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diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle
index 9a3aebce5..709e2a87a 100644
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diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree
index ac1eecb0d..813266fcf 100644
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diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
index cc8956d28..f3d888536 100644
--- a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:27.766466Z",
- "iopub.status.busy": "2024-06-25T19:31:27.766073Z",
- "iopub.status.idle": "2024-06-25T19:31:28.950995Z",
- "shell.execute_reply": "2024-06-25T19:31:28.950453Z"
+ "iopub.execute_input": "2024-06-25T23:13:19.683650Z",
+ "iopub.status.busy": "2024-06-25T23:13:19.683483Z",
+ "iopub.status.idle": "2024-06-25T23:13:20.876411Z",
+ "shell.execute_reply": "2024-06-25T23:13:20.875863Z"
},
"nbsphinx": "hidden"
},
@@ -126,7 +126,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:28.953618Z",
- "iopub.status.busy": "2024-06-25T19:31:28.953345Z",
- "iopub.status.idle": "2024-06-25T19:31:28.970797Z",
- "shell.execute_reply": "2024-06-25T19:31:28.970252Z"
+ "iopub.execute_input": "2024-06-25T23:13:20.879016Z",
+ "iopub.status.busy": "2024-06-25T23:13:20.878582Z",
+ "iopub.status.idle": "2024-06-25T23:13:20.895831Z",
+ "shell.execute_reply": "2024-06-25T23:13:20.895402Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:28.973223Z",
- "iopub.status.busy": "2024-06-25T19:31:28.972835Z",
- "iopub.status.idle": "2024-06-25T19:31:29.167625Z",
- "shell.execute_reply": "2024-06-25T19:31:29.167053Z"
+ "iopub.execute_input": "2024-06-25T23:13:20.897855Z",
+ "iopub.status.busy": "2024-06-25T23:13:20.897628Z",
+ "iopub.status.idle": "2024-06-25T23:13:21.010572Z",
+ "shell.execute_reply": "2024-06-25T23:13:21.009996Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:29.197486Z",
- "iopub.status.busy": "2024-06-25T19:31:29.197079Z",
- "iopub.status.idle": "2024-06-25T19:31:29.200622Z",
- "shell.execute_reply": "2024-06-25T19:31:29.200145Z"
+ "iopub.execute_input": "2024-06-25T23:13:21.037181Z",
+ "iopub.status.busy": "2024-06-25T23:13:21.036568Z",
+ "iopub.status.idle": "2024-06-25T23:13:21.040405Z",
+ "shell.execute_reply": "2024-06-25T23:13:21.039967Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:29.202620Z",
- "iopub.status.busy": "2024-06-25T19:31:29.202441Z",
- "iopub.status.idle": "2024-06-25T19:31:29.210646Z",
- "shell.execute_reply": "2024-06-25T19:31:29.210233Z"
+ "iopub.execute_input": "2024-06-25T23:13:21.042333Z",
+ "iopub.status.busy": "2024-06-25T23:13:21.042161Z",
+ "iopub.status.idle": "2024-06-25T23:13:21.050408Z",
+ "shell.execute_reply": "2024-06-25T23:13:21.049993Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:29.212637Z",
- "iopub.status.busy": "2024-06-25T19:31:29.212443Z",
- "iopub.status.idle": "2024-06-25T19:31:29.214911Z",
- "shell.execute_reply": "2024-06-25T19:31:29.214495Z"
+ "iