\n",
" \n",
@@ -28,10 +28,10 @@
"\n",
"# Assuming your dataset has a label column named 'label'\n",
"lab = Datalab(dataset, label_name='label', task='multilabel')\n",
+ "# To detect more issue types, optionally supply `features` (numeric dataset values or model embeddings of the data)\n",
+ "lab.find_issues(pred_probs=pred_probs, features=features)\n",
"\n",
- "lab.find_issues(pred_probs=pred_probs, issue_types={\"label\": {}})\n",
- "\n",
- "ranked_label_issues = lab.get_issues(\"label\").sort_values(\"label_score\")\n",
+ "lab.report()\n",
"```\n",
"\n",
" \n",
@@ -64,10 +64,10 @@
"id": "7383d024-8273-4039-bccd-aab3020d331f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:38.074145Z",
- "iopub.status.busy": "2024-02-07T22:15:38.073970Z",
- "iopub.status.idle": "2024-02-07T22:15:39.203313Z",
- "shell.execute_reply": "2024-02-07T22:15:39.202712Z"
+ "iopub.execute_input": "2024-02-07T23:56:08.687266Z",
+ "iopub.status.busy": "2024-02-07T23:56:08.687100Z",
+ "iopub.status.idle": "2024-02-07T23:56:09.754564Z",
+ "shell.execute_reply": "2024-02-07T23:56:09.754027Z"
},
"nbsphinx": "hidden"
},
@@ -79,7 +79,7 @@
"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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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": "2024-02-07T22:15:39.205859Z",
- "iopub.status.busy": "2024-02-07T22:15:39.205596Z",
- "iopub.status.idle": "2024-02-07T22:15:39.414109Z",
- "shell.execute_reply": "2024-02-07T22:15:39.413479Z"
+ "iopub.execute_input": "2024-02-07T23:56:09.757160Z",
+ "iopub.status.busy": "2024-02-07T23:56:09.756746Z",
+ "iopub.status.idle": "2024-02-07T23:56:09.946615Z",
+ "shell.execute_reply": "2024-02-07T23:56:09.946097Z"
}
},
"outputs": [],
@@ -268,10 +268,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:39.416970Z",
- "iopub.status.busy": "2024-02-07T22:15:39.416561Z",
- "iopub.status.idle": "2024-02-07T22:15:39.429574Z",
- "shell.execute_reply": "2024-02-07T22:15:39.429157Z"
+ "iopub.execute_input": "2024-02-07T23:56:09.949158Z",
+ "iopub.status.busy": "2024-02-07T23:56:09.948714Z",
+ "iopub.status.idle": "2024-02-07T23:56:09.961413Z",
+ "shell.execute_reply": "2024-02-07T23:56:09.960973Z"
},
"nbsphinx": "hidden"
},
@@ -407,10 +407,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:39.431695Z",
- "iopub.status.busy": "2024-02-07T22:15:39.431290Z",
- "iopub.status.idle": "2024-02-07T22:15:42.066450Z",
- "shell.execute_reply": "2024-02-07T22:15:42.065835Z"
+ "iopub.execute_input": "2024-02-07T23:56:09.963377Z",
+ "iopub.status.busy": "2024-02-07T23:56:09.963052Z",
+ "iopub.status.idle": "2024-02-07T23:56:12.619380Z",
+ "shell.execute_reply": "2024-02-07T23:56:12.618822Z"
}
},
"outputs": [
@@ -452,10 +452,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:42.068923Z",
- "iopub.status.busy": "2024-02-07T22:15:42.068564Z",
- "iopub.status.idle": "2024-02-07T22:15:43.411615Z",
- "shell.execute_reply": "2024-02-07T22:15:43.411041Z"
+ "iopub.execute_input": "2024-02-07T23:56:12.621613Z",
+ "iopub.status.busy": "2024-02-07T23:56:12.621277Z",
+ "iopub.status.idle": "2024-02-07T23:56:13.971194Z",
+ "shell.execute_reply": "2024-02-07T23:56:13.970652Z"
}
},
"outputs": [],
@@ -497,10 +497,10 @@
"id": "ac1a60df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:43.414000Z",
- "iopub.status.busy": "2024-02-07T22:15:43.413818Z",
- "iopub.status.idle": "2024-02-07T22:15:43.417446Z",
- "shell.execute_reply": "2024-02-07T22:15:43.416934Z"
+ "iopub.execute_input": "2024-02-07T23:56:13.973499Z",
+ "iopub.status.busy": "2024-02-07T23:56:13.973162Z",
+ "iopub.status.idle": "2024-02-07T23:56:13.977165Z",
+ "shell.execute_reply": "2024-02-07T23:56:13.976701Z"
}
},
"outputs": [
@@ -542,10 +542,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:43.419309Z",
- "iopub.status.busy": "2024-02-07T22:15:43.419135Z",
- "iopub.status.idle": "2024-02-07T22:15:45.207494Z",
- "shell.execute_reply": "2024-02-07T22:15:45.206816Z"
+ "iopub.execute_input": "2024-02-07T23:56:13.979194Z",
+ "iopub.status.busy": "2024-02-07T23:56:13.978885Z",
+ "iopub.status.idle": "2024-02-07T23:56:15.657571Z",
+ "shell.execute_reply": "2024-02-07T23:56:15.657005Z"
}
},
"outputs": [
@@ -592,10 +592,10 @@
"id": "c18dd83b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:45.210051Z",
- "iopub.status.busy": "2024-02-07T22:15:45.209488Z",
- "iopub.status.idle": "2024-02-07T22:15:45.217636Z",
- "shell.execute_reply": "2024-02-07T22:15:45.217158Z"
+ "iopub.execute_input": "2024-02-07T23:56:15.660326Z",
+ "iopub.status.busy": "2024-02-07T23:56:15.659641Z",
+ "iopub.status.idle": "2024-02-07T23:56:15.667026Z",
+ "shell.execute_reply": "2024-02-07T23:56:15.666496Z"
}
},
"outputs": [
@@ -631,10 +631,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:45.219651Z",
- "iopub.status.busy": "2024-02-07T22:15:45.219310Z",
- "iopub.status.idle": "2024-02-07T22:15:47.808437Z",
- "shell.execute_reply": "2024-02-07T22:15:47.807942Z"
+ "iopub.execute_input": "2024-02-07T23:56:15.669181Z",
+ "iopub.status.busy": "2024-02-07T23:56:15.668872Z",
+ "iopub.status.idle": "2024-02-07T23:56:18.270814Z",
+ "shell.execute_reply": "2024-02-07T23:56:18.270242Z"
}
},
"outputs": [
@@ -669,10 +669,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:47.810738Z",
- "iopub.status.busy": "2024-02-07T22:15:47.810301Z",
- "iopub.status.idle": "2024-02-07T22:15:47.813993Z",
- "shell.execute_reply": "2024-02-07T22:15:47.813455Z"
+ "iopub.execute_input": "2024-02-07T23:56:18.273199Z",
+ "iopub.status.busy": "2024-02-07T23:56:18.272850Z",
+ "iopub.status.idle": "2024-02-07T23:56:18.276268Z",
+ "shell.execute_reply": "2024-02-07T23:56:18.275699Z"
}
},
"outputs": [
@@ -691,6 +691,16 @@
"print(f\"Label quality scores of the first 10 examples in dataset:\\n{scores[:10]}\")"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "d65af827-aeda-4b6b-9ae7-b1f0b84700d6",
+ "metadata": {},
+ "source": [
+ "While this tutorial focused on label issues, cleanlab's `Datalab` object can automatically detect many other types of issues in your dataset (outliers, near duplicates, etc).\n",
+ "Simply remove the `issue_types` argument from the above call to `Datalab.find_issues()` above and `Datalab` will more comprehensively audit your dataset.\n",
+ "Refer to our [Datalab quickstart tutorial](./datalab/datalab_quickstart.html) to learn how to interpret the results (the interpretation remains mostly the same across different types of ML tasks)."
+ ]
+ },
{
"cell_type": "markdown",
"id": "d65af827-aeda-4b6b-9ae7-b1f0b84700d5",
@@ -707,10 +717,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:47.815974Z",
- "iopub.status.busy": "2024-02-07T22:15:47.815684Z",
- "iopub.status.idle": "2024-02-07T22:15:47.819808Z",
- "shell.execute_reply": "2024-02-07T22:15:47.819250Z"
+ "iopub.execute_input": "2024-02-07T23:56:18.278300Z",
+ "iopub.status.busy": "2024-02-07T23:56:18.277998Z",
+ "iopub.status.idle": "2024-02-07T23:56:18.282388Z",
+ "shell.execute_reply": "2024-02-07T23:56:18.281980Z"
}
},
"outputs": [],
@@ -733,10 +743,10 @@
"id": "d1a2c008",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:47.821661Z",
- "iopub.status.busy": "2024-02-07T22:15:47.821392Z",
- "iopub.status.idle": "2024-02-07T22:15:47.824553Z",
- "shell.execute_reply": "2024-02-07T22:15:47.824004Z"
+ "iopub.execute_input": "2024-02-07T23:56:18.284367Z",
+ "iopub.status.busy": "2024-02-07T23:56:18.284047Z",
+ "iopub.status.idle": "2024-02-07T23:56:18.286987Z",
+ "shell.execute_reply": "2024-02-07T23:56:18.286563Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
index bd98ee443..74d92a714 100644
--- a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
@@ -70,10 +70,10 @@
"id": "0ba0dc70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:50.447928Z",
- "iopub.status.busy": "2024-02-07T22:15:50.447758Z",
- "iopub.status.idle": "2024-02-07T22:15:51.564104Z",
- "shell.execute_reply": "2024-02-07T22:15:51.563498Z"
+ "iopub.execute_input": "2024-02-07T23:56:20.562684Z",
+ "iopub.status.busy": "2024-02-07T23:56:20.562520Z",
+ "iopub.status.idle": "2024-02-07T23:56:21.638239Z",
+ "shell.execute_reply": "2024-02-07T23:56:21.637675Z"
},
"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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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": "2024-02-07T22:15:51.566518Z",
- "iopub.status.busy": "2024-02-07T22:15:51.566240Z",
- "iopub.status.idle": "2024-02-07T22:15:52.801961Z",
- "shell.execute_reply": "2024-02-07T22:15:52.801317Z"
+ "iopub.execute_input": "2024-02-07T23:56:21.640925Z",
+ "iopub.status.busy": "2024-02-07T23:56:21.640505Z",
+ "iopub.status.idle": "2024-02-07T23:56:22.688443Z",
+ "shell.execute_reply": "2024-02-07T23:56:22.687829Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:52.804419Z",
- "iopub.status.busy": "2024-02-07T22:15:52.804219Z",
- "iopub.status.idle": "2024-02-07T22:15:52.807410Z",
- "shell.execute_reply": "2024-02-07T22:15:52.806937Z"
+ "iopub.execute_input": "2024-02-07T23:56:22.690921Z",
+ "iopub.status.busy": "2024-02-07T23:56:22.690540Z",
+ "iopub.status.idle": "2024-02-07T23:56:22.693716Z",
+ "shell.execute_reply": "2024-02-07T23:56:22.693271Z"
}
},
"outputs": [],
@@ -165,10 +165,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:52.809336Z",
- "iopub.status.busy": "2024-02-07T22:15:52.809039Z",
- "iopub.status.idle": "2024-02-07T22:15:52.815182Z",
- "shell.execute_reply": "2024-02-07T22:15:52.814647Z"
+ "iopub.execute_input": "2024-02-07T23:56:22.695625Z",
+ "iopub.status.busy": "2024-02-07T23:56:22.695302Z",
+ "iopub.status.idle": "2024-02-07T23:56:22.701268Z",
+ "shell.execute_reply": "2024-02-07T23:56:22.700863Z"
}
},
"outputs": [],
@@ -194,10 +194,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:52.817367Z",
- "iopub.status.busy": "2024-02-07T22:15:52.816989Z",
- "iopub.status.idle": "2024-02-07T22:15:53.308064Z",
- "shell.execute_reply": "2024-02-07T22:15:53.307480Z"
+ "iopub.execute_input": "2024-02-07T23:56:22.703196Z",
+ "iopub.status.busy": "2024-02-07T23:56:22.702938Z",
+ "iopub.status.idle": "2024-02-07T23:56:23.186186Z",
+ "shell.execute_reply": "2024-02-07T23:56:23.185652Z"
},
"scrolled": true
},
@@ -238,10 +238,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:53.310939Z",
- "iopub.status.busy": "2024-02-07T22:15:53.310566Z",
- "iopub.status.idle": "2024-02-07T22:15:53.315939Z",
- "shell.execute_reply": "2024-02-07T22:15:53.315394Z"
+ "iopub.execute_input": "2024-02-07T23:56:23.188813Z",
+ "iopub.status.busy": "2024-02-07T23:56:23.188488Z",
+ "iopub.status.idle": "2024-02-07T23:56:23.193539Z",
+ "shell.execute_reply": "2024-02-07T23:56:23.193126Z"
}
},
"outputs": [
@@ -493,10 +493,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:53.317995Z",
- "iopub.status.busy": "2024-02-07T22:15:53.317689Z",
- "iopub.status.idle": "2024-02-07T22:15:53.321394Z",
- "shell.execute_reply": "2024-02-07T22:15:53.320912Z"
+ "iopub.execute_input": "2024-02-07T23:56:23.195528Z",
+ "iopub.status.busy": "2024-02-07T23:56:23.195222Z",
+ "iopub.status.idle": "2024-02-07T23:56:23.199084Z",
+ "shell.execute_reply": "2024-02-07T23:56:23.198542Z"
}
},
"outputs": [
@@ -553,10 +553,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:53.323449Z",
- "iopub.status.busy": "2024-02-07T22:15:53.323111Z",
- "iopub.status.idle": "2024-02-07T22:15:54.015545Z",
- "shell.execute_reply": "2024-02-07T22:15:54.014869Z"
+ "iopub.execute_input": "2024-02-07T23:56:23.201119Z",
+ "iopub.status.busy": "2024-02-07T23:56:23.200823Z",
+ "iopub.status.idle": "2024-02-07T23:56:23.836419Z",
+ "shell.execute_reply": "2024-02-07T23:56:23.835758Z"
}
},
"outputs": [
@@ -612,10 +612,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:54.018097Z",
- "iopub.status.busy": "2024-02-07T22:15:54.017708Z",
- "iopub.status.idle": "2024-02-07T22:15:54.187878Z",
- "shell.execute_reply": "2024-02-07T22:15:54.187411Z"
+ "iopub.execute_input": "2024-02-07T23:56:23.838728Z",
+ "iopub.status.busy": "2024-02-07T23:56:23.838526Z",
+ "iopub.status.idle": "2024-02-07T23:56:23.988464Z",
+ "shell.execute_reply": "2024-02-07T23:56:23.988047Z"
}
},
"outputs": [
@@ -656,10 +656,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:54.189935Z",
- "iopub.status.busy": "2024-02-07T22:15:54.189745Z",
- "iopub.status.idle": "2024-02-07T22:15:54.194222Z",
- "shell.execute_reply": "2024-02-07T22:15:54.193779Z"
+ "iopub.execute_input": "2024-02-07T23:56:23.990471Z",
+ "iopub.status.busy": "2024-02-07T23:56:23.990165Z",
+ "iopub.status.idle": "2024-02-07T23:56:23.994186Z",
+ "shell.execute_reply": "2024-02-07T23:56:23.993763Z"
}
},
"outputs": [
@@ -696,10 +696,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:54.196260Z",
- "iopub.status.busy": "2024-02-07T22:15:54.195894Z",
- "iopub.status.idle": "2024-02-07T22:15:54.649689Z",
- "shell.execute_reply": "2024-02-07T22:15:54.649109Z"
+ "iopub.execute_input": "2024-02-07T23:56:23.996117Z",
+ "iopub.status.busy": "2024-02-07T23:56:23.995822Z",
+ "iopub.status.idle": "2024-02-07T23:56:24.434799Z",
+ "shell.execute_reply": "2024-02-07T23:56:24.434220Z"
}
},
"outputs": [
@@ -758,10 +758,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:54.652534Z",
- "iopub.status.busy": "2024-02-07T22:15:54.652158Z",
- "iopub.status.idle": "2024-02-07T22:15:54.984855Z",
- "shell.execute_reply": "2024-02-07T22:15:54.984298Z"
+ "iopub.execute_input": "2024-02-07T23:56:24.437281Z",
+ "iopub.status.busy": "2024-02-07T23:56:24.436886Z",
+ "iopub.status.idle": "2024-02-07T23:56:24.768783Z",
+ "shell.execute_reply": "2024-02-07T23:56:24.768200Z"
}
},
"outputs": [
@@ -808,10 +808,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-02-07T22:15:55.469879Z"
+ "iopub.execute_input": "2024-02-07T23:56:24.771064Z",
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+ "shell.execute_reply": "2024-02-07T23:56:25.249843Z"
}
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"outputs": [
@@ -858,10 +858,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:55.475223Z",
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- "iopub.status.idle": "2024-02-07T22:15:55.916362Z",
- "shell.execute_reply": "2024-02-07T22:15:55.915780Z"
+ "iopub.execute_input": "2024-02-07T23:56:25.254651Z",
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+ "shell.execute_reply": "2024-02-07T23:56:25.662264Z"
}
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"outputs": [
@@ -921,10 +921,10 @@
"id": "7e770d23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:55.920172Z",
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+ "iopub.execute_input": "2024-02-07T23:56:25.666313Z",
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+ "shell.execute_reply": "2024-02-07T23:56:26.089821Z"
}
},
"outputs": [
@@ -967,10 +967,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:56.372426Z",
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- "shell.execute_reply": "2024-02-07T22:15:56.586546Z"
+ "iopub.execute_input": "2024-02-07T23:56:26.093719Z",
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+ "shell.execute_reply": "2024-02-07T23:56:26.280571Z"
}
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"outputs": [
@@ -1013,10 +1013,10 @@
"id": "0302818a",
"metadata": {
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+ "shell.execute_reply": "2024-02-07T23:56:26.462690Z"
}
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"outputs": [
@@ -1051,10 +1051,10 @@
"id": "8ce74938",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:56.790786Z",
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+ "iopub.execute_input": "2024-02-07T23:56:26.465892Z",
+ "iopub.status.busy": "2024-02-07T23:56:26.465487Z",
+ "iopub.status.idle": "2024-02-07T23:56:26.468734Z",
+ "shell.execute_reply": "2024-02-07T23:56:26.468320Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
index a17f8d98c..216232f6b 100644
--- a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
@@ -109,10 +109,10 @@
"id": "2bbebfc8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:15:59.020657Z",
- "iopub.status.busy": "2024-02-07T22:15:59.020490Z",
- "iopub.status.idle": "2024-02-07T22:16:01.758945Z",
- "shell.execute_reply": "2024-02-07T22:16:01.758382Z"
+ "iopub.execute_input": "2024-02-07T23:56:28.439526Z",
+ "iopub.status.busy": "2024-02-07T23:56:28.439052Z",
+ "iopub.status.idle": "2024-02-07T23:56:31.054002Z",
+ "shell.execute_reply": "2024-02-07T23:56:31.053462Z"
},
"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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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": "2024-02-07T22:16:01.761638Z",
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- "shell.execute_reply": "2024-02-07T22:16:02.095807Z"
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+ "shell.execute_reply": "2024-02-07T23:56:31.365105Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "3792f82e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:02.099057Z",
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- "iopub.status.idle": "2024-02-07T22:16:02.102883Z",
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+ "shell.execute_reply": "2024-02-07T23:56:31.371533Z"
},
"nbsphinx": "hidden"
},
@@ -225,10 +225,10 @@
"id": "fd853a54",
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+ "iopub.execute_input": "2024-02-07T23:56:31.374309Z",
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+ "shell.execute_reply": "2024-02-07T23:56:35.798918Z"
}
},
"outputs": [
@@ -252,7 +252,7 @@
"output_type": "stream",
"text": [
"\r",
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+ " 1%| | 1802240/170498071 [00:00<00:09, 17679640.48it/s]"
]
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{
@@ -260,7 +260,7 @@
"output_type": "stream",
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"\r",
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+ " 7%|▋ | 12124160/170498071 [00:00<00:02, 67499577.03it/s]"
]
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{
@@ -268,7 +268,7 @@
"output_type": "stream",
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"\r",
