:15.482569Z", - "iopub.status.busy": "2024-05-02T13:36:15.482397Z", - "iopub.status.idle": "2024-05-02T13:36:15.489778Z", - "shell.execute_reply": "2024-05-02T13:36:15.489353Z" + "iopub.execute_input": "2024-05-03T22:19:18.986901Z", + "iopub.status.busy": "2024-05-03T22:19:18.986353Z", + "iopub.status.idle": "2024-05-03T22:19:18.996382Z", + "shell.execute_reply": "2024-05-03T22:19:18.995879Z" } }, "outputs": [], @@ -1564,7 +1564,7 @@ }, { "cell_type": "markdown", - "id": "22dde99e", + "id": "74aefacf", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1579,13 +1579,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "8ade84f7", + "id": "d5170d86", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:15.491920Z", - "iopub.status.busy": "2024-05-02T13:36:15.491577Z", - "iopub.status.idle": "2024-05-02T13:36:15.510549Z", - "shell.execute_reply": "2024-05-02T13:36:15.509911Z" + "iopub.execute_input": "2024-05-03T22:19:18.998762Z", + "iopub.status.busy": "2024-05-03T22:19:18.998394Z", + "iopub.status.idle": "2024-05-03T22:19:19.018760Z", + "shell.execute_reply": "2024-05-03T22:19:19.018136Z" } }, "outputs": [ @@ -1602,7 +1602,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_7630/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_7821/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values\n" ] } @@ -1636,13 +1636,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "6c72ae6d", + "id": "4b5f30cf", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:15.512465Z", - "iopub.status.busy": "2024-05-02T13:36:15.512294Z", - "iopub.status.idle": "2024-05-02T13:36:15.515444Z", - "shell.execute_reply": "2024-05-02T13:36:15.514922Z" + "iopub.execute_input": "2024-05-03T22:19:19.021063Z", + "iopub.status.busy": "2024-05-03T22:19:19.020625Z", + "iopub.status.idle": "2024-05-03T22:19:19.024304Z", + "shell.execute_reply": "2024-05-03T22:19:19.023730Z" } }, "outputs": [ @@ -1737,7 +1737,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "022b5650239c4616a88276c18d5076c5": { + "19ba9c8dc125441a8896e09be3231373": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1790,7 +1790,7 @@ "width": null } }, - 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"iopub.execute_input": "2024-05-02T13:36:18.700799Z", - "iopub.status.busy": "2024-05-02T13:36:18.700630Z", - "iopub.status.idle": "2024-05-02T13:36:19.828373Z", - "shell.execute_reply": "2024-05-02T13:36:19.827832Z" + "iopub.execute_input": "2024-05-03T22:19:23.368183Z", + "iopub.status.busy": "2024-05-03T22:19:23.367680Z", + "iopub.status.idle": "2024-05-03T22:19:24.606690Z", + "shell.execute_reply": "2024-05-03T22:19:24.606125Z" }, "nbsphinx": "hidden" }, @@ -68,7 +68,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -95,10 +95,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:19.830709Z", - "iopub.status.busy": "2024-05-02T13:36:19.830440Z", - "iopub.status.idle": "2024-05-02T13:36:20.007015Z", - "shell.execute_reply": "2024-05-02T13:36:20.006472Z" + "iopub.execute_input": "2024-05-03T22:19:24.609540Z", + "iopub.status.busy": "2024-05-03T22:19:24.608977Z", + "iopub.status.idle": "2024-05-03T22:19:24.794847Z", + "shell.execute_reply": "2024-05-03T22:19:24.794231Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:20.009475Z", - "iopub.status.busy": "2024-05-02T13:36:20.009281Z", - "iopub.status.idle": "2024-05-02T13:36:20.021539Z", - "shell.execute_reply": "2024-05-02T13:36:20.020986Z" + "iopub.execute_input": "2024-05-03T22:19:24.797519Z", + "iopub.status.busy": "2024-05-03T22:19:24.797321Z", + "iopub.status.idle": "2024-05-03T22:19:24.811209Z", + "shell.execute_reply": "2024-05-03T22:19:24.810711Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - 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"iopub.execute_input": "2024-05-02T13:36:20.286143Z", - "iopub.status.busy": "2024-05-02T13:36:20.285799Z", - "iopub.status.idle": "2024-05-02T13:36:21.901046Z", - "shell.execute_reply": "2024-05-02T13:36:21.900412Z" + "iopub.execute_input": "2024-05-03T22:19:25.060175Z", + "iopub.status.busy": "2024-05-03T22:19:25.059939Z", + "iopub.status.idle": "2024-05-03T22:19:26.863299Z", + "shell.execute_reply": "2024-05-03T22:19:26.862604Z" } }, "outputs": [ @@ -483,10 +483,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:21.903420Z", - "iopub.status.busy": "2024-05-02T13:36:21.903029Z", - "iopub.status.idle": "2024-05-02T13:36:21.921042Z", - "shell.execute_reply": "2024-05-02T13:36:21.920562Z" + "iopub.execute_input": "2024-05-03T22:19:26.866152Z", + "iopub.status.busy": "2024-05-03T22:19:26.865573Z", + "iopub.status.idle": "2024-05-03T22:19:26.885297Z", + "shell.execute_reply": "2024-05-03T22:19:26.884768Z" }, "scrolled": true }, @@ -611,10 +611,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:21.923128Z", - "iopub.status.busy": "2024-05-02T13:36:21.922807Z", - "iopub.status.idle": "2024-05-02T13:36:23.304206Z", - "shell.execute_reply": "2024-05-02T13:36:23.303576Z" + "iopub.execute_input": "2024-05-03T22:19:26.887574Z", + "iopub.status.busy": "2024-05-03T22:19:26.887208Z", + "iopub.status.idle": "2024-05-03T22:19:28.369219Z", + "shell.execute_reply": "2024-05-03T22:19:28.368537Z" }, "id": "AaHC5MRKjruT" }, @@ -733,10 +733,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:23.306871Z", - "iopub.status.busy": "2024-05-02T13:36:23.306240Z", - "iopub.status.idle": "2024-05-02T13:36:23.320385Z", - "shell.execute_reply": "2024-05-02T13:36:23.319799Z" + "iopub.execute_input": "2024-05-03T22:19:28.371936Z", + "iopub.status.busy": "2024-05-03T22:19:28.371282Z", + "iopub.status.idle": "2024-05-03T22:19:28.385746Z", + "shell.execute_reply": "2024-05-03T22:19:28.385216Z" }, "id": "Wy27rvyhjruU" }, @@ -785,10 +785,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:23.322704Z", - "iopub.status.busy": "2024-05-02T13:36:23.322233Z", - "iopub.status.idle": "2024-05-02T13:36:23.399782Z", - "shell.execute_reply": "2024-05-02T13:36:23.399168Z" + "iopub.execute_input": "2024-05-03T22:19:28.387918Z", + "iopub.status.busy": "2024-05-03T22:19:28.387729Z", + "iopub.status.idle": "2024-05-03T22:19:28.466268Z", + "shell.execute_reply": "2024-05-03T22:19:28.465661Z" }, "id": "Db8YHnyVjruU" }, @@ -895,10 +895,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:23.401938Z", - "iopub.status.busy": "2024-05-02T13:36:23.401715Z", - "iopub.status.idle": "2024-05-02T13:36:23.613354Z", - "shell.execute_reply": "2024-05-02T13:36:23.612789Z" + "iopub.execute_input": "2024-05-03T22:19:28.468609Z", + "iopub.status.busy": "2024-05-03T22:19:28.468297Z", + "iopub.status.idle": "2024-05-03T22:19:28.685899Z", + "shell.execute_reply": "2024-05-03T22:19:28.685330Z" }, "id": "iJqAHuS2jruV" }, @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:23.615594Z", - "iopub.status.busy": "2024-05-02T13:36:23.615241Z", - "iopub.status.idle": "2024-05-02T13:36:23.632047Z", - "shell.execute_reply": "2024-05-02T13:36:23.631621Z" + "iopub.execute_input": "2024-05-03T22:19:28.688012Z", + "iopub.status.busy": "2024-05-03T22:19:28.687826Z", + "iopub.status.idle": "2024-05-03T22:19:28.705194Z", + "shell.execute_reply": "2024-05-03T22:19:28.704639Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1404,10 +1404,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:23.634225Z", - "iopub.status.busy": "2024-05-02T13:36:23.633812Z", - "iopub.status.idle": "2024-05-02T13:36:23.643243Z", - "shell.execute_reply": "2024-05-02T13:36:23.642818Z" + "iopub.execute_input": "2024-05-03T22:19:28.707321Z", + "iopub.status.busy": "2024-05-03T22:19:28.707139Z", + "iopub.status.idle": "2024-05-03T22:19:28.717632Z", + "shell.execute_reply": "2024-05-03T22:19:28.717176Z" }, "id": "0lonvOYvjruV" }, @@ -1554,10 +1554,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:23.645223Z", - "iopub.status.busy": "2024-05-02T13:36:23.644896Z", - "iopub.status.idle": "2024-05-02T13:36:23.730756Z", - "shell.execute_reply": "2024-05-02T13:36:23.730145Z" + "iopub.execute_input": "2024-05-03T22:19:28.719766Z", + "iopub.status.busy": "2024-05-03T22:19:28.719421Z", + "iopub.status.idle": "2024-05-03T22:19:28.807828Z", + "shell.execute_reply": "2024-05-03T22:19:28.807250Z" }, "id": "MfqTCa3kjruV" }, @@ -1638,10 +1638,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:23.733278Z", - "iopub.status.busy": "2024-05-02T13:36:23.732849Z", - "iopub.status.idle": "2024-05-02T13:36:23.851838Z", - "shell.execute_reply": "2024-05-02T13:36:23.851296Z" + "iopub.execute_input": "2024-05-03T22:19:28.810286Z", + "iopub.status.busy": "2024-05-03T22:19:28.809967Z", + "iopub.status.idle": "2024-05-03T22:19:28.944035Z", + "shell.execute_reply": "2024-05-03T22:19:28.943379Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1701,10 +1701,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:23.854028Z", - "iopub.status.busy": "2024-05-02T13:36:23.853781Z", - "iopub.status.idle": "2024-05-02T13:36:23.857894Z", - "shell.execute_reply": "2024-05-02T13:36:23.857434Z" + "iopub.execute_input": "2024-05-03T22:19:28.946444Z", + "iopub.status.busy": "2024-05-03T22:19:28.946230Z", + "iopub.status.idle": "2024-05-03T22:19:28.950639Z", + "shell.execute_reply": "2024-05-03T22:19:28.950052Z" }, "id": "0rXP3ZPWjruW" }, @@ -1742,10 +1742,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:23.859781Z", - "iopub.status.busy": "2024-05-02T13:36:23.859612Z", - "iopub.status.idle": "2024-05-02T13:36:23.863892Z", - "shell.execute_reply": "2024-05-02T13:36:23.863430Z" + "iopub.execute_input": "2024-05-03T22:19:28.953043Z", + "iopub.status.busy": "2024-05-03T22:19:28.952690Z", + "iopub.status.idle": "2024-05-03T22:19:28.957019Z", + "shell.execute_reply": "2024-05-03T22:19:28.956502Z" }, "id": "-iRPe8KXjruW" }, @@ -1800,10 +1800,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:23.865717Z", - "iopub.status.busy": "2024-05-02T13:36:23.865547Z", - "iopub.status.idle": "2024-05-02T13:36:23.903395Z", - "shell.execute_reply": "2024-05-02T13:36:23.902843Z" + "iopub.execute_input": "2024-05-03T22:19:28.959348Z", + "iopub.status.busy": "2024-05-03T22:19:28.958987Z", + "iopub.status.idle": "2024-05-03T22:19:28.998889Z", + "shell.execute_reply": "2024-05-03T22:19:28.998382Z" }, "id": "ZpipUliyjruW" }, @@ -1854,10 +1854,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:23.905478Z", - "iopub.status.busy": "2024-05-02T13:36:23.905143Z", - "iopub.status.idle": "2024-05-02T13:36:23.947849Z", - "shell.execute_reply": "2024-05-02T13:36:23.947281Z" + "iopub.execute_input": "2024-05-03T22:19:29.001159Z", + "iopub.status.busy": "2024-05-03T22:19:29.000797Z", + "iopub.status.idle": "2024-05-03T22:19:29.046238Z", + "shell.execute_reply": "2024-05-03T22:19:29.045646Z" }, "id": "SLq-3q4xjruX" }, @@ -1926,10 +1926,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:23.949870Z", - "iopub.status.busy": "2024-05-02T13:36:23.949694Z", - "iopub.status.idle": "2024-05-02T13:36:24.041914Z", - "shell.execute_reply": "2024-05-02T13:36:24.041234Z" + "iopub.execute_input": "2024-05-03T22:19:29.048601Z", + "iopub.status.busy": "2024-05-03T22:19:29.048233Z", + "iopub.status.idle": "2024-05-03T22:19:29.177741Z", + "shell.execute_reply": "2024-05-03T22:19:29.177039Z" }, "id": "g5LHhhuqFbXK" }, @@ -1961,10 +1961,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:24.044396Z", - "iopub.status.busy": "2024-05-02T13:36:24.044212Z", - "iopub.status.idle": "2024-05-02T13:36:24.131406Z", - "shell.execute_reply": "2024-05-02T13:36:24.130779Z" + "iopub.execute_input": "2024-05-03T22:19:29.180557Z", + "iopub.status.busy": "2024-05-03T22:19:29.180130Z", + "iopub.status.idle": "2024-05-03T22:19:29.276547Z", + "shell.execute_reply": "2024-05-03T22:19:29.275894Z" }, "id": "p7w8F8ezBcet" }, @@ -2021,10 +2021,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:24.133971Z", - "iopub.status.busy": "2024-05-02T13:36:24.133729Z", - "iopub.status.idle": "2024-05-02T13:36:24.344137Z", - "shell.execute_reply": "2024-05-02T13:36:24.343671Z" + "iopub.execute_input": "2024-05-03T22:19:29.279105Z", + "iopub.status.busy": "2024-05-03T22:19:29.278640Z", + "iopub.status.idle": "2024-05-03T22:19:29.491295Z", + "shell.execute_reply": "2024-05-03T22:19:29.490718Z" }, "id": "WETRL74tE_sU" }, @@ -2059,10 +2059,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:24.346181Z", - "iopub.status.busy": "2024-05-02T13:36:24.345982Z", - "iopub.status.idle": "2024-05-02T13:36:24.504052Z", - "shell.execute_reply": "2024-05-02T13:36:24.503461Z" + "iopub.execute_input": "2024-05-03T22:19:29.493583Z", + "iopub.status.busy": "2024-05-03T22:19:29.493249Z", + "iopub.status.idle": "2024-05-03T22:19:29.694090Z", + "shell.execute_reply": "2024-05-03T22:19:29.693575Z" }, "id": "kCfdx2gOLmXS" }, @@ -2224,10 +2224,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:24.506248Z", - "iopub.status.busy": "2024-05-02T13:36:24.506027Z", - "iopub.status.idle": "2024-05-02T13:36:24.512493Z", - "shell.execute_reply": "2024-05-02T13:36:24.512084Z" + "iopub.execute_input": "2024-05-03T22:19:29.696465Z", + "iopub.status.busy": "2024-05-03T22:19:29.696095Z", + "iopub.status.idle": "2024-05-03T22:19:29.702623Z", + "shell.execute_reply": "2024-05-03T22:19:29.702164Z" }, "id": "-uogYRWFYnuu" }, @@ -2281,10 +2281,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:24.514380Z", - "iopub.status.busy": "2024-05-02T13:36:24.514206Z", - "iopub.status.idle": "2024-05-02T13:36:24.724430Z", - "shell.execute_reply": "2024-05-02T13:36:24.723873Z" + "iopub.execute_input": "2024-05-03T22:19:29.704623Z", + "iopub.status.busy": "2024-05-03T22:19:29.704442Z", + "iopub.status.idle": "2024-05-03T22:19:29.925663Z", + "shell.execute_reply": "2024-05-03T22:19:29.925083Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2331,10 +2331,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:24.726409Z", - "iopub.status.busy": "2024-05-02T13:36:24.726237Z", - "iopub.status.idle": "2024-05-02T13:36:25.766200Z", - "shell.execute_reply": "2024-05-02T13:36:25.765597Z" + "iopub.execute_input": "2024-05-03T22:19:29.928101Z", + "iopub.status.busy": "2024-05-03T22:19:29.927654Z", + "iopub.status.idle": "2024-05-03T22:19:30.994814Z", + "shell.execute_reply": "2024-05-03T22:19:30.994167Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index 42caa0563..c2b6abb31 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:29.082951Z", - "iopub.status.busy": "2024-05-02T13:36:29.082791Z", - "iopub.status.idle": "2024-05-02T13:36:30.155007Z", - "shell.execute_reply": "2024-05-02T13:36:30.154400Z" + "iopub.execute_input": "2024-05-03T22:19:34.702387Z", + "iopub.status.busy": "2024-05-03T22:19:34.702212Z", + "iopub.status.idle": "2024-05-03T22:19:35.881530Z", + "shell.execute_reply": "2024-05-03T22:19:35.880968Z" }, "nbsphinx": "hidden" }, @@ -101,7 +101,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:30.157586Z", - "iopub.status.busy": "2024-05-02T13:36:30.157321Z", - "iopub.status.idle": "2024-05-02T13:36:30.160318Z", - "shell.execute_reply": "2024-05-02T13:36:30.159888Z" + "iopub.execute_input": "2024-05-03T22:19:35.884020Z", + "iopub.status.busy": "2024-05-03T22:19:35.883707Z", + "iopub.status.idle": "2024-05-03T22:19:35.886885Z", + "shell.execute_reply": "2024-05-03T22:19:35.886419Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:30.162381Z", - "iopub.status.busy": "2024-05-02T13:36:30.162209Z", - "iopub.status.idle": "2024-05-02T13:36:30.169630Z", - "shell.execute_reply": "2024-05-02T13:36:30.169193Z" + "iopub.execute_input": "2024-05-03T22:19:35.889106Z", + "iopub.status.busy": "2024-05-03T22:19:35.888779Z", + "iopub.status.idle": "2024-05-03T22:19:35.896526Z", + "shell.execute_reply": "2024-05-03T22:19:35.896060Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:30.171688Z", - "iopub.status.busy": "2024-05-02T13:36:30.171246Z", - "iopub.status.idle": "2024-05-02T13:36:30.222459Z", - "shell.execute_reply": "2024-05-02T13:36:30.221895Z" + "iopub.execute_input": "2024-05-03T22:19:35.898451Z", + "iopub.status.busy": "2024-05-03T22:19:35.898188Z", + "iopub.status.idle": "2024-05-03T22:19:35.946045Z", + "shell.execute_reply": "2024-05-03T22:19:35.945413Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:30.224658Z", - "iopub.status.busy": "2024-05-02T13:36:30.224391Z", - "iopub.status.idle": "2024-05-02T13:36:30.240758Z", - "shell.execute_reply": "2024-05-02T13:36:30.240331Z" + "iopub.execute_input": "2024-05-03T22:19:35.949007Z", + "iopub.status.busy": "2024-05-03T22:19:35.948534Z", + "iopub.status.idle": "2024-05-03T22:19:35.966043Z", + "shell.execute_reply": "2024-05-03T22:19:35.965461Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:30.242817Z", - "iopub.status.busy": "2024-05-02T13:36:30.242504Z", - "iopub.status.idle": "2024-05-02T13:36:30.246151Z", - "shell.execute_reply": "2024-05-02T13:36:30.245645Z" + "iopub.execute_input": "2024-05-03T22:19:35.968449Z", + "iopub.status.busy": "2024-05-03T22:19:35.968084Z", + "iopub.status.idle": "2024-05-03T22:19:35.972464Z", + "shell.execute_reply": "2024-05-03T22:19:35.971968Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:30.248171Z", - "iopub.status.busy": "2024-05-02T13:36:30.247919Z", - "iopub.status.idle": "2024-05-02T13:36:30.275796Z", - "shell.execute_reply": "2024-05-02T13:36:30.275382Z" + "iopub.execute_input": "2024-05-03T22:19:35.974705Z", + "iopub.status.busy": "2024-05-03T22:19:35.974318Z", + "iopub.status.idle": "2024-05-03T22:19:36.002803Z", + "shell.execute_reply": "2024-05-03T22:19:36.002286Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:30.277880Z", - "iopub.status.busy": "2024-05-02T13:36:30.277566Z", - "iopub.status.idle": "2024-05-02T13:36:30.303710Z", - "shell.execute_reply": "2024-05-02T13:36:30.303297Z" + "iopub.execute_input": "2024-05-03T22:19:36.005429Z", + "iopub.status.busy": "2024-05-03T22:19:36.005053Z", + "iopub.status.idle": "2024-05-03T22:19:36.032937Z", + "shell.execute_reply": "2024-05-03T22:19:36.032401Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:30.305712Z", - "iopub.status.busy": "2024-05-02T13:36:30.305461Z", - "iopub.status.idle": "2024-05-02T13:36:31.964467Z", - "shell.execute_reply": "2024-05-02T13:36:31.963916Z" + "iopub.execute_input": "2024-05-03T22:19:36.035492Z", + "iopub.status.busy": "2024-05-03T22:19:36.035101Z", + "iopub.status.idle": "2024-05-03T22:19:37.888002Z", + "shell.execute_reply": "2024-05-03T22:19:37.887422Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:31.967014Z", - "iopub.status.busy": "2024-05-02T13:36:31.966572Z", - "iopub.status.idle": "2024-05-02T13:36:31.973204Z", - "shell.execute_reply": "2024-05-02T13:36:31.972653Z" + "iopub.execute_input": "2024-05-03T22:19:37.890783Z", + "iopub.status.busy": "2024-05-03T22:19:37.890273Z", + "iopub.status.idle": "2024-05-03T22:19:37.897153Z", + "shell.execute_reply": "2024-05-03T22:19:37.896653Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:31.975482Z", - "iopub.status.busy": "2024-05-02T13:36:31.974960Z", - "iopub.status.idle": "2024-05-02T13:36:31.987465Z", - "shell.execute_reply": "2024-05-02T13:36:31.987039Z" + "iopub.execute_input": "2024-05-03T22:19:37.899314Z", + "iopub.status.busy": "2024-05-03T22:19:37.898979Z", + "iopub.status.idle": "2024-05-03T22:19:37.911963Z", + "shell.execute_reply": "2024-05-03T22:19:37.911488Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:31.989359Z", - "iopub.status.busy": "2024-05-02T13:36:31.989057Z", - "iopub.status.idle": "2024-05-02T13:36:31.995381Z", - "shell.execute_reply": "2024-05-02T13:36:31.994934Z" + "iopub.execute_input": "2024-05-03T22:19:37.914201Z", + "iopub.status.busy": "2024-05-03T22:19:37.913862Z", + "iopub.status.idle": "2024-05-03T22:19:37.920421Z", + "shell.execute_reply": "2024-05-03T22:19:37.919890Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:31.997421Z", - "iopub.status.busy": "2024-05-02T13:36:31.997098Z", - "iopub.status.idle": "2024-05-02T13:36:31.999758Z", - "shell.execute_reply": "2024-05-02T13:36:31.999316Z" + "iopub.execute_input": "2024-05-03T22:19:37.922672Z", + "iopub.status.busy": "2024-05-03T22:19:37.922327Z", + "iopub.status.idle": "2024-05-03T22:19:37.925015Z", + "shell.execute_reply": "2024-05-03T22:19:37.924564Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:32.001730Z", - "iopub.status.busy": "2024-05-02T13:36:32.001419Z", - "iopub.status.idle": "2024-05-02T13:36:32.004755Z", - "shell.execute_reply": "2024-05-02T13:36:32.004254Z" + "iopub.execute_input": "2024-05-03T22:19:37.927118Z", + "iopub.status.busy": "2024-05-03T22:19:37.926800Z", + "iopub.status.idle": "2024-05-03T22:19:37.930332Z", + "shell.execute_reply": "2024-05-03T22:19:37.929815Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:32.006858Z", - "iopub.status.busy": "2024-05-02T13:36:32.006546Z", - "iopub.status.idle": "2024-05-02T13:36:32.009014Z", - "shell.execute_reply": "2024-05-02T13:36:32.008591Z" + "iopub.execute_input": "2024-05-03T22:19:37.932451Z", + "iopub.status.busy": "2024-05-03T22:19:37.932131Z", + "iopub.status.idle": "2024-05-03T22:19:37.934862Z", + "shell.execute_reply": "2024-05-03T22:19:37.934400Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:32.010947Z", - "iopub.status.busy": "2024-05-02T13:36:32.010640Z", - "iopub.status.idle": "2024-05-02T13:36:32.014748Z", - "shell.execute_reply": "2024-05-02T13:36:32.014209Z" + "iopub.execute_input": "2024-05-03T22:19:37.936907Z", + "iopub.status.busy": "2024-05-03T22:19:37.936585Z", + "iopub.status.idle": "2024-05-03T22:19:37.940874Z", + "shell.execute_reply": "2024-05-03T22:19:37.940413Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:32.016779Z", - "iopub.status.busy": "2024-05-02T13:36:32.016461Z", - "iopub.status.idle": "2024-05-02T13:36:32.044499Z", - "shell.execute_reply": "2024-05-02T13:36:32.044083Z" + "iopub.execute_input": "2024-05-03T22:19:37.942998Z", + "iopub.status.busy": "2024-05-03T22:19:37.942696Z", + "iopub.status.idle": "2024-05-03T22:19:37.972803Z", + "shell.execute_reply": "2024-05-03T22:19:37.972148Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:32.046470Z", - "iopub.status.busy": "2024-05-02T13:36:32.046149Z", - "iopub.status.idle": "2024-05-02T13:36:32.050403Z", - "shell.execute_reply": "2024-05-02T13:36:32.049965Z" + "iopub.execute_input": "2024-05-03T22:19:37.975429Z", + "iopub.status.busy": "2024-05-03T22:19:37.975034Z", + "iopub.status.idle": "2024-05-03T22:19:37.979976Z", + "shell.execute_reply": "2024-05-03T22:19:37.979532Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index 68699cd66..443caf0c0 100644 --- a/master/tutorials/multilabel_classification.ipynb +++ b/master/tutorials/multilabel_classification.ipynb @@ -64,10 +64,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:34.743170Z", - "iopub.status.busy": "2024-05-02T13:36:34.742710Z", - "iopub.status.idle": "2024-05-02T13:36:35.858397Z", - "shell.execute_reply": "2024-05-02T13:36:35.857798Z" + "iopub.execute_input": "2024-05-03T22:19:40.777858Z", + "iopub.status.busy": "2024-05-03T22:19:40.777685Z", + "iopub.status.idle": "2024-05-03T22:19:41.969311Z", + "shell.execute_reply": "2024-05-03T22:19:41.968706Z" }, "nbsphinx": "hidden" }, @@ -79,7 +79,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -105,10 +105,10 @@ "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:35.860964Z", - "iopub.status.busy": "2024-05-02T13:36:35.860730Z", - "iopub.status.idle": "2024-05-02T13:36:36.048660Z", - "shell.execute_reply": "2024-05-02T13:36:36.048212Z" + "iopub.execute_input": "2024-05-03T22:19:41.972099Z", + "iopub.status.busy": "2024-05-03T22:19:41.971631Z", + "iopub.status.idle": "2024-05-03T22:19:42.177815Z", + "shell.execute_reply": "2024-05-03T22:19:42.177228Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:36.051095Z", - "iopub.status.busy": "2024-05-02T13:36:36.050717Z", - "iopub.status.idle": "2024-05-02T13:36:36.063514Z", - "shell.execute_reply": "2024-05-02T13:36:36.062962Z" + "iopub.execute_input": "2024-05-03T22:19:42.180792Z", + "iopub.status.busy": "2024-05-03T22:19:42.180236Z", + "iopub.status.idle": "2024-05-03T22:19:42.193344Z", + "shell.execute_reply": "2024-05-03T22:19:42.192775Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:36.065445Z", - "iopub.status.busy": "2024-05-02T13:36:36.065139Z", - "iopub.status.idle": "2024-05-02T13:36:38.651951Z", - "shell.execute_reply": "2024-05-02T13:36:38.651304Z" + "iopub.execute_input": "2024-05-03T22:19:42.195563Z", + "iopub.status.busy": "2024-05-03T22:19:42.195284Z", + "iopub.status.idle": "2024-05-03T22:19:44.910593Z", + "shell.execute_reply": "2024-05-03T22:19:44.909959Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:38.654079Z", - "iopub.status.busy": "2024-05-02T13:36:38.653891Z", - "iopub.status.idle": "2024-05-02T13:36:39.973292Z", - "shell.execute_reply": "2024-05-02T13:36:39.972745Z" + "iopub.execute_input": "2024-05-03T22:19:44.913028Z", + "iopub.status.busy": "2024-05-03T22:19:44.912632Z", + "iopub.status.idle": "2024-05-03T22:19:46.271016Z", + "shell.execute_reply": "2024-05-03T22:19:46.270412Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:39.975705Z", - "iopub.status.busy": "2024-05-02T13:36:39.975198Z", - "iopub.status.idle": "2024-05-02T13:36:39.979273Z", - "shell.execute_reply": "2024-05-02T13:36:39.978818Z" + "iopub.execute_input": "2024-05-03T22:19:46.273709Z", + "iopub.status.busy": "2024-05-03T22:19:46.273339Z", + "iopub.status.idle": "2024-05-03T22:19:46.277188Z", + "shell.execute_reply": "2024-05-03T22:19:46.276615Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:39.981261Z", - "iopub.status.busy": "2024-05-02T13:36:39.980961Z", - "iopub.status.idle": "2024-05-02T13:36:41.664453Z", - "shell.execute_reply": "2024-05-02T13:36:41.663902Z" + "iopub.execute_input": "2024-05-03T22:19:46.279331Z", + "iopub.status.busy": "2024-05-03T22:19:46.278922Z", + "iopub.status.idle": "2024-05-03T22:19:48.183448Z", + "shell.execute_reply": "2024-05-03T22:19:48.182756Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:41.667004Z", - "iopub.status.busy": "2024-05-02T13:36:41.666583Z", - "iopub.status.idle": "2024-05-02T13:36:41.674063Z", - "shell.execute_reply": "2024-05-02T13:36:41.673550Z" + "iopub.execute_input": "2024-05-03T22:19:48.185901Z", + "iopub.status.busy": "2024-05-03T22:19:48.185464Z", + "iopub.status.idle": "2024-05-03T22:19:48.194394Z", + "shell.execute_reply": "2024-05-03T22:19:48.193903Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:41.676072Z", - "iopub.status.busy": "2024-05-02T13:36:41.675749Z", - "iopub.status.idle": "2024-05-02T13:36:44.192611Z", - "shell.execute_reply": "2024-05-02T13:36:44.192085Z" + "iopub.execute_input": "2024-05-03T22:19:48.196754Z", + "iopub.status.busy": "2024-05-03T22:19:48.196317Z", + "iopub.status.idle": "2024-05-03T22:19:50.824127Z", + "shell.execute_reply": "2024-05-03T22:19:50.823594Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:44.194825Z", - "iopub.status.busy": "2024-05-02T13:36:44.194481Z", - "iopub.status.idle": "2024-05-02T13:36:44.198019Z", - "shell.execute_reply": "2024-05-02T13:36:44.197544Z" + "iopub.execute_input": "2024-05-03T22:19:50.826368Z", + "iopub.status.busy": "2024-05-03T22:19:50.826027Z", + "iopub.status.idle": "2024-05-03T22:19:50.829861Z", + "shell.execute_reply": "2024-05-03T22:19:50.829308Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:44.200056Z", - "iopub.status.busy": "2024-05-02T13:36:44.199753Z", - "iopub.status.idle": "2024-05-02T13:36:44.203246Z", - "shell.execute_reply": "2024-05-02T13:36:44.202802Z" + "iopub.execute_input": "2024-05-03T22:19:50.831982Z", + "iopub.status.busy": "2024-05-03T22:19:50.831669Z", + "iopub.status.idle": "2024-05-03T22:19:50.835425Z", + "shell.execute_reply": "2024-05-03T22:19:50.834842Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:44.205369Z", - "iopub.status.busy": "2024-05-02T13:36:44.204945Z", - "iopub.status.idle": "2024-05-02T13:36:44.208384Z", - "shell.execute_reply": "2024-05-02T13:36:44.207938Z" + "iopub.execute_input": "2024-05-03T22:19:50.837747Z", + "iopub.status.busy": "2024-05-03T22:19:50.837292Z", + "iopub.status.idle": "2024-05-03T22:19:50.840742Z", + "shell.execute_reply": "2024-05-03T22:19:50.840148Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index 7e0ba987c..0e4351328 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:46.490371Z", - "iopub.status.busy": "2024-05-02T13:36:46.489940Z", - "iopub.status.idle": "2024-05-02T13:36:47.606767Z", - "shell.execute_reply": "2024-05-02T13:36:47.606165Z" + "iopub.execute_input": "2024-05-03T22:19:53.386145Z", + "iopub.status.busy": "2024-05-03T22:19:53.385852Z", + "iopub.status.idle": "2024-05-03T22:19:54.629141Z", + "shell.execute_reply": "2024-05-03T22:19:54.628444Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -109,10 +109,10 @@ "id": "c90449c8", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:47.609364Z", - "iopub.status.busy": "2024-05-02T13:36:47.608951Z", - "iopub.status.idle": "2024-05-02T13:36:48.724475Z", - "shell.execute_reply": "2024-05-02T13:36:48.723828Z" + "iopub.execute_input": "2024-05-03T22:19:54.631719Z", + "iopub.status.busy": "2024-05-03T22:19:54.631402Z", + "iopub.status.idle": "2024-05-03T22:19:55.595584Z", + "shell.execute_reply": "2024-05-03T22:19:55.594885Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:48.726957Z", - "iopub.status.busy": "2024-05-02T13:36:48.726584Z", - "iopub.status.idle": "2024-05-02T13:36:48.729582Z", - "shell.execute_reply": "2024-05-02T13:36:48.729150Z" + "iopub.execute_input": "2024-05-03T22:19:55.598432Z", + "iopub.status.busy": "2024-05-03T22:19:55.598001Z", + "iopub.status.idle": "2024-05-03T22:19:55.601431Z", + "shell.execute_reply": "2024-05-03T22:19:55.600967Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:48.731563Z", - "iopub.status.busy": "2024-05-02T13:36:48.731232Z", - "iopub.status.idle": "2024-05-02T13:36:48.737841Z", - "shell.execute_reply": "2024-05-02T13:36:48.737333Z" + "iopub.execute_input": "2024-05-03T22:19:55.603576Z", + "iopub.status.busy": "2024-05-03T22:19:55.603220Z", + "iopub.status.idle": "2024-05-03T22:19:55.610681Z", + "shell.execute_reply": "2024-05-03T22:19:55.610169Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:48.739973Z", - "iopub.status.busy": "2024-05-02T13:36:48.739790Z", - "iopub.status.idle": "2024-05-02T13:36:49.221678Z", - "shell.execute_reply": "2024-05-02T13:36:49.221140Z" + "iopub.execute_input": "2024-05-03T22:19:55.613161Z", + "iopub.status.busy": "2024-05-03T22:19:55.612798Z", + "iopub.status.idle": "2024-05-03T22:19:56.115462Z", + "shell.execute_reply": "2024-05-03T22:19:56.114850Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:49.224137Z", - "iopub.status.busy": "2024-05-02T13:36:49.223951Z", - "iopub.status.idle": "2024-05-02T13:36:49.229180Z", - "shell.execute_reply": "2024-05-02T13:36:49.228631Z" + "iopub.execute_input": "2024-05-03T22:19:56.118471Z", + "iopub.status.busy": "2024-05-03T22:19:56.118104Z", + "iopub.status.idle": "2024-05-03T22:19:56.123646Z", + "shell.execute_reply": "2024-05-03T22:19:56.123077Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