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+ " 13%|█▎ | 23003136/170498071 [00:00<00:01, 86235389.73it/s]"
]
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{
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+ " 19%|█▉ | 32899072/170498071 [00:00<00:01, 91230976.11it/s]"
]
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{
@@ -284,7 +284,7 @@
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"\r",
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+ " 25%|██▌ | 43384832/170498071 [00:00<00:01, 96066497.78it/s]"
]
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{
@@ -292,7 +292,7 @@
"output_type": "stream",
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+ " 32%|███▏ | 53739520/170498071 [00:00<00:01, 98584790.93it/s]"
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{
@@ -300,7 +300,7 @@
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+ " 38%|███▊ | 64028672/170498071 [00:00<00:01, 99955094.01it/s]"
]
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+ " 44%|████▍ | 74874880/170498071 [00:00<00:00, 102643580.09it/s]"
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+ " 50%|████▉ | 85164032/170498071 [00:00<00:00, 101368779.28it/s]"
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+ " 57%|█████▋ | 96403456/170498071 [00:01<00:00, 104721663.01it/s]"
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@@ -332,7 +332,7 @@
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+ " 63%|██████▎ | 106889216/170498071 [00:01<00:00, 102308446.63it/s]"
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{
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+ " 69%|██████▉ | 118259712/170498071 [00:01<00:00, 105670851.32it/s]"
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+ " 76%|███████▌ | 128876544/170498071 [00:01<00:00, 102956737.54it/s]"
]
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{
@@ -356,7 +356,7 @@
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+ " 82%|████████▏ | 140181504/170498071 [00:01<00:00, 105850590.83it/s]"
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+ " 88%|████████▊ | 150798336/170498071 [00:01<00:00, 103312662.70it/s]"
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+ " 95%|█████████▍| 161841152/170498071 [00:01<00:00, 105282046.90it/s]"
]
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- ]
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- ]
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+ "100%|██████████| 170498071/170498071 [00:01<00:00, 99133860.68it/s] "
]
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{
@@ -570,10 +498,10 @@
"id": "9b64e0aa",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-02-07T22:16:07.275905Z"
+ "iopub.execute_input": "2024-02-07T23:56:35.802008Z",
+ "iopub.status.busy": "2024-02-07T23:56:35.801591Z",
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+ "shell.execute_reply": "2024-02-07T23:56:35.805886Z"
},
"nbsphinx": "hidden"
},
@@ -624,10 +552,10 @@
"id": "a00aa3ed",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:07.278274Z",
- "iopub.status.busy": "2024-02-07T22:16:07.277964Z",
- "iopub.status.idle": "2024-02-07T22:16:07.830552Z",
- "shell.execute_reply": "2024-02-07T22:16:07.829958Z"
+ "iopub.execute_input": "2024-02-07T23:56:35.808410Z",
+ "iopub.status.busy": "2024-02-07T23:56:35.808083Z",
+ "iopub.status.idle": "2024-02-07T23:56:36.353076Z",
+ "shell.execute_reply": "2024-02-07T23:56:36.352508Z"
}
},
"outputs": [
@@ -660,10 +588,10 @@
"id": "41e5cb6b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:07.832750Z",
- "iopub.status.busy": "2024-02-07T22:16:07.832421Z",
- "iopub.status.idle": "2024-02-07T22:16:08.355543Z",
- "shell.execute_reply": "2024-02-07T22:16:08.354936Z"
+ "iopub.execute_input": "2024-02-07T23:56:36.355346Z",
+ "iopub.status.busy": "2024-02-07T23:56:36.354914Z",
+ "iopub.status.idle": "2024-02-07T23:56:36.873202Z",
+ "shell.execute_reply": "2024-02-07T23:56:36.872720Z"
}
},
"outputs": [
@@ -701,10 +629,10 @@
"id": "1cf25354",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:08.357548Z",
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- "shell.execute_reply": "2024-02-07T22:16:08.360580Z"
+ "iopub.execute_input": "2024-02-07T23:56:36.875189Z",
+ "iopub.status.busy": "2024-02-07T23:56:36.875002Z",
+ "iopub.status.idle": "2024-02-07T23:56:36.878463Z",
+ "shell.execute_reply": "2024-02-07T23:56:36.878026Z"
}
},
"outputs": [],
@@ -727,17 +655,17 @@
"id": "85a58d41",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-02-07T22:16:20.965959Z"
+ "iopub.execute_input": "2024-02-07T23:56:36.880489Z",
+ "iopub.status.busy": "2024-02-07T23:56:36.880123Z",
+ "iopub.status.idle": "2024-02-07T23:56:49.380652Z",
+ "shell.execute_reply": "2024-02-07T23:56:49.380045Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "066739a4f86b491c983744119357f90a",
+ "model_id": "104aeedd0a604fe19bdfe63c8894bf8c",
"version_major": 2,
"version_minor": 0
},
@@ -796,10 +724,10 @@
"id": "feb0f519",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-02-07T22:16:22.543395Z"
+ "iopub.execute_input": "2024-02-07T23:56:49.383110Z",
+ "iopub.status.busy": "2024-02-07T23:56:49.382718Z",
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+ "shell.execute_reply": "2024-02-07T23:56:50.972522Z"
}
},
"outputs": [
@@ -843,10 +771,10 @@
"id": "089d5860",
"metadata": {
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- "shell.execute_reply": "2024-02-07T22:16:22.971973Z"
+ "iopub.execute_input": "2024-02-07T23:56:50.975995Z",
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+ "shell.execute_reply": "2024-02-07T23:56:51.397417Z"
}
},
"outputs": [
@@ -882,10 +810,10 @@
"id": "78b1951c",
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@@ -935,10 +863,10 @@
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@@ -986,10 +914,10 @@
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@@ -1045,10 +973,10 @@
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@@ -1069,10 +997,10 @@
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@@ -1109,10 +1037,10 @@
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@@ -1143,10 +1071,10 @@
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@@ -1160,10 +1088,10 @@
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@@ -1185,10 +1113,10 @@
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@@ -1233,7 +1161,7 @@
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@@ -1248,16 +1176,117 @@
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],
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}
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@@ -1310,23 +1339,7 @@
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@@ -1379,92 +1392,7 @@
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@@ -1517,7 +1445,7 @@
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@@ -1532,15 +1460,15 @@
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- "layout": "IPY_MODEL_fc78c8afb40d44d585718c7ac9cacbe8",
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- "value": " 102M/102M [00:00<00:00, 300MB/s]"
+ "value": " 102M/102M [00:00<00:00, 208MB/s]"
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diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
index 6460b1da7..f14378353 100644
--- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
@@ -94,10 +94,10 @@
"id": "2e1af7d8",
"metadata": {
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- "iopub.status.idle": "2024-02-07T22:16:42.113233Z",
- "shell.execute_reply": "2024-02-07T22:16:42.112677Z"
+ "iopub.execute_input": "2024-02-07T23:57:09.007821Z",
+ "iopub.status.busy": "2024-02-07T23:57:09.007615Z",
+ "iopub.status.idle": "2024-02-07T23:57:10.072485Z",
+ "shell.execute_reply": "2024-02-07T23:57:10.071948Z"
},
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@@ -109,7 +109,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -135,10 +135,10 @@
"id": "4fb10b8f",
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- "iopub.status.idle": "2024-02-07T22:16:42.133567Z",
- "shell.execute_reply": "2024-02-07T22:16:42.133124Z"
+ "iopub.execute_input": "2024-02-07T23:57:10.074834Z",
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+ "iopub.status.idle": "2024-02-07T23:57:10.092876Z",
+ "shell.execute_reply": "2024-02-07T23:57:10.092428Z"
}
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@@ -157,10 +157,10 @@
"id": "284dc264",
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- "shell.execute_reply": "2024-02-07T22:16:42.138043Z"
+ "iopub.execute_input": "2024-02-07T23:57:10.095294Z",
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+ "shell.execute_reply": "2024-02-07T23:57:10.097396Z"
},
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@@ -191,10 +191,10 @@
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- "iopub.status.idle": "2024-02-07T22:16:42.204946Z",
- "shell.execute_reply": "2024-02-07T22:16:42.204403Z"
+ "iopub.execute_input": "2024-02-07T23:57:10.099990Z",
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+ "shell.execute_reply": "2024-02-07T23:57:10.154741Z"
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@@ -367,10 +367,10 @@
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- "shell.execute_reply": "2024-02-07T22:16:42.385435Z"
+ "iopub.execute_input": "2024-02-07T23:57:10.157365Z",
+ "iopub.status.busy": "2024-02-07T23:57:10.156978Z",
+ "iopub.status.idle": "2024-02-07T23:57:10.331972Z",
+ "shell.execute_reply": "2024-02-07T23:57:10.331386Z"
},
"nbsphinx": "hidden"
},
@@ -410,10 +410,10 @@
"id": "df5a0f59",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:42.388432Z",
- "iopub.status.busy": "2024-02-07T22:16:42.388089Z",
- "iopub.status.idle": "2024-02-07T22:16:42.607298Z",
- "shell.execute_reply": "2024-02-07T22:16:42.606720Z"
+ "iopub.execute_input": "2024-02-07T23:57:10.334267Z",
+ "iopub.status.busy": "2024-02-07T23:57:10.334002Z",
+ "iopub.status.idle": "2024-02-07T23:57:10.541166Z",
+ "shell.execute_reply": "2024-02-07T23:57:10.540646Z"
}
},
"outputs": [
@@ -449,10 +449,10 @@
"id": "7af78a8a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:42.609580Z",
- "iopub.status.busy": "2024-02-07T22:16:42.609148Z",
- "iopub.status.idle": "2024-02-07T22:16:42.613684Z",
- "shell.execute_reply": "2024-02-07T22:16:42.613262Z"
+ "iopub.execute_input": "2024-02-07T23:57:10.543123Z",
+ "iopub.status.busy": "2024-02-07T23:57:10.542912Z",
+ "iopub.status.idle": "2024-02-07T23:57:10.547063Z",
+ "shell.execute_reply": "2024-02-07T23:57:10.546643Z"
}
},
"outputs": [],
@@ -470,10 +470,10 @@
"id": "9556c624",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:42.615732Z",
- "iopub.status.busy": "2024-02-07T22:16:42.615409Z",
- "iopub.status.idle": "2024-02-07T22:16:42.621064Z",
- "shell.execute_reply": "2024-02-07T22:16:42.620649Z"
+ "iopub.execute_input": "2024-02-07T23:57:10.549116Z",
+ "iopub.status.busy": "2024-02-07T23:57:10.548782Z",
+ "iopub.status.idle": "2024-02-07T23:57:10.554644Z",
+ "shell.execute_reply": "2024-02-07T23:57:10.554206Z"
}
},
"outputs": [],
@@ -520,10 +520,10 @@
"id": "3c2f1ccc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:42.623161Z",
- "iopub.status.busy": "2024-02-07T22:16:42.622751Z",
- "iopub.status.idle": "2024-02-07T22:16:42.625299Z",
- "shell.execute_reply": "2024-02-07T22:16:42.624875Z"
+ "iopub.execute_input": "2024-02-07T23:57:10.556665Z",
+ "iopub.status.busy": "2024-02-07T23:57:10.556409Z",
+ "iopub.status.idle": "2024-02-07T23:57:10.558873Z",
+ "shell.execute_reply": "2024-02-07T23:57:10.558458Z"
}
},
"outputs": [],
@@ -538,10 +538,10 @@
"id": "7e1b7860",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:42.627236Z",
- "iopub.status.busy": "2024-02-07T22:16:42.626928Z",
- "iopub.status.idle": "2024-02-07T22:16:50.819298Z",
- "shell.execute_reply": "2024-02-07T22:16:50.818629Z"
+ "iopub.execute_input": "2024-02-07T23:57:10.560767Z",
+ "iopub.status.busy": "2024-02-07T23:57:10.560447Z",
+ "iopub.status.idle": "2024-02-07T23:57:18.597883Z",
+ "shell.execute_reply": "2024-02-07T23:57:18.597245Z"
}
},
"outputs": [],
@@ -565,10 +565,10 @@
"id": "f407bd69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:50.822227Z",
- "iopub.status.busy": "2024-02-07T22:16:50.821588Z",
- "iopub.status.idle": "2024-02-07T22:16:50.828619Z",
- "shell.execute_reply": "2024-02-07T22:16:50.828176Z"
+ "iopub.execute_input": "2024-02-07T23:57:18.600502Z",
+ "iopub.status.busy": "2024-02-07T23:57:18.600144Z",
+ "iopub.status.idle": "2024-02-07T23:57:18.607142Z",
+ "shell.execute_reply": "2024-02-07T23:57:18.606625Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "f7385336",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:50.830508Z",
- "iopub.status.busy": "2024-02-07T22:16:50.830326Z",
- "iopub.status.idle": "2024-02-07T22:16:50.834051Z",
- "shell.execute_reply": "2024-02-07T22:16:50.833572Z"
+ "iopub.execute_input": "2024-02-07T23:57:18.609010Z",
+ "iopub.status.busy": "2024-02-07T23:57:18.608834Z",
+ "iopub.status.idle": "2024-02-07T23:57:18.612483Z",
+ "shell.execute_reply": "2024-02-07T23:57:18.611944Z"
}
},
"outputs": [],
@@ -689,10 +689,10 @@
"id": "59fc3091",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:50.836017Z",
- "iopub.status.busy": "2024-02-07T22:16:50.835693Z",
- "iopub.status.idle": "2024-02-07T22:16:50.838791Z",
- "shell.execute_reply": "2024-02-07T22:16:50.838260Z"
+ "iopub.execute_input": "2024-02-07T23:57:18.614300Z",
+ "iopub.status.busy": "2024-02-07T23:57:18.614127Z",
+ "iopub.status.idle": "2024-02-07T23:57:18.617088Z",
+ "shell.execute_reply": "2024-02-07T23:57:18.616563Z"
}
},
"outputs": [
@@ -727,10 +727,10 @@
"id": "00949977",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:50.840784Z",
- "iopub.status.busy": "2024-02-07T22:16:50.840458Z",
- "iopub.status.idle": "2024-02-07T22:16:50.843463Z",
- "shell.execute_reply": "2024-02-07T22:16:50.843001Z"
+ "iopub.execute_input": "2024-02-07T23:57:18.618875Z",
+ "iopub.status.busy": "2024-02-07T23:57:18.618705Z",
+ "iopub.status.idle": "2024-02-07T23:57:18.621573Z",
+ "shell.execute_reply": "2024-02-07T23:57:18.621156Z"
}
},
"outputs": [],
@@ -749,10 +749,10 @@
"id": "b6c1ae3a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:50.845383Z",
- "iopub.status.busy": "2024-02-07T22:16:50.845063Z",
- "iopub.status.idle": "2024-02-07T22:16:50.852852Z",
- "shell.execute_reply": "2024-02-07T22:16:50.852402Z"
+ "iopub.execute_input": "2024-02-07T23:57:18.623300Z",
+ "iopub.status.busy": "2024-02-07T23:57:18.623130Z",
+ "iopub.status.idle": "2024-02-07T23:57:18.631019Z",
+ "shell.execute_reply": "2024-02-07T23:57:18.630588Z"
}
},
"outputs": [
@@ -894,10 +894,10 @@
"id": "31c704e7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:50.854861Z",
- "iopub.status.busy": "2024-02-07T22:16:50.854539Z",
- "iopub.status.idle": "2024-02-07T22:16:50.974215Z",
- "shell.execute_reply": "2024-02-07T22:16:50.973641Z"
+ "iopub.execute_input": "2024-02-07T23:57:18.632839Z",
+ "iopub.status.busy": "2024-02-07T23:57:18.632669Z",
+ "iopub.status.idle": "2024-02-07T23:57:18.751110Z",
+ "shell.execute_reply": "2024-02-07T23:57:18.750652Z"
}
},
"outputs": [
@@ -936,10 +936,10 @@
"id": "0bcc43db",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:50.976792Z",
- "iopub.status.busy": "2024-02-07T22:16:50.976387Z",
- "iopub.status.idle": "2024-02-07T22:16:51.083738Z",
- "shell.execute_reply": "2024-02-07T22:16:51.083123Z"
+ "iopub.execute_input": "2024-02-07T23:57:18.753135Z",
+ "iopub.status.busy": "2024-02-07T23:57:18.752961Z",
+ "iopub.status.idle": "2024-02-07T23:57:18.854301Z",
+ "shell.execute_reply": "2024-02-07T23:57:18.853737Z"
}
},
"outputs": [
@@ -995,10 +995,10 @@
"id": "7021bd68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:51.086417Z",
- "iopub.status.busy": "2024-02-07T22:16:51.085958Z",
- "iopub.status.idle": "2024-02-07T22:16:51.565882Z",
- "shell.execute_reply": "2024-02-07T22:16:51.565259Z"
+ "iopub.execute_input": "2024-02-07T23:57:18.856714Z",
+ "iopub.status.busy": "2024-02-07T23:57:18.856277Z",
+ "iopub.status.idle": "2024-02-07T23:57:19.344066Z",
+ "shell.execute_reply": "2024-02-07T23:57:19.343446Z"
}
},
"outputs": [],
@@ -1014,10 +1014,10 @@
"id": "d49c990b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:51.568859Z",
- "iopub.status.busy": "2024-02-07T22:16:51.568362Z",
- "iopub.status.idle": "2024-02-07T22:16:51.646362Z",
- "shell.execute_reply": "2024-02-07T22:16:51.645818Z"
+ "iopub.execute_input": "2024-02-07T23:57:19.346737Z",
+ "iopub.status.busy": "2024-02-07T23:57:19.346338Z",
+ "iopub.status.idle": "2024-02-07T23:57:19.423350Z",
+ "shell.execute_reply": "2024-02-07T23:57:19.422787Z"
}
},
"outputs": [
@@ -1055,10 +1055,10 @@
"id": "95531cda",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:51.648680Z",
- "iopub.status.busy": "2024-02-07T22:16:51.648305Z",
- "iopub.status.idle": "2024-02-07T22:16:51.658253Z",
- "shell.execute_reply": "2024-02-07T22:16:51.657843Z"
+ "iopub.execute_input": "2024-02-07T23:57:19.425650Z",
+ "iopub.status.busy": "2024-02-07T23:57:19.425311Z",
+ "iopub.status.idle": "2024-02-07T23:57:19.434732Z",
+ "shell.execute_reply": "2024-02-07T23:57:19.434279Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
index 2278816b2..979c7e3be 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": "2024-02-07T22:16:54.759216Z",
- "iopub.status.busy": "2024-02-07T22:16:54.759059Z",
- "iopub.status.idle": "2024-02-07T22:16:56.709104Z",
- "shell.execute_reply": "2024-02-07T22:16:56.708365Z"
+ "iopub.execute_input": "2024-02-07T23:57:22.267015Z",
+ "iopub.status.busy": "2024-02-07T23:57:22.266847Z",
+ "iopub.status.idle": "2024-02-07T23:57:23.557742Z",
+ "shell.execute_reply": "2024-02-07T23:57:23.557100Z"
}
},
"outputs": [],
@@ -79,10 +79,10 @@
"id": "58fd4c55",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:16:56.711689Z",
- "iopub.status.busy": "2024-02-07T22:16:56.711492Z",
- "iopub.status.idle": "2024-02-07T22:17:51.576173Z",
- "shell.execute_reply": "2024-02-07T22:17:51.575514Z"
+ "iopub.execute_input": "2024-02-07T23:57:23.560412Z",
+ "iopub.status.busy": "2024-02-07T23:57:23.560029Z",
+ "iopub.status.idle": "2024-02-07T23:58:14.095094Z",
+ "shell.execute_reply": "2024-02-07T23:58:14.094451Z"
}
},
"outputs": [],
@@ -97,10 +97,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:17:51.578880Z",
- "iopub.status.busy": "2024-02-07T22:17:51.578435Z",
- "iopub.status.idle": "2024-02-07T22:17:52.624888Z",
- "shell.execute_reply": "2024-02-07T22:17:52.624282Z"
+ "iopub.execute_input": "2024-02-07T23:58:14.097742Z",
+ "iopub.status.busy": "2024-02-07T23:58:14.097332Z",
+ "iopub.status.idle": "2024-02-07T23:58:15.117655Z",
+ "shell.execute_reply": "2024-02-07T23:58:15.117170Z"
},
"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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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": "2024-02-07T22:17:52.627442Z",
- "iopub.status.busy": "2024-02-07T22:17:52.627135Z",
- "iopub.status.idle": "2024-02-07T22:17:52.630302Z",
- "shell.execute_reply": "2024-02-07T22:17:52.629875Z"
+ "iopub.execute_input": "2024-02-07T23:58:15.120117Z",
+ "iopub.status.busy": "2024-02-07T23:58:15.119697Z",
+ "iopub.status.idle": "2024-02-07T23:58:15.122830Z",
+ "shell.execute_reply": "2024-02-07T23:58:15.122391Z"
}
},
"outputs": [],
@@ -203,10 +203,10 @@
"id": "07dc5678",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:17:52.632362Z",
- "iopub.status.busy": "2024-02-07T22:17:52.632061Z",
- "iopub.status.idle": "2024-02-07T22:17:52.635961Z",
- "shell.execute_reply": "2024-02-07T22:17:52.635445Z"
+ "iopub.execute_input": "2024-02-07T23:58:15.125002Z",
+ "iopub.status.busy": "2024-02-07T23:58:15.124688Z",
+ "iopub.status.idle": "2024-02-07T23:58:15.128438Z",
+ "shell.execute_reply": "2024-02-07T23:58:15.127995Z"
}
},
"outputs": [
@@ -247,10 +247,10 @@
"id": "25ebe22a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:17:52.637990Z",
- "iopub.status.busy": "2024-02-07T22:17:52.637625Z",
- "iopub.status.idle": "2024-02-07T22:17:52.641010Z",
- "shell.execute_reply": "2024-02-07T22:17:52.640595Z"
+ "iopub.execute_input": "2024-02-07T23:58:15.130385Z",
+ "iopub.status.busy": "2024-02-07T23:58:15.130025Z",
+ "iopub.status.idle": "2024-02-07T23:58:15.133555Z",
+ "shell.execute_reply": "2024-02-07T23:58:15.133040Z"
}
},
"outputs": [
@@ -290,10 +290,10 @@
"id": "3faedea9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:17:52.642975Z",
- "iopub.status.busy": "2024-02-07T22:17:52.642693Z",
- "iopub.status.idle": "2024-02-07T22:17:52.645515Z",
- "shell.execute_reply": "2024-02-07T22:17:52.645069Z"
+ "iopub.execute_input": "2024-02-07T23:58:15.135489Z",
+ "iopub.status.busy": "2024-02-07T23:58:15.135128Z",
+ "iopub.status.idle": "2024-02-07T23:58:15.137853Z",
+ "shell.execute_reply": "2024-02-07T23:58:15.137431Z"
}
},
"outputs": [],
@@ -333,17 +333,17 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:17:52.647312Z",
- "iopub.status.busy": "2024-02-07T22:17:52.647133Z",
- "iopub.status.idle": "2024-02-07T22:19:07.736286Z",
- "shell.execute_reply": "2024-02-07T22:19:07.735682Z"
+ "iopub.execute_input": "2024-02-07T23:58:15.139805Z",
+ "iopub.status.busy": "2024-02-07T23:58:15.139476Z",
+ "iopub.status.idle": "2024-02-07T23:59:30.237567Z",
+ "shell.execute_reply": "2024-02-07T23:59:30.236895Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f8e42fb70e364942b5126777ae7364b8",
+ "model_id": "516f602a1da94152b495bff09963ecc2",
"version_major": 2,
"version_minor": 0
},
@@ -357,7 +357,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "be506591e8ac431dabddd430053816e2",
+ "model_id": "04cf97e28dc54e9e8ad9b5cad5a1f640",
"version_major": 2,
"version_minor": 0
},
@@ -400,10 +400,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:19:07.738877Z",
- "iopub.status.busy": "2024-02-07T22:19:07.738502Z",
- "iopub.status.idle": "2024-02-07T22:19:08.410129Z",
- "shell.execute_reply": "2024-02-07T22:19:08.409587Z"
+ "iopub.execute_input": "2024-02-07T23:59:30.240593Z",
+ "iopub.status.busy": "2024-02-07T23:59:30.240063Z",
+ "iopub.status.idle": "2024-02-07T23:59:30.904928Z",
+ "shell.execute_reply": "2024-02-07T23:59:30.904345Z"
}
},
"outputs": [
@@ -446,10 +446,10 @@
"id": "57fed473",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:19:08.412426Z",
- "iopub.status.busy": "2024-02-07T22:19:08.411976Z",
- "iopub.status.idle": "2024-02-07T22:19:11.136068Z",
- "shell.execute_reply": "2024-02-07T22:19:11.135474Z"
+ "iopub.execute_input": "2024-02-07T23:59:30.907247Z",
+ "iopub.status.busy": "2024-02-07T23:59:30.906730Z",
+ "iopub.status.idle": "2024-02-07T23:59:33.588948Z",
+ "shell.execute_reply": "2024-02-07T23:59:33.588459Z"
}
},
"outputs": [
@@ -519,10 +519,10 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:19:11.138343Z",
- "iopub.status.busy": "2024-02-07T22:19:11.138010Z",
- "iopub.status.idle": "2024-02-07T22:19:43.883956Z",
- "shell.execute_reply": "2024-02-07T22:19:43.883330Z"
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@@ -4710,6 +4684,32 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb
index 12339b227..76a108569 100644
--- a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb
@@ -112,10 +112,10 @@
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@@ -125,7 +125,7 @@
"dependencies = [\"cleanlab\"]\n",
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -383,10 +383,10 @@