:49.231247Z", - "iopub.status.busy": "2024-05-02T13:36:49.230876Z", - "iopub.status.idle": "2024-05-02T13:36:49.235048Z", - "shell.execute_reply": "2024-05-02T13:36:49.234504Z" + "iopub.execute_input": "2024-05-03T22:19:56.125789Z", + "iopub.status.busy": "2024-05-03T22:19:56.125595Z", + "iopub.status.idle": "2024-05-03T22:19:56.130027Z", + "shell.execute_reply": "2024-05-03T22:19:56.129442Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:49.237142Z", - "iopub.status.busy": "2024-05-02T13:36:49.236752Z", - "iopub.status.idle": "2024-05-02T13:36:49.906512Z", - "shell.execute_reply": "2024-05-02T13:36:49.905963Z" + "iopub.execute_input": "2024-05-03T22:19:56.132407Z", + "iopub.status.busy": "2024-05-03T22:19:56.131987Z", + "iopub.status.idle": "2024-05-03T22:19:57.018056Z", + "shell.execute_reply": "2024-05-03T22:19:57.017391Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:49.909072Z", - "iopub.status.busy": "2024-05-02T13:36:49.908591Z", - "iopub.status.idle": "2024-05-02T13:36:50.074366Z", - "shell.execute_reply": "2024-05-02T13:36:50.073905Z" + "iopub.execute_input": "2024-05-03T22:19:57.020760Z", + "iopub.status.busy": "2024-05-03T22:19:57.020286Z", + "iopub.status.idle": "2024-05-03T22:19:57.317293Z", + "shell.execute_reply": "2024-05-03T22:19:57.316682Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:50.076221Z", - "iopub.status.busy": "2024-05-02T13:36:50.076049Z", - "iopub.status.idle": "2024-05-02T13:36:50.080344Z", - "shell.execute_reply": "2024-05-02T13:36:50.079907Z" + "iopub.execute_input": "2024-05-03T22:19:57.319615Z", + "iopub.status.busy": "2024-05-03T22:19:57.319422Z", + "iopub.status.idle": "2024-05-03T22:19:57.324280Z", + "shell.execute_reply": "2024-05-03T22:19:57.323813Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:50.082361Z", - "iopub.status.busy": "2024-05-02T13:36:50.081939Z", - "iopub.status.idle": "2024-05-02T13:36:50.521896Z", - "shell.execute_reply": "2024-05-02T13:36:50.521284Z" + "iopub.execute_input": "2024-05-03T22:19:57.326563Z", + "iopub.status.busy": "2024-05-03T22:19:57.326163Z", + "iopub.status.idle": "2024-05-03T22:19:57.790000Z", + "shell.execute_reply": "2024-05-03T22:19:57.789406Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:50.524440Z", - "iopub.status.busy": "2024-05-02T13:36:50.524016Z", - "iopub.status.idle": "2024-05-02T13:36:50.853407Z", - "shell.execute_reply": "2024-05-02T13:36:50.852892Z" + "iopub.execute_input": "2024-05-03T22:19:57.792617Z", + "iopub.status.busy": "2024-05-03T22:19:57.792164Z", + "iopub.status.idle": "2024-05-03T22:19:58.126696Z", + "shell.execute_reply": "2024-05-03T22:19:58.126086Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:50.855883Z", - "iopub.status.busy": "2024-05-02T13:36:50.855692Z", - "iopub.status.idle": "2024-05-02T13:36:51.186221Z", - "shell.execute_reply": "2024-05-02T13:36:51.185615Z" + "iopub.execute_input": "2024-05-03T22:19:58.129230Z", + "iopub.status.busy": "2024-05-03T22:19:58.128886Z", + "iopub.status.idle": "2024-05-03T22:19:58.494502Z", + "shell.execute_reply": "2024-05-03T22:19:58.493877Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:51.189094Z", - "iopub.status.busy": "2024-05-02T13:36:51.188769Z", - "iopub.status.idle": "2024-05-02T13:36:51.604267Z", - "shell.execute_reply": "2024-05-02T13:36:51.603688Z" + "iopub.execute_input": "2024-05-03T22:19:58.497799Z", + "iopub.status.busy": "2024-05-03T22:19:58.497402Z", + "iopub.status.idle": "2024-05-03T22:19:58.944543Z", + "shell.execute_reply": "2024-05-03T22:19:58.943931Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:51.608298Z", - "iopub.status.busy": "2024-05-02T13:36:51.607881Z", - "iopub.status.idle": "2024-05-02T13:36:52.052673Z", - "shell.execute_reply": "2024-05-02T13:36:52.052099Z" + "iopub.execute_input": "2024-05-03T22:19:58.948805Z", + "iopub.status.busy": "2024-05-03T22:19:58.948406Z", + "iopub.status.idle": "2024-05-03T22:19:59.406414Z", + "shell.execute_reply": "2024-05-03T22:19:59.405781Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:52.055364Z", - "iopub.status.busy": "2024-05-02T13:36:52.055180Z", - "iopub.status.idle": "2024-05-02T13:36:52.268258Z", - "shell.execute_reply": "2024-05-02T13:36:52.267710Z" + "iopub.execute_input": "2024-05-03T22:19:59.409810Z", + "iopub.status.busy": "2024-05-03T22:19:59.409434Z", + "iopub.status.idle": "2024-05-03T22:19:59.632557Z", + "shell.execute_reply": "2024-05-03T22:19:59.631966Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:52.270337Z", - "iopub.status.busy": "2024-05-02T13:36:52.270155Z", - "iopub.status.idle": "2024-05-02T13:36:52.450557Z", - "shell.execute_reply": "2024-05-02T13:36:52.449952Z" + "iopub.execute_input": "2024-05-03T22:19:59.634616Z", + "iopub.status.busy": "2024-05-03T22:19:59.634433Z", + "iopub.status.idle": "2024-05-03T22:19:59.818687Z", + "shell.execute_reply": "2024-05-03T22:19:59.818144Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:52.453372Z", - "iopub.status.busy": "2024-05-02T13:36:52.452969Z", - "iopub.status.idle": "2024-05-02T13:36:52.456400Z", - "shell.execute_reply": "2024-05-02T13:36:52.455934Z" + "iopub.execute_input": "2024-05-03T22:19:59.821321Z", + "iopub.status.busy": "2024-05-03T22:19:59.820996Z", + "iopub.status.idle": "2024-05-03T22:19:59.824043Z", + "shell.execute_reply": "2024-05-03T22:19:59.823484Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:52.458352Z", - "iopub.status.busy": "2024-05-02T13:36:52.458031Z", - "iopub.status.idle": "2024-05-02T13:36:53.360646Z", - "shell.execute_reply": "2024-05-02T13:36:53.360143Z" + "iopub.execute_input": "2024-05-03T22:19:59.826097Z", + "iopub.status.busy": "2024-05-03T22:19:59.825763Z", + "iopub.status.idle": "2024-05-03T22:20:00.739334Z", + "shell.execute_reply": "2024-05-03T22:20:00.738782Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:53.363249Z", - "iopub.status.busy": "2024-05-02T13:36:53.362917Z", - "iopub.status.idle": "2024-05-02T13:36:53.462904Z", - "shell.execute_reply": "2024-05-02T13:36:53.462386Z" + "iopub.execute_input": "2024-05-03T22:20:00.742156Z", + "iopub.status.busy": "2024-05-03T22:20:00.741821Z", + "iopub.status.idle": "2024-05-03T22:20:00.881567Z", + "shell.execute_reply": "2024-05-03T22:20:00.880972Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:53.465197Z", - "iopub.status.busy": "2024-05-02T13:36:53.464795Z", - "iopub.status.idle": "2024-05-02T13:36:53.590425Z", - "shell.execute_reply": "2024-05-02T13:36:53.589883Z" + "iopub.execute_input": "2024-05-03T22:20:00.883974Z", + "iopub.status.busy": "2024-05-03T22:20:00.883588Z", + "iopub.status.idle": "2024-05-03T22:20:01.028874Z", + "shell.execute_reply": "2024-05-03T22:20:01.028241Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:53.592696Z", - "iopub.status.busy": "2024-05-02T13:36:53.592317Z", - "iopub.status.idle": "2024-05-02T13:36:54.251258Z", - "shell.execute_reply": "2024-05-02T13:36:54.250800Z" + "iopub.execute_input": "2024-05-03T22:20:01.031353Z", + "iopub.status.busy": "2024-05-03T22:20:01.030985Z", + "iopub.status.idle": "2024-05-03T22:20:01.757491Z", + "shell.execute_reply": "2024-05-03T22:20:01.756902Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:54.253425Z", - "iopub.status.busy": "2024-05-02T13:36:54.253122Z", - "iopub.status.idle": "2024-05-02T13:36:54.256657Z", - "shell.execute_reply": "2024-05-02T13:36:54.256211Z" + "iopub.execute_input": "2024-05-03T22:20:01.759901Z", + "iopub.status.busy": "2024-05-03T22:20:01.759536Z", + "iopub.status.idle": "2024-05-03T22:20:01.763213Z", + "shell.execute_reply": "2024-05-03T22:20:01.762783Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index 1ecfb5959..2b394b006 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -746,7 +746,7 @@

2. Pre-process the Cifar10 dataset 
-100%|██████████| 170498071/170498071 [00:03<00:00, 56730580.89it/s]
+100%|██████████| 170498071/170498071 [00:03<00:00, 48343794.19it/s]
@@ -876,7 +876,7 @@

3. Use cleanlab and feature embeddings to find outliers in the data

-
+
@@ -1090,7 +1090,7 @@

4. Use cleanlab and here.

diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index bb18682e7..b06b1ffdb 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:56.448299Z", - "iopub.status.busy": "2024-05-02T13:36:56.448130Z", - "iopub.status.idle": "2024-05-02T13:36:59.064651Z", - "shell.execute_reply": "2024-05-02T13:36:59.064124Z" + "iopub.execute_input": "2024-05-03T22:20:04.046894Z", + "iopub.status.busy": "2024-05-03T22:20:04.046717Z", + "iopub.status.idle": "2024-05-03T22:20:06.957423Z", + "shell.execute_reply": "2024-05-03T22:20:06.956836Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "id": "4396f544", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:59.067352Z", - "iopub.status.busy": "2024-05-02T13:36:59.066810Z", - "iopub.status.idle": "2024-05-02T13:36:59.369727Z", - "shell.execute_reply": "2024-05-02T13:36:59.369194Z" + "iopub.execute_input": "2024-05-03T22:20:06.960089Z", + "iopub.status.busy": "2024-05-03T22:20:06.959699Z", + "iopub.status.idle": "2024-05-03T22:20:07.303685Z", + "shell.execute_reply": "2024-05-03T22:20:07.303127Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:59.372344Z", - "iopub.status.busy": "2024-05-02T13:36:59.371803Z", - "iopub.status.idle": "2024-05-02T13:36:59.375981Z", - "shell.execute_reply": "2024-05-02T13:36:59.375460Z" + "iopub.execute_input": "2024-05-03T22:20:07.306550Z", + "iopub.status.busy": "2024-05-03T22:20:07.305875Z", + "iopub.status.idle": "2024-05-03T22:20:07.310484Z", + "shell.execute_reply": "2024-05-03T22:20:07.309928Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:36:59.378081Z", - "iopub.status.busy": "2024-05-02T13:36:59.377765Z", - "iopub.status.idle": "2024-05-02T13:37:05.143239Z", - "shell.execute_reply": "2024-05-02T13:37:05.142645Z" + "iopub.execute_input": "2024-05-03T22:20:07.312459Z", + "iopub.status.busy": "2024-05-03T22:20:07.312280Z", + "iopub.status.idle": "2024-05-03T22:20:13.609662Z", + "shell.execute_reply": "2024-05-03T22:20:13.609031Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 491520/170498071 [00:00<00:34, 4891112.38it/s]" + " 1%| | 1179648/170498071 [00:00<00:14, 11620315.05it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 2686976/170498071 [00:00<00:11, 14775958.90it/s]" + " 3%|▎ | 5668864/170498071 [00:00<00:05, 31050768.78it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 5242880/170498071 [00:00<00:08, 19568044.73it/s]" + " 6%|▋ | 10977280/170498071 [00:00<00:03, 40875098.79it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - 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"iopub.execute_input": "2024-05-02T13:37:05.145551Z", - "iopub.status.busy": "2024-05-02T13:37:05.145358Z", - "iopub.status.idle": "2024-05-02T13:37:05.150147Z", - "shell.execute_reply": "2024-05-02T13:37:05.149683Z" + "iopub.execute_input": "2024-05-03T22:20:13.612031Z", + "iopub.status.busy": "2024-05-03T22:20:13.611790Z", + "iopub.status.idle": "2024-05-03T22:20:13.616806Z", + "shell.execute_reply": "2024-05-03T22:20:13.616230Z" }, "nbsphinx": "hidden" }, @@ -656,10 +688,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:05.152088Z", - "iopub.status.busy": "2024-05-02T13:37:05.151769Z", - "iopub.status.idle": "2024-05-02T13:37:05.665171Z", - "shell.execute_reply": "2024-05-02T13:37:05.664622Z" + "iopub.execute_input": "2024-05-03T22:20:13.619644Z", + "iopub.status.busy": "2024-05-03T22:20:13.619251Z", + "iopub.status.idle": "2024-05-03T22:20:14.195173Z", + "shell.execute_reply": "2024-05-03T22:20:14.194627Z" } }, "outputs": [ @@ -692,10 +724,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:05.667517Z", - "iopub.status.busy": "2024-05-02T13:37:05.667085Z", - "iopub.status.idle": "2024-05-02T13:37:06.175687Z", - "shell.execute_reply": "2024-05-02T13:37:06.175184Z" + "iopub.execute_input": "2024-05-03T22:20:14.197510Z", + "iopub.status.busy": "2024-05-03T22:20:14.197148Z", + "iopub.status.idle": "2024-05-03T22:20:14.739422Z", + "shell.execute_reply": "2024-05-03T22:20:14.738840Z" } }, "outputs": [ @@ -733,10 +765,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:06.177884Z", - "iopub.status.busy": "2024-05-02T13:37:06.177552Z", - "iopub.status.idle": "2024-05-02T13:37:06.180979Z", - "shell.execute_reply": "2024-05-02T13:37:06.180523Z" + "iopub.execute_input": "2024-05-03T22:20:14.741850Z", + "iopub.status.busy": "2024-05-03T22:20:14.741482Z", + "iopub.status.idle": "2024-05-03T22:20:14.745572Z", + "shell.execute_reply": "2024-05-03T22:20:14.745131Z" } }, "outputs": [], @@ -759,17 +791,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:06.182972Z", - "iopub.status.busy": "2024-05-02T13:37:06.182565Z", - "iopub.status.idle": "2024-05-02T13:37:18.649497Z", - "shell.execute_reply": "2024-05-02T13:37:18.648901Z" + "iopub.execute_input": "2024-05-03T22:20:14.747484Z", + "iopub.status.busy": "2024-05-03T22:20:14.747282Z", + "iopub.status.idle": "2024-05-03T22:20:27.646179Z", + "shell.execute_reply": "2024-05-03T22:20:27.645545Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4757e446bb20472d86fd78b2fe6d2321", + "model_id": "4f3fc25b54b74ff1aca63e802f46a3da", "version_major": 2, "version_minor": 0 }, @@ -828,10 +860,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:18.651843Z", - "iopub.status.busy": "2024-05-02T13:37:18.651650Z", - "iopub.status.idle": "2024-05-02T13:37:20.378740Z", - "shell.execute_reply": "2024-05-02T13:37:20.378113Z" + "iopub.execute_input": "2024-05-03T22:20:27.648808Z", + "iopub.status.busy": "2024-05-03T22:20:27.648377Z", + "iopub.status.idle": "2024-05-03T22:20:29.466503Z", + "shell.execute_reply": "2024-05-03T22:20:29.465807Z" } }, "outputs": [ @@ -875,10 +907,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:20.381414Z", - "iopub.status.busy": "2024-05-02T13:37:20.381208Z", - "iopub.status.idle": "2024-05-02T13:37:20.634754Z", - "shell.execute_reply": "2024-05-02T13:37:20.633703Z" + "iopub.execute_input": "2024-05-03T22:20:29.469636Z", + "iopub.status.busy": "2024-05-03T22:20:29.469080Z", + "iopub.status.idle": "2024-05-03T22:20:29.737462Z", + "shell.execute_reply": "2024-05-03T22:20:29.736861Z" } }, "outputs": [ @@ -914,10 +946,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:20.637291Z", - "iopub.status.busy": "2024-05-02T13:37:20.636993Z", - "iopub.status.idle": "2024-05-02T13:37:21.302071Z", - "shell.execute_reply": "2024-05-02T13:37:21.301503Z" + "iopub.execute_input": "2024-05-03T22:20:29.740228Z", + "iopub.status.busy": "2024-05-03T22:20:29.739891Z", + "iopub.status.idle": "2024-05-03T22:20:30.450048Z", + "shell.execute_reply": "2024-05-03T22:20:30.449444Z" } }, "outputs": [ @@ -967,10 +999,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:21.305055Z", - "iopub.status.busy": "2024-05-02T13:37:21.304753Z", - "iopub.status.idle": "2024-05-02T13:37:21.644315Z", - "shell.execute_reply": "2024-05-02T13:37:21.643688Z" + "iopub.execute_input": "2024-05-03T22:20:30.453100Z", + "iopub.status.busy": "2024-05-03T22:20:30.452760Z", + "iopub.status.idle": "2024-05-03T22:20:30.805759Z", + "shell.execute_reply": "2024-05-03T22:20:30.805153Z" } }, "outputs": [ @@ -1018,10 +1050,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:21.646466Z", - "iopub.status.busy": "2024-05-02T13:37:21.646295Z", - "iopub.status.idle": "2024-05-02T13:37:21.887961Z", - "shell.execute_reply": "2024-05-02T13:37:21.887345Z" + "iopub.execute_input": "2024-05-03T22:20:30.808440Z", + "iopub.status.busy": "2024-05-03T22:20:30.807933Z", + "iopub.status.idle": "2024-05-03T22:20:31.057047Z", + "shell.execute_reply": "2024-05-03T22:20:31.053824Z" } }, "outputs": [ @@ -1077,10 +1109,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:21.890359Z", - "iopub.status.busy": "2024-05-02T13:37:21.889966Z", - "iopub.status.idle": "2024-05-02T13:37:21.979477Z", - "shell.execute_reply": "2024-05-02T13:37:21.978979Z" + "iopub.execute_input": "2024-05-03T22:20:31.061487Z", + "iopub.status.busy": "2024-05-03T22:20:31.060228Z", + "iopub.status.idle": "2024-05-03T22:20:31.147111Z", + "shell.execute_reply": "2024-05-03T22:20:31.146594Z" } }, "outputs": [], @@ -1101,10 +1133,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:21.981920Z", - "iopub.status.busy": "2024-05-02T13:37:21.981565Z", - "iopub.status.idle": "2024-05-02T13:37:32.265006Z", - "shell.execute_reply": "2024-05-02T13:37:32.264313Z" + "iopub.execute_input": "2024-05-03T22:20:31.149755Z", + "iopub.status.busy": "2024-05-03T22:20:31.149204Z", + "iopub.status.idle": "2024-05-03T22:20:41.830172Z", + "shell.execute_reply": "2024-05-03T22:20:41.829430Z" } }, "outputs": [ @@ -1141,10 +1173,10 @@ "id": "874c885a", "metadata": { "execution": { - 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"iopub.execute_input": "2024-05-02T13:37:38.430277Z", - "iopub.status.busy": "2024-05-02T13:37:38.429864Z", - "iopub.status.idle": "2024-05-02T13:37:39.544158Z", - "shell.execute_reply": "2024-05-02T13:37:39.543678Z" + "iopub.execute_input": "2024-05-03T22:20:48.457300Z", + "iopub.status.busy": "2024-05-03T22:20:48.456794Z", + "iopub.status.idle": "2024-05-03T22:20:49.718031Z", + "shell.execute_reply": "2024-05-03T22:20:49.717462Z" }, "nbsphinx": "hidden" }, @@ -116,7 +116,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -142,10 +142,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:39.546763Z", - "iopub.status.busy": "2024-05-02T13:37:39.546284Z", - "iopub.status.idle": "2024-05-02T13:37:39.564144Z", - "shell.execute_reply": "2024-05-02T13:37:39.563693Z" + "iopub.execute_input": "2024-05-03T22:20:49.720716Z", + "iopub.status.busy": "2024-05-03T22:20:49.720228Z", + "iopub.status.idle": "2024-05-03T22:20:49.739927Z", + "shell.execute_reply": "2024-05-03T22:20:49.739408Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:39.566210Z", - "iopub.status.busy": "2024-05-02T13:37:39.565866Z", - "iopub.status.idle": "2024-05-02T13:37:39.568803Z", - "shell.execute_reply": "2024-05-02T13:37:39.568375Z" + "iopub.execute_input": "2024-05-03T22:20:49.742704Z", + "iopub.status.busy": "2024-05-03T22:20:49.742139Z", + "iopub.status.idle": "2024-05-03T22:20:49.745441Z", + "shell.execute_reply": "2024-05-03T22:20:49.744874Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - 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3. Use cleanlab to find label issues