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@@ -480,10 +480,10 @@
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@@ -604,10 +604,10 @@
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@@ -632,10 +632,10 @@
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@@ -690,10 +690,10 @@
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@@ -714,10 +714,10 @@
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@@ -737,10 +737,10 @@
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@@ -770,10 +770,10 @@
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- "shell.execute_reply": "2024-02-07T22:20:20.121770Z"
+ "iopub.execute_input": "2024-02-08T00:00:42.035536Z",
+ "iopub.status.busy": "2024-02-08T00:00:42.035222Z",
+ "iopub.status.idle": "2024-02-08T00:00:42.066169Z",
+ "shell.execute_reply": "2024-02-08T00:00:42.065646Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/text.ipynb b/master/.doctrees/nbsphinx/tutorials/text.ipynb
index 14b0188fb..a33793a5f 100644
--- a/master/.doctrees/nbsphinx/tutorials/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/text.ipynb
@@ -114,10 +114,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:23.034108Z",
- "iopub.status.busy": "2024-02-07T22:20:23.033949Z",
- "iopub.status.idle": "2024-02-07T22:20:25.644096Z",
- "shell.execute_reply": "2024-02-07T22:20:25.643492Z"
+ "iopub.execute_input": "2024-02-08T00:00:44.559964Z",
+ "iopub.status.busy": "2024-02-08T00:00:44.559768Z",
+ "iopub.status.idle": "2024-02-08T00:00:47.083313Z",
+ "shell.execute_reply": "2024-02-08T00:00:47.082784Z"
},
"nbsphinx": "hidden"
},
@@ -134,7 +134,7 @@
"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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -159,10 +159,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:25.646761Z",
- "iopub.status.busy": "2024-02-07T22:20:25.646232Z",
- "iopub.status.idle": "2024-02-07T22:20:25.649662Z",
- "shell.execute_reply": "2024-02-07T22:20:25.649125Z"
+ "iopub.execute_input": "2024-02-08T00:00:47.085963Z",
+ "iopub.status.busy": "2024-02-08T00:00:47.085456Z",
+ "iopub.status.idle": "2024-02-08T00:00:47.088646Z",
+ "shell.execute_reply": "2024-02-08T00:00:47.088220Z"
}
},
"outputs": [],
@@ -184,10 +184,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:25.651772Z",
- "iopub.status.busy": "2024-02-07T22:20:25.651399Z",
- "iopub.status.idle": "2024-02-07T22:20:25.654323Z",
- "shell.execute_reply": "2024-02-07T22:20:25.653903Z"
+ "iopub.execute_input": "2024-02-08T00:00:47.090601Z",
+ "iopub.status.busy": "2024-02-08T00:00:47.090279Z",
+ "iopub.status.idle": "2024-02-08T00:00:47.093338Z",
+ "shell.execute_reply": "2024-02-08T00:00:47.092797Z"
},
"nbsphinx": "hidden"
},
@@ -218,10 +218,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:25.656427Z",
- "iopub.status.busy": "2024-02-07T22:20:25.656112Z",
- "iopub.status.idle": "2024-02-07T22:20:25.696715Z",
- "shell.execute_reply": "2024-02-07T22:20:25.696251Z"
+ "iopub.execute_input": "2024-02-08T00:00:47.095320Z",
+ "iopub.status.busy": "2024-02-08T00:00:47.095018Z",
+ "iopub.status.idle": "2024-02-08T00:00:47.117162Z",
+ "shell.execute_reply": "2024-02-08T00:00:47.116660Z"
}
},
"outputs": [
@@ -311,10 +311,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:25.698892Z",
- "iopub.status.busy": "2024-02-07T22:20:25.698436Z",
- "iopub.status.idle": "2024-02-07T22:20:25.701940Z",
- "shell.execute_reply": "2024-02-07T22:20:25.701520Z"
+ "iopub.execute_input": "2024-02-08T00:00:47.119103Z",
+ "iopub.status.busy": "2024-02-08T00:00:47.118781Z",
+ "iopub.status.idle": "2024-02-08T00:00:47.122228Z",
+ "shell.execute_reply": "2024-02-08T00:00:47.121782Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:25.703894Z",
- "iopub.status.busy": "2024-02-07T22:20:25.703566Z",
- "iopub.status.idle": "2024-02-07T22:20:25.706909Z",
- "shell.execute_reply": "2024-02-07T22:20:25.706467Z"
+ "iopub.execute_input": "2024-02-08T00:00:47.124229Z",
+ "iopub.status.busy": "2024-02-08T00:00:47.123895Z",
+ "iopub.status.idle": "2024-02-08T00:00:47.127280Z",
+ "shell.execute_reply": "2024-02-08T00:00:47.126831Z"
}
},
"outputs": [
@@ -341,7 +341,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'change_pin', 'card_about_to_expire', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'cancel_transfer', 'lost_or_stolen_phone', 'visa_or_mastercard', 'beneficiary_not_allowed', 'getting_spare_card'}\n"
+ "Classes: {'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'getting_spare_card', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'card_about_to_expire', 'cancel_transfer', 'card_payment_fee_charged', 'change_pin', 'visa_or_mastercard'}\n"
]
}
],
@@ -364,10 +364,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:25.708983Z",
- "iopub.status.busy": "2024-02-07T22:20:25.708666Z",
- "iopub.status.idle": "2024-02-07T22:20:25.711641Z",
- "shell.execute_reply": "2024-02-07T22:20:25.711082Z"
+ "iopub.execute_input": "2024-02-08T00:00:47.129237Z",
+ "iopub.status.busy": "2024-02-08T00:00:47.128922Z",
+ "iopub.status.idle": "2024-02-08T00:00:47.132004Z",
+ "shell.execute_reply": "2024-02-08T00:00:47.131534Z"
}
},
"outputs": [
@@ -408,10 +408,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:25.713626Z",
- "iopub.status.busy": "2024-02-07T22:20:25.713301Z",
- "iopub.status.idle": "2024-02-07T22:20:25.716436Z",
- "shell.execute_reply": "2024-02-07T22:20:25.716011Z"
+ "iopub.execute_input": "2024-02-08T00:00:47.133964Z",
+ "iopub.status.busy": "2024-02-08T00:00:47.133652Z",
+ "iopub.status.idle": "2024-02-08T00:00:47.136698Z",
+ "shell.execute_reply": "2024-02-08T00:00:47.136289Z"
}
},
"outputs": [],
@@ -452,10 +452,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:25.718429Z",
- "iopub.status.busy": "2024-02-07T22:20:25.718116Z",
- "iopub.status.idle": "2024-02-07T22:20:29.429090Z",
- "shell.execute_reply": "2024-02-07T22:20:29.428554Z"
+ "iopub.execute_input": "2024-02-08T00:00:47.138634Z",
+ "iopub.status.busy": "2024-02-08T00:00:47.138333Z",
+ "iopub.status.idle": "2024-02-08T00:00:50.729967Z",
+ "shell.execute_reply": "2024-02-08T00:00:50.729315Z"
}
},
"outputs": [
@@ -510,10 +510,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:29.431649Z",
- "iopub.status.busy": "2024-02-07T22:20:29.431416Z",
- "iopub.status.idle": "2024-02-07T22:20:29.434206Z",
- "shell.execute_reply": "2024-02-07T22:20:29.433681Z"
+ "iopub.execute_input": "2024-02-08T00:00:50.732784Z",
+ "iopub.status.busy": "2024-02-08T00:00:50.732432Z",
+ "iopub.status.idle": "2024-02-08T00:00:50.735259Z",
+ "shell.execute_reply": "2024-02-08T00:00:50.734700Z"
}
},
"outputs": [],
@@ -535,10 +535,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:29.436191Z",
- "iopub.status.busy": "2024-02-07T22:20:29.435874Z",
- "iopub.status.idle": "2024-02-07T22:20:29.438880Z",
- "shell.execute_reply": "2024-02-07T22:20:29.438478Z"
+ "iopub.execute_input": "2024-02-08T00:00:50.737214Z",
+ "iopub.status.busy": "2024-02-08T00:00:50.736909Z",
+ "iopub.status.idle": "2024-02-08T00:00:50.739581Z",
+ "shell.execute_reply": "2024-02-08T00:00:50.739056Z"
}
},
"outputs": [],
@@ -553,10 +553,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:29.440688Z",
- "iopub.status.busy": "2024-02-07T22:20:29.440515Z",
- "iopub.status.idle": "2024-02-07T22:20:31.731024Z",
- "shell.execute_reply": "2024-02-07T22:20:31.730409Z"
+ "iopub.execute_input": "2024-02-08T00:00:50.741554Z",
+ "iopub.status.busy": "2024-02-08T00:00:50.741265Z",
+ "iopub.status.idle": "2024-02-08T00:00:52.955848Z",
+ "shell.execute_reply": "2024-02-08T00:00:52.955096Z"
},
"scrolled": true
},
@@ -579,10 +579,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:31.733921Z",
- "iopub.status.busy": "2024-02-07T22:20:31.733231Z",
- "iopub.status.idle": "2024-02-07T22:20:31.740520Z",
- "shell.execute_reply": "2024-02-07T22:20:31.740077Z"
+ "iopub.execute_input": "2024-02-08T00:00:52.958573Z",
+ "iopub.status.busy": "2024-02-08T00:00:52.958020Z",
+ "iopub.status.idle": "2024-02-08T00:00:52.965371Z",
+ "shell.execute_reply": "2024-02-08T00:00:52.964862Z"
}
},
"outputs": [
@@ -683,10 +683,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:31.742564Z",
- "iopub.status.busy": "2024-02-07T22:20:31.742256Z",
- "iopub.status.idle": "2024-02-07T22:20:31.745803Z",
- "shell.execute_reply": "2024-02-07T22:20:31.745375Z"
+ "iopub.execute_input": "2024-02-08T00:00:52.967398Z",
+ "iopub.status.busy": "2024-02-08T00:00:52.967027Z",
+ "iopub.status.idle": "2024-02-08T00:00:52.970968Z",
+ "shell.execute_reply": "2024-02-08T00:00:52.970539Z"
}
},
"outputs": [],
@@ -700,10 +700,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:31.747786Z",
- "iopub.status.busy": "2024-02-07T22:20:31.747469Z",
- "iopub.status.idle": "2024-02-07T22:20:31.750335Z",
- "shell.execute_reply": "2024-02-07T22:20:31.749834Z"
+ "iopub.execute_input": "2024-02-08T00:00:52.972799Z",
+ "iopub.status.busy": "2024-02-08T00:00:52.972623Z",
+ "iopub.status.idle": "2024-02-08T00:00:52.975944Z",
+ "shell.execute_reply": "2024-02-08T00:00:52.975459Z"
}
},
"outputs": [
@@ -738,10 +738,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:31.752406Z",
- "iopub.status.busy": "2024-02-07T22:20:31.752088Z",
- "iopub.status.idle": "2024-02-07T22:20:31.754796Z",
- "shell.execute_reply": "2024-02-07T22:20:31.754356Z"
+ "iopub.execute_input": "2024-02-08T00:00:52.978048Z",
+ "iopub.status.busy": "2024-02-08T00:00:52.977628Z",
+ "iopub.status.idle": "2024-02-08T00:00:52.980927Z",
+ "shell.execute_reply": "2024-02-08T00:00:52.980389Z"
}
},
"outputs": [],
@@ -761,10 +761,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:31.756820Z",
- "iopub.status.busy": "2024-02-07T22:20:31.756510Z",
- "iopub.status.idle": "2024-02-07T22:20:31.763078Z",
- "shell.execute_reply": "2024-02-07T22:20:31.762532Z"
+ "iopub.execute_input": "2024-02-08T00:00:52.983081Z",
+ "iopub.status.busy": "2024-02-08T00:00:52.982752Z",
+ "iopub.status.idle": "2024-02-08T00:00:52.989594Z",
+ "shell.execute_reply": "2024-02-08T00:00:52.989175Z"
}
},
"outputs": [
@@ -889,10 +889,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:31.765349Z",
- "iopub.status.busy": "2024-02-07T22:20:31.764941Z",
- "iopub.status.idle": "2024-02-07T22:20:31.990470Z",
- "shell.execute_reply": "2024-02-07T22:20:31.989930Z"
+ "iopub.execute_input": "2024-02-08T00:00:52.991604Z",
+ "iopub.status.busy": "2024-02-08T00:00:52.991288Z",
+ "iopub.status.idle": "2024-02-08T00:00:53.215514Z",
+ "shell.execute_reply": "2024-02-08T00:00:53.214999Z"
},
"scrolled": true
},
@@ -931,10 +931,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:31.993806Z",
- "iopub.status.busy": "2024-02-07T22:20:31.992872Z",
- "iopub.status.idle": "2024-02-07T22:20:32.169989Z",
- "shell.execute_reply": "2024-02-07T22:20:32.169440Z"
+ "iopub.execute_input": "2024-02-08T00:00:53.217866Z",
+ "iopub.status.busy": "2024-02-08T00:00:53.217475Z",
+ "iopub.status.idle": "2024-02-08T00:00:53.396736Z",
+ "shell.execute_reply": "2024-02-08T00:00:53.396217Z"
},
"scrolled": true
},
@@ -967,10 +967,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:32.173948Z",
- "iopub.status.busy": "2024-02-07T22:20:32.172981Z",
- "iopub.status.idle": "2024-02-07T22:20:32.177939Z",
- "shell.execute_reply": "2024-02-07T22:20:32.177455Z"
+ "iopub.execute_input": "2024-02-08T00:00:53.399160Z",
+ "iopub.status.busy": "2024-02-08T00:00:53.398767Z",
+ "iopub.status.idle": "2024-02-08T00:00:53.402668Z",
+ "shell.execute_reply": "2024-02-08T00:00:53.402187Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
index 408427f4d..552855170 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": "2024-02-07T22:20:35.313990Z",
- "iopub.status.busy": "2024-02-07T22:20:35.313816Z",
- "iopub.status.idle": "2024-02-07T22:20:36.922275Z",
- "shell.execute_reply": "2024-02-07T22:20:36.921658Z"
+ "iopub.execute_input": "2024-02-08T00:00:56.196767Z",
+ "iopub.status.busy": "2024-02-08T00:00:56.196596Z",
+ "iopub.status.idle": "2024-02-08T00:00:57.333399Z",
+ "shell.execute_reply": "2024-02-08T00:00:57.332829Z"
}
},
"outputs": [
@@ -86,7 +86,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-02-07 22:20:35-- https://data.deepai.org/conll2003.zip\r\n",
+ "--2024-02-08 00:00:56-- https://data.deepai.org/conll2003.zip\r\n",
"Resolving data.deepai.org (data.deepai.org)... "
]
},
@@ -94,8 +94,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "185.93.1.249, 2400:52e0:1a00::845:1\r\n",
- "Connecting to data.deepai.org (data.deepai.org)|185.93.1.249|:443... connected.\r\n"
+ "169.150.236.100, 2400:52e0:1a00::1069:1\r\n",
+ "Connecting to data.deepai.org (data.deepai.org)|169.150.236.100|:443... connected.\r\n"
]
},
{
@@ -122,9 +122,9 @@
"output_type": "stream",
"text": [
"\r",
- "conll2003.zip 100%[===================>] 959.94K 5.80MB/s in 0.2s \r\n",
+ "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n",
"\r\n",
- "2024-02-07 22:20:35 (5.80 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+ "2024-02-08 00:00:56 (6.83 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
"\r\n",
"mkdir: cannot create directory ‘data’: File exists\r\n"
]
@@ -136,14 +136,7 @@
"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"
]
},
@@ -151,9 +144,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-02-07 22:20:36-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
- "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.29.33, 52.216.44.121, 3.5.9.100, ...\r\n",
- "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.29.33|:443... connected.\r\n",
+ "--2024-02-08 00:00:56-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+ "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.195.65, 52.216.34.89, 52.217.194.57, ...\r\n",
+ "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.195.65|:443... connected.\r\n",
"HTTP request sent, awaiting response... "
]
},
@@ -174,18 +167,10 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 32%[=====> ] 5.32M 26.6MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 96%[==================> ] 15.71M 39.0MB/s \r",
- "pred_probs.npz 100%[===================>] 16.26M 40.1MB/s in 0.4s \r\n",
+ "pred_probs.npz 96%[==================> ] 15.71M 73.8MB/s \r",
+ "pred_probs.npz 100%[===================>] 16.26M 75.2MB/s in 0.2s \r\n",
"\r\n",
- "2024-02-07 22:20:36 (40.1 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+ "2024-02-08 00:00:57 (75.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
"\r\n"
]
}
@@ -202,10 +187,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:36.924800Z",
- "iopub.status.busy": "2024-02-07T22:20:36.924609Z",
- "iopub.status.idle": "2024-02-07T22:20:37.975651Z",
- "shell.execute_reply": "2024-02-07T22:20:37.975124Z"
+ "iopub.execute_input": "2024-02-08T00:00:57.335654Z",
+ "iopub.status.busy": "2024-02-08T00:00:57.335468Z",
+ "iopub.status.idle": "2024-02-08T00:00:58.349948Z",
+ "shell.execute_reply": "2024-02-08T00:00:58.349412Z"
},
"nbsphinx": "hidden"
},
@@ -216,7 +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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -242,10 +227,10 @@
"id": "a1349304",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:37.978094Z",
- "iopub.status.busy": "2024-02-07T22:20:37.977720Z",
- "iopub.status.idle": "2024-02-07T22:20:37.981475Z",
- "shell.execute_reply": "2024-02-07T22:20:37.981015Z"
+ "iopub.execute_input": "2024-02-08T00:00:58.352415Z",
+ "iopub.status.busy": "2024-02-08T00:00:58.352000Z",
+ "iopub.status.idle": "2024-02-08T00:00:58.355349Z",
+ "shell.execute_reply": "2024-02-08T00:00:58.354896Z"
}
},
"outputs": [],
@@ -295,10 +280,10 @@
"id": "ab9d59a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:37.983434Z",
- "iopub.status.busy": "2024-02-07T22:20:37.983151Z",
- "iopub.status.idle": "2024-02-07T22:20:37.986159Z",
- "shell.execute_reply": "2024-02-07T22:20:37.985712Z"
+ "iopub.execute_input": "2024-02-08T00:00:58.357557Z",
+ "iopub.status.busy": "2024-02-08T00:00:58.357166Z",
+ "iopub.status.idle": "2024-02-08T00:00:58.360004Z",
+ "shell.execute_reply": "2024-02-08T00:00:58.359557Z"
},
"nbsphinx": "hidden"
},
@@ -316,10 +301,10 @@
"id": "519cb80c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:37.988170Z",
- "iopub.status.busy": "2024-02-07T22:20:37.987840Z",
- "iopub.status.idle": "2024-02-07T22:20:47.095100Z",
- "shell.execute_reply": "2024-02-07T22:20:47.094496Z"
+ "iopub.execute_input": "2024-02-08T00:00:58.362059Z",
+ "iopub.status.busy": "2024-02-08T00:00:58.361747Z",
+ "iopub.status.idle": "2024-02-08T00:01:07.375886Z",
+ "shell.execute_reply": "2024-02-08T00:01:07.375279Z"
}
},
"outputs": [],
@@ -393,10 +378,10 @@
"id": "202f1526",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:47.097690Z",
- "iopub.status.busy": "2024-02-07T22:20:47.097345Z",
- "iopub.status.idle": "2024-02-07T22:20:47.103018Z",
- "shell.execute_reply": "2024-02-07T22:20:47.102553Z"
+ "iopub.execute_input": "2024-02-08T00:01:07.378420Z",
+ "iopub.status.busy": "2024-02-08T00:01:07.378100Z",
+ "iopub.status.idle": "2024-02-08T00:01:07.384174Z",
+ "shell.execute_reply": "2024-02-08T00:01:07.383731Z"
},
"nbsphinx": "hidden"
},
@@ -436,10 +421,10 @@
"id": "a4381f03",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:47.104811Z",
- "iopub.status.busy": "2024-02-07T22:20:47.104636Z",
- "iopub.status.idle": "2024-02-07T22:20:47.452942Z",
- "shell.execute_reply": "2024-02-07T22:20:47.452409Z"
+ "iopub.execute_input": "2024-02-08T00:01:07.386092Z",
+ "iopub.status.busy": "2024-02-08T00:01:07.385770Z",
+ "iopub.status.idle": "2024-02-08T00:01:07.712835Z",
+ "shell.execute_reply": "2024-02-08T00:01:07.712280Z"
}
},
"outputs": [],
@@ -476,10 +461,10 @@
"id": "7842e4a3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:47.455228Z",
- "iopub.status.busy": "2024-02-07T22:20:47.455038Z",
- "iopub.status.idle": "2024-02-07T22:20:47.459456Z",
- "shell.execute_reply": "2024-02-07T22:20:47.458974Z"
+ "iopub.execute_input": "2024-02-08T00:01:07.715171Z",
+ "iopub.status.busy": "2024-02-08T00:01:07.714983Z",
+ "iopub.status.idle": "2024-02-08T00:01:07.719127Z",
+ "shell.execute_reply": "2024-02-08T00:01:07.718612Z"
}
},
"outputs": [
@@ -551,10 +536,10 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:47.461295Z",
- "iopub.status.busy": "2024-02-07T22:20:47.461140Z",
- "iopub.status.idle": "2024-02-07T22:20:49.817383Z",
- "shell.execute_reply": "2024-02-07T22:20:49.816736Z"
+ "iopub.execute_input": "2024-02-08T00:01:07.721205Z",
+ "iopub.status.busy": "2024-02-08T00:01:07.720894Z",
+ "iopub.status.idle": "2024-02-08T00:01:09.991203Z",
+ "shell.execute_reply": "2024-02-08T00:01:09.990397Z"
}
},
"outputs": [],
@@ -576,10 +561,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:49.820516Z",
- "iopub.status.busy": "2024-02-07T22:20:49.819779Z",
- "iopub.status.idle": "2024-02-07T22:20:49.823692Z",
- "shell.execute_reply": "2024-02-07T22:20:49.823151Z"
+ "iopub.execute_input": "2024-02-08T00:01:09.994297Z",
+ "iopub.status.busy": "2024-02-08T00:01:09.993597Z",
+ "iopub.status.idle": "2024-02-08T00:01:09.997604Z",
+ "shell.execute_reply": "2024-02-08T00:01:09.997062Z"
}
},
"outputs": [
@@ -615,10 +600,10 @@
"id": "e13de188",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:49.825615Z",
- "iopub.status.busy": "2024-02-07T22:20:49.825439Z",
- "iopub.status.idle": "2024-02-07T22:20:49.830967Z",
- "shell.execute_reply": "2024-02-07T22:20:49.830515Z"
+ "iopub.execute_input": "2024-02-08T00:01:09.999626Z",
+ "iopub.status.busy": "2024-02-08T00:01:09.999251Z",
+ "iopub.status.idle": "2024-02-08T00:01:10.004932Z",
+ "shell.execute_reply": "2024-02-08T00:01:10.004388Z"
}
},
"outputs": [
@@ -796,10 +781,10 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:49.833007Z",
- "iopub.status.busy": "2024-02-07T22:20:49.832708Z",
- "iopub.status.idle": "2024-02-07T22:20:49.858073Z",
- "shell.execute_reply": "2024-02-07T22:20:49.857632Z"
+ "iopub.execute_input": "2024-02-08T00:01:10.007156Z",
+ "iopub.status.busy": "2024-02-08T00:01:10.006650Z",
+ "iopub.status.idle": "2024-02-08T00:01:10.031991Z",
+ "shell.execute_reply": "2024-02-08T00:01:10.031546Z"
}
},
"outputs": [
@@ -901,10 +886,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:49.859992Z",
- "iopub.status.busy": "2024-02-07T22:20:49.859817Z",
- "iopub.status.idle": "2024-02-07T22:20:49.863878Z",
- "shell.execute_reply": "2024-02-07T22:20:49.863329Z"
+ "iopub.execute_input": "2024-02-08T00:01:10.034012Z",
+ "iopub.status.busy": "2024-02-08T00:01:10.033696Z",
+ "iopub.status.idle": "2024-02-08T00:01:10.037460Z",
+ "shell.execute_reply": "2024-02-08T00:01:10.036924Z"
}
},
"outputs": [
@@ -978,10 +963,10 @@
"id": "db0b5179",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:49.865694Z",
- "iopub.status.busy": "2024-02-07T22:20:49.865524Z",
- "iopub.status.idle": "2024-02-07T22:20:51.295184Z",
- "shell.execute_reply": "2024-02-07T22:20:51.294642Z"
+ "iopub.execute_input": "2024-02-08T00:01:10.039392Z",
+ "iopub.status.busy": "2024-02-08T00:01:10.039074Z",
+ "iopub.status.idle": "2024-02-08T00:01:11.408481Z",
+ "shell.execute_reply": "2024-02-08T00:01:11.407944Z"
}
},
"outputs": [
@@ -1153,10 +1138,10 @@
"id": "a18795eb",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-07T22:20:51.297251Z",
- "iopub.status.busy": "2024-02-07T22:20:51.297061Z",
- "iopub.status.idle": "2024-02-07T22:20:51.301790Z",
- "shell.execute_reply": "2024-02-07T22:20:51.301246Z"
+ "iopub.execute_input": "2024-02-08T00:01:11.410630Z",
+ "iopub.status.busy": "2024-02-08T00:01:11.410275Z",
+ "iopub.status.idle": "2024-02-08T00:01:11.414236Z",
+ "shell.execute_reply": "2024-02-08T00:01:11.413795Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/tutorials/audio.doctree b/master/.doctrees/tutorials/audio.doctree
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diff --git a/master/.doctrees/tutorials/multilabel_classification.doctree b/master/.doctrees/tutorials/multilabel_classification.doctree
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index 138868692..8b4925deb 100644
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index e17d6bce8..2082b306c 100644
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index 5461b94d3..468214b53 100644
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diff --git a/master/.doctrees/tutorials/tabular.doctree b/master/.doctrees/tutorials/tabular.doctree
index 9f171f425..ffb7a6aec 100644
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diff --git a/master/.doctrees/tutorials/text.doctree b/master/.doctrees/tutorials/text.doctree
index 853fa8f9f..dd4cea2a6 100644
Binary files a/master/.doctrees/tutorials/text.doctree and b/master/.doctrees/tutorials/text.doctree differ
diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree
index 339f73c03..90d01e1ab 100644
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diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb
index e1e7b3580..7df3c5f84 100644
--- a/master/_sources/tutorials/audio.ipynb
+++ b/master/_sources/tutorials/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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 d84dc1be3..de35e30ef 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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 6939de730..caf107126 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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 ec836b743..c4931019a 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -81,7 +81,7 @@