-
+
-
+
@@ -857,7 +857,7 @@

Classes which are commonly mislabeled overall

-
+
@@ -1106,7 +1106,7 @@

Get label quality scores

-
+

Beyond scoring the overall label quality of each image, the above method produces a (0 to 1) quality score for each pixel. We can apply a thresholding function to these scores in order to extract the same style True or False mask as find_label_issues().

@@ -1162,7 +1162,7 @@

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"_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_7f771dba0cf24e28b606dda5f4d75fb2", "IPY_MODEL_266d6230d3b44c6699a62c30d4f96a99", "IPY_MODEL_034a2244415a41caa33e8ea418a9f6a8"], "layout": "IPY_MODEL_8b9c6c40fde941ec8cb633aeee2cf626", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index 58774a79b..caa3f2c41 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:57.804859Z", - "iopub.status.busy": "2024-05-02T13:37:57.804392Z", - "iopub.status.idle": "2024-05-02T13:37:59.142610Z", - "shell.execute_reply": "2024-05-02T13:37:59.141972Z" + "iopub.execute_input": "2024-05-03T22:21:09.354623Z", + "iopub.status.busy": "2024-05-03T22:21:09.354237Z", + "iopub.status.idle": "2024-05-03T22:21:10.777441Z", + "shell.execute_reply": "2024-05-03T22:21:10.776744Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:37:59.144920Z", - "iopub.status.busy": "2024-05-02T13:37:59.144750Z", - "iopub.status.idle": "2024-05-02T13:38:40.069504Z", - "shell.execute_reply": "2024-05-02T13:38:40.068821Z" + "iopub.execute_input": "2024-05-03T22:21:10.780051Z", + "iopub.status.busy": "2024-05-03T22:21:10.779824Z", + "iopub.status.idle": "2024-05-03T22:21:54.904003Z", + "shell.execute_reply": "2024-05-03T22:21:54.903350Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:38:40.072006Z", - "iopub.status.busy": "2024-05-02T13:38:40.071649Z", - "iopub.status.idle": "2024-05-02T13:38:41.144536Z", - "shell.execute_reply": "2024-05-02T13:38:41.143998Z" + "iopub.execute_input": "2024-05-03T22:21:54.906583Z", + "iopub.status.busy": "2024-05-03T22:21:54.906244Z", + "iopub.status.idle": "2024-05-03T22:21:56.059453Z", + "shell.execute_reply": "2024-05-03T22:21:56.058793Z" }, "nbsphinx": "hidden" }, @@ -111,7 +111,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -137,10 +137,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:38:41.147135Z", - "iopub.status.busy": "2024-05-02T13:38:41.146624Z", - "iopub.status.idle": "2024-05-02T13:38:41.149860Z", - "shell.execute_reply": "2024-05-02T13:38:41.149339Z" + "iopub.execute_input": "2024-05-03T22:21:56.062349Z", + "iopub.status.busy": "2024-05-03T22:21:56.061885Z", + "iopub.status.idle": "2024-05-03T22:21:56.065288Z", + "shell.execute_reply": "2024-05-03T22:21:56.064811Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:38:41.151955Z", - "iopub.status.busy": "2024-05-02T13:38:41.151564Z", - "iopub.status.idle": "2024-05-02T13:38:41.155359Z", - "shell.execute_reply": "2024-05-02T13:38:41.154844Z" + "iopub.execute_input": "2024-05-03T22:21:56.067491Z", + "iopub.status.busy": "2024-05-03T22:21:56.067170Z", + "iopub.status.idle": "2024-05-03T22:21:56.071116Z", + "shell.execute_reply": "2024-05-03T22:21:56.070663Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:38:41.157350Z", - "iopub.status.busy": "2024-05-02T13:38:41.157037Z", - "iopub.status.idle": "2024-05-02T13:38:41.160371Z", - "shell.execute_reply": "2024-05-02T13:38:41.159859Z" + "iopub.execute_input": "2024-05-03T22:21:56.073204Z", + "iopub.status.busy": "2024-05-03T22:21:56.072874Z", + "iopub.status.idle": "2024-05-03T22:21:56.076491Z", + "shell.execute_reply": "2024-05-03T22:21:56.076037Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:38:41.162419Z", - "iopub.status.busy": "2024-05-02T13:38:41.162107Z", - "iopub.status.idle": "2024-05-02T13:38:41.164772Z", - "shell.execute_reply": "2024-05-02T13:38:41.164346Z" + "iopub.execute_input": "2024-05-03T22:21:56.078540Z", + "iopub.status.busy": "2024-05-03T22:21:56.078228Z", + "iopub.status.idle": "2024-05-03T22:21:56.081127Z", + "shell.execute_reply": "2024-05-03T22:21:56.080608Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:38:41.166771Z", - 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"iopub.status.busy": "2024-05-02T13:39:18.789357Z", - "iopub.status.idle": "2024-05-02T13:39:51.060869Z", - "shell.execute_reply": "2024-05-02T13:39:51.060398Z" + "iopub.execute_input": "2024-05-03T22:22:35.380306Z", + "iopub.status.busy": "2024-05-03T22:22:35.379907Z", + "iopub.status.idle": "2024-05-03T22:23:08.297444Z", + "shell.execute_reply": "2024-05-03T22:23:08.296915Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3e925f9a098d4e4ab28b64e9572ec39f", + "model_id": "15b7e5ecfcd9415bb1577c1b5a9c71c6", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:39:51.063093Z", - "iopub.status.busy": "2024-05-02T13:39:51.062765Z", - "iopub.status.idle": "2024-05-02T13:40:05.397590Z", - "shell.execute_reply": "2024-05-02T13:40:05.397039Z" + "iopub.execute_input": "2024-05-03T22:23:08.299784Z", + "iopub.status.busy": "2024-05-03T22:23:08.299426Z", + 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1. Install required dependencies and download data 
---2024-05-02 13:40:18--  https://data.deepai.org/conll2003.zip
-Resolving data.deepai.org (data.deepai.org)... 185.93.1.251, 2400:52e0:1a00::894:1
+--2024-05-03 22:23:37--  https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 185.93.1.251, 2400:52e0:1a00::1068:1
 Connecting to data.deepai.org (data.deepai.org)|185.93.1.251|:443... connected.
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-conll2003.zip       100%[===================>] 959.94K  4.49MB/s    in 0.2s
+conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.1s
 
-2024-05-02 13:40:19 (4.49 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-05-03 22:23:37 (6.53 MB/s) - ‘conll2003.zip’ saved [982975/982975]
 