"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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 b78fee999..4f154be30 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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 5b779141e..5701497fd 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -77,7 +77,7 @@
"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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 a1bb4c374..c98db501c 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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 2931c3f3b..a5da30c44 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -96,7 +96,7 @@
"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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 496ff82ba..374b56832 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -19,7 +19,7 @@
"Quickstart\n",
"
\n",
" \n",
- "cleanlab finds label issues based on two inputs: `labels` formatted as a list of lists of integer class indices that apply to each example in your dataset, and `pred_probs` from a trained multi-label classification model (which do not need to sum to 1 since the classes are not mutually exclusive). Once you have these, run the code below to find label issues in your multi-label dataset:\n",
+ "cleanlab finds data/label issues based on two inputs: `labels` formatted as a list of lists of integer class indices that apply to each example in your dataset, and `pred_probs` from a trained multi-label classification model (which do not need to sum to 1 since the classes are not mutually exclusive). Once you have these, run the code below to find issues in your multi-label dataset:\n",
"\n",
"
\n",
" \n",
@@ -28,10 +28,10 @@
"\n",
"# Assuming your dataset has a label column named 'label'\n",
"lab = Datalab(dataset, label_name='label', task='multilabel')\n",
+ "# To detect more issue types, optionally supply `features` (numeric dataset values or model embeddings of the data)\n",
+ "lab.find_issues(pred_probs=pred_probs, features=features)\n",
"\n",
- "lab.find_issues(pred_probs=pred_probs, issue_types={\"label\": {}})\n",
- "\n",
- "ranked_label_issues = lab.get_issues(\"label\").sort_values(\"label_score\")\n",
+ "lab.report()\n",
"```\n",
"\n",
" \n",
@@ -73,7 +73,7 @@
"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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -553,6 +553,16 @@
"print(f\"Label quality scores of the first 10 examples in dataset:\\n{scores[:10]}\")"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "d65af827-aeda-4b6b-9ae7-b1f0b84700d6",
+ "metadata": {},
+ "source": [
+ "While this tutorial focused on label issues, cleanlab's `Datalab` object can automatically detect many other types of issues in your dataset (outliers, near duplicates, etc).\n",
+ "Simply remove the `issue_types` argument from the above call to `Datalab.find_issues()` above and `Datalab` will more comprehensively audit your dataset.\n",
+ "Refer to our [Datalab quickstart tutorial](./datalab/datalab_quickstart.html) to learn how to interpret the results (the interpretation remains mostly the same across different types of ML tasks)."
+ ]
+ },
{
"cell_type": "markdown",
"id": "d65af827-aeda-4b6b-9ae7-b1f0b84700d5",
diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb
index 18e5c833d..1fed0cdaa 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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 206ce0d9c..cd3edb0ed 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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 5e6ecaaa9..e2ffc2a97 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -103,7 +103,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 928c700d4..0bf8faadf 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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/tabular.ipynb b/master/_sources/tutorials/tabular.ipynb
index 0e04ce609..382d08d62 100644
--- a/master/_sources/tutorials/tabular.ipynb
+++ b/master/_sources/tutorials/tabular.ipynb
@@ -119,7 +119,7 @@
"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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/text.ipynb b/master/_sources/tutorials/text.ipynb
index 11a5c643d..b8651e3af 100644
--- a/master/_sources/tutorials/text.ipynb
+++ b/master/_sources/tutorials/text.ipynb
@@ -128,7 +128,7 @@
"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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 2cb39ad6b..2328cbcda 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@387ffa3504f24ddd5ac58ebdfa7d39c6ccb44623\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@077f5a936954c203fc8740fefd9eeda606f26f5d\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 5b7f7c3eb..07cbc934a 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
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Use cleanlab to find issues in your dataset": [[79, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[79, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[80, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[80, "Install-dependencies-and-import-them"], [83, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[80, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[80, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[81, "FAQ"]], "What data can cleanlab detect issues in?": [[81, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[81, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[81, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[81, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[81, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[81, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[81, "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?": [[81, "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?": [[81, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[81, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[81, "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?": [[81, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[81, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[81, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[82, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[82, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[82, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[82, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[82, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[82, "7.-Use-cleanlab-to-find-issues"]], "View report": [[82, "View-report"]], "View most likely examples with label errors": [[82, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[82, "View-most-severe-outliers"]], "View sets of near duplicate images": [[82, "View-sets-of-near-duplicate-images"]], "Dark images": [[82, "Dark-images"]], "View top examples of dark images": [[82, "View-top-examples-of-dark-images"]], "Low information images": [[82, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[83, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[83, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[83, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[83, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[83, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[83, "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.": [[83, "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": [[83, "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": [[83, "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!": [[83, "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": [[83, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[83, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[83, "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)": [[83, "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:": [[83, "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": [[83, "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.": [[83, "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.": [[83, "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.": [[83, "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.": [[83, "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?": [[83, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[83, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[84, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[85, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[85, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[85, "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": [[85, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[85, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[85, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[85, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[85, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[85, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[86, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[86, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[86, "2.-Format-data,-labels,-and-model-predictions"], [87, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[86, "3.-Use-cleanlab-to-find-label-issues"], [87, "3.-Use-cleanlab-to-find-label-issues"], [91, "3.-Use-cleanlab-to-find-label-issues"], [94, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[86, "Label-quality-scores"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[86, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Finding Label Errors in Object Detection Datasets": [[87, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[87, "1.-Install-required-dependencies-and-download-data"], [91, "1.-Install-required-dependencies-and-download-data"], [94, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[87, "Get-label-quality-scores"], [91, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[87, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[87, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[87, "Other-uses-of-visualize"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[88, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[88, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[88, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[88, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[88, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[88, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[89, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[89, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[89, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[90, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[90, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[90, "4.-Train-a-more-robust-model-from-noisy-labels"], [93, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[90, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[91, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[91, "2.-Get-data,-labels,-and-pred_probs"], [94, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[91, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[91, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[91, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[92, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[92, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[92, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[93, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[93, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[94, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[94, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[94, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[94, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[94, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.datalab.datalab"], [9, 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"cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[10, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[10, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[10, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[10, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[10, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[10, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[10, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[10, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[10, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[10, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[10, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[10, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[10, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[10, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[10, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[10, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[11, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in 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"set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[12, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[13, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[14, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[17, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[18, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[20, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[21, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[22, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[22, 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Use cleanlab to find issues in your dataset": [[79, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[79, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[80, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[80, "Install-dependencies-and-import-them"], [83, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[80, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[80, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[81, "FAQ"]], "What data can cleanlab detect issues in?": [[81, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[81, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[81, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[81, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[81, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[81, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[81, "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?": [[81, "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?": [[81, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[81, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[81, "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?": [[81, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[81, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[81, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[82, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[82, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[82, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[82, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[82, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[82, "7.-Use-cleanlab-to-find-issues"]], "View report": [[82, "View-report"]], "View most likely examples with label errors": [[82, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[82, "View-most-severe-outliers"]], "View sets of near duplicate images": [[82, "View-sets-of-near-duplicate-images"]], "Dark images": [[82, "Dark-images"]], "View top examples of dark images": [[82, "View-top-examples-of-dark-images"]], "Low information images": [[82, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[83, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[83, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[83, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[83, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[83, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[83, "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.": [[83, "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": [[83, "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": [[83, "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!": [[83, "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": [[83, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[83, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[83, "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)": [[83, "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:": [[83, "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": [[83, "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.": [[83, "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.": [[83, "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.": [[83, "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.": [[83, "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?": [[83, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[83, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[84, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[85, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[85, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[85, "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": [[85, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[85, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[85, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[85, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[85, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[85, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[86, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[86, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[86, "2.-Format-data,-labels,-and-model-predictions"], [87, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[86, "3.-Use-cleanlab-to-find-label-issues"], [87, "3.-Use-cleanlab-to-find-label-issues"], [91, "3.-Use-cleanlab-to-find-label-issues"], [94, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[86, "Label-quality-scores"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[86, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Finding Label Errors in Object Detection Datasets": [[87, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[87, "1.-Install-required-dependencies-and-download-data"], [91, "1.-Install-required-dependencies-and-download-data"], [94, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[87, "Get-label-quality-scores"], [91, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[87, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[87, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[87, "Other-uses-of-visualize"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[88, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[88, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[88, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[88, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[88, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[88, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[89, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[89, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[89, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[90, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[90, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[90, "4.-Train-a-more-robust-model-from-noisy-labels"], [93, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[90, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[91, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[91, "2.-Get-data,-labels,-and-pred_probs"], [94, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[91, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[91, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[91, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[92, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[92, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[92, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[93, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[93, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[94, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[94, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[94, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[94, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[94, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.datalab.datalab"], [9, 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"cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[10, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[10, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[10, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[10, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[10, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[10, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[10, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[10, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[10, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[10, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[10, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[10, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[10, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[10, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[10, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[10, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[11, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in 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"set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[12, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[13, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[14, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[17, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[18, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[20, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[21, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[22, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[22, 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module cleanlab.object_detection.summary)": [[58, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[58, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[58, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[59, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[59, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[59, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[59, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[59, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[60, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[60, "cleanlab.rank.find_top_issues"]], 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cleanlab.token_classification.summary)": [[71, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[71, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[71, "cleanlab.token_classification.summary.filter_by_token"]]}})
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diff --git a/master/tutorials/audio.html b/master/tutorials/audio.html
index bf1bb0225..41c5d0795 100644
--- a/master/tutorials/audio.html
+++ b/master/tutorials/audio.html
@@ -1274,7 +1274,7 @@
5. Use cleanlab to find label issues