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@@ -693,16 +693,16 @@ 
1. Install required dependencies and download data

diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index 5890cf2c4..fbe7fe68d 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:18.838890Z", - "iopub.status.busy": "2024-05-02T13:40:18.838736Z", - "iopub.status.idle": "2024-05-02T13:40:20.303449Z", - "shell.execute_reply": "2024-05-02T13:40:20.302883Z" + "iopub.execute_input": "2024-05-03T22:23:37.109560Z", + "iopub.status.busy": "2024-05-03T22:23:37.109386Z", + "iopub.status.idle": "2024-05-03T22:23:38.354224Z", + "shell.execute_reply": "2024-05-03T22:23:38.353645Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-05-02 13:40:18-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-05-03 22:23:37-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,14 +94,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.251, 2400:52e0:1a00::894:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.251|:443... connected.\r\n" + "185.93.1.251, 2400:52e0:1a00::1068:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.251|:443... " ] }, { "name": "stdout", "output_type": "stream", "text": [ + "connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -122,10 +123,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 90%[=================> ] 865.67K 4.10MB/s \r", - "conll2003.zip 100%[===================>] 959.94K 4.49MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-05-02 13:40:19 (4.49 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-05-03 22:23:37 (6.53 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -137,14 +137,7 @@ "Archive: conll2003.zip\r\n", " inflating: data/metadata \r\n", " inflating: data/test.txt \r\n", - " inflating: data/train.txt " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", + " inflating: data/train.txt \r\n", " inflating: data/valid.txt \r\n" ] }, @@ -152,9 +145,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-05-02 13:40:19-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.101.65, 3.5.28.252, 52.216.177.115, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.101.65|:443... connected.\r\n", + "--2024-05-03 22:23:37-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.230.89, 54.231.198.169, 3.5.20.164, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.230.89|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -177,7 +170,7 @@ "\r", "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", "\r\n", - "2024-05-02 13:40:20 (120 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-05-03 22:23:38 (151 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -194,10 +187,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:20.305771Z", - "iopub.status.busy": "2024-05-02T13:40:20.305463Z", - "iopub.status.idle": "2024-05-02T13:40:21.515797Z", - "shell.execute_reply": "2024-05-02T13:40:21.515245Z" + "iopub.execute_input": "2024-05-03T22:23:38.356845Z", + "iopub.status.busy": "2024-05-03T22:23:38.356625Z", + "iopub.status.idle": "2024-05-03T22:23:39.676466Z", + "shell.execute_reply": "2024-05-03T22:23:39.675901Z" }, "nbsphinx": "hidden" }, @@ -208,7 +201,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@b13d27e9b9524b6853d31a585111bd1eeedc173b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3c6c9a107ad0b56cc6b85476a11f22d7b27f9219\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -234,10 +227,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:21.518227Z", - "iopub.status.busy": "2024-05-02T13:40:21.517942Z", - "iopub.status.idle": "2024-05-02T13:40:21.521185Z", - "shell.execute_reply": "2024-05-02T13:40:21.520768Z" + "iopub.execute_input": "2024-05-03T22:23:39.678961Z", + "iopub.status.busy": "2024-05-03T22:23:39.678662Z", + "iopub.status.idle": "2024-05-03T22:23:39.682231Z", + "shell.execute_reply": "2024-05-03T22:23:39.681754Z" } }, "outputs": [], @@ -287,10 +280,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:21.523266Z", - "iopub.status.busy": "2024-05-02T13:40:21.522960Z", - "iopub.status.idle": "2024-05-02T13:40:21.526363Z", - "shell.execute_reply": "2024-05-02T13:40:21.525926Z" + "iopub.execute_input": "2024-05-03T22:23:39.684500Z", + "iopub.status.busy": "2024-05-03T22:23:39.684094Z", + "iopub.status.idle": "2024-05-03T22:23:39.687145Z", + "shell.execute_reply": "2024-05-03T22:23:39.686710Z" }, "nbsphinx": "hidden" }, @@ -308,10 +301,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:21.528351Z", - "iopub.status.busy": "2024-05-02T13:40:21.528032Z", - "iopub.status.idle": "2024-05-02T13:40:30.505208Z", - "shell.execute_reply": "2024-05-02T13:40:30.504621Z" + "iopub.execute_input": "2024-05-03T22:23:39.689097Z", + "iopub.status.busy": "2024-05-03T22:23:39.688919Z", + "iopub.status.idle": "2024-05-03T22:23:48.757440Z", + "shell.execute_reply": "2024-05-03T22:23:48.756894Z" } }, "outputs": [], @@ -385,10 +378,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:30.507896Z", - "iopub.status.busy": "2024-05-02T13:40:30.507445Z", - "iopub.status.idle": "2024-05-02T13:40:30.512858Z", - "shell.execute_reply": "2024-05-02T13:40:30.512422Z" + "iopub.execute_input": "2024-05-03T22:23:48.759866Z", + "iopub.status.busy": "2024-05-03T22:23:48.759658Z", + "iopub.status.idle": "2024-05-03T22:23:48.765417Z", + "shell.execute_reply": "2024-05-03T22:23:48.764899Z" }, "nbsphinx": "hidden" }, @@ -428,10 +421,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:30.514973Z", - "iopub.status.busy": "2024-05-02T13:40:30.514562Z", - "iopub.status.idle": "2024-05-02T13:40:30.847546Z", - "shell.execute_reply": "2024-05-02T13:40:30.847003Z" + "iopub.execute_input": "2024-05-03T22:23:48.767520Z", + "iopub.status.busy": "2024-05-03T22:23:48.767327Z", + "iopub.status.idle": "2024-05-03T22:23:49.131868Z", + "shell.execute_reply": "2024-05-03T22:23:49.131295Z" } }, "outputs": [], @@ -468,10 +461,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:30.849872Z", - "iopub.status.busy": "2024-05-02T13:40:30.849552Z", - "iopub.status.idle": "2024-05-02T13:40:30.853912Z", - "shell.execute_reply": "2024-05-02T13:40:30.853373Z" + "iopub.execute_input": "2024-05-03T22:23:49.134505Z", + "iopub.status.busy": "2024-05-03T22:23:49.134179Z", + "iopub.status.idle": "2024-05-03T22:23:49.138593Z", + "shell.execute_reply": "2024-05-03T22:23:49.138045Z" } }, "outputs": [ @@ -543,10 +536,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:30.855948Z", - "iopub.status.busy": "2024-05-02T13:40:30.855548Z", - "iopub.status.idle": "2024-05-02T13:40:33.093365Z", - "shell.execute_reply": "2024-05-02T13:40:33.092608Z" + "iopub.execute_input": "2024-05-03T22:23:49.140885Z", + "iopub.status.busy": "2024-05-03T22:23:49.140596Z", + "iopub.status.idle": "2024-05-03T22:23:51.622613Z", + "shell.execute_reply": "2024-05-03T22:23:51.621823Z" } }, "outputs": [], @@ -568,10 +561,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:33.096362Z", - "iopub.status.busy": "2024-05-02T13:40:33.095810Z", - "iopub.status.idle": "2024-05-02T13:40:33.099757Z", - "shell.execute_reply": "2024-05-02T13:40:33.099252Z" + "iopub.execute_input": "2024-05-03T22:23:51.625864Z", + "iopub.status.busy": "2024-05-03T22:23:51.625130Z", + "iopub.status.idle": "2024-05-03T22:23:51.629398Z", + "shell.execute_reply": "2024-05-03T22:23:51.628892Z" } }, "outputs": [ @@ -607,10 +600,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:33.101776Z", - "iopub.status.busy": "2024-05-02T13:40:33.101453Z", - "iopub.status.idle": "2024-05-02T13:40:33.106479Z", - "shell.execute_reply": "2024-05-02T13:40:33.105927Z" + "iopub.execute_input": "2024-05-03T22:23:51.631367Z", + "iopub.status.busy": "2024-05-03T22:23:51.631188Z", + "iopub.status.idle": "2024-05-03T22:23:51.636936Z", + "shell.execute_reply": "2024-05-03T22:23:51.636445Z" } }, "outputs": [ @@ -788,10 +781,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:33.108502Z", - "iopub.status.busy": "2024-05-02T13:40:33.108179Z", - "iopub.status.idle": "2024-05-02T13:40:33.133976Z", - "shell.execute_reply": "2024-05-02T13:40:33.133529Z" + "iopub.execute_input": "2024-05-03T22:23:51.638922Z", + "iopub.status.busy": "2024-05-03T22:23:51.638726Z", + "iopub.status.idle": "2024-05-03T22:23:51.667029Z", + "shell.execute_reply": "2024-05-03T22:23:51.666455Z" } }, "outputs": [ @@ -893,10 +886,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:33.135992Z", - "iopub.status.busy": "2024-05-02T13:40:33.135668Z", - "iopub.status.idle": "2024-05-02T13:40:33.139857Z", - "shell.execute_reply": "2024-05-02T13:40:33.139427Z" + "iopub.execute_input": "2024-05-03T22:23:51.669153Z", + "iopub.status.busy": "2024-05-03T22:23:51.668968Z", + "iopub.status.idle": "2024-05-03T22:23:51.674198Z", + "shell.execute_reply": "2024-05-03T22:23:51.673668Z" } }, "outputs": [ @@ -970,10 +963,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:33.141842Z", - "iopub.status.busy": "2024-05-02T13:40:33.141534Z", - "iopub.status.idle": "2024-05-02T13:40:34.486384Z", - "shell.execute_reply": "2024-05-02T13:40:34.485763Z" + "iopub.execute_input": "2024-05-03T22:23:51.676452Z", + "iopub.status.busy": "2024-05-03T22:23:51.676059Z", + "iopub.status.idle": "2024-05-03T22:23:53.102278Z", + "shell.execute_reply": "2024-05-03T22:23:53.101726Z" } }, "outputs": [ @@ -1145,10 +1138,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-05-02T13:40:34.488408Z", - "iopub.status.busy": "2024-05-02T13:40:34.488226Z", - "iopub.status.idle": "2024-05-02T13:40:34.492323Z", - "shell.execute_reply": "2024-05-02T13:40:34.491867Z" + "iopub.execute_input": "2024-05-03T22:23:53.104731Z", + "iopub.status.busy": "2024-05-03T22:23:53.104290Z", + "iopub.status.idle": "2024-05-03T22:23:53.108435Z", + "shell.execute_reply": "2024-05-03T22:23:53.107944Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index c6194524a..21eae59c1 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.3", - commit_hash: "b13d27e9b9524b6853d31a585111bd1eeedc173b", + commit_hash: "3c6c9a107ad0b56cc6b85476a11f22d7b27f9219", }; \ No newline at end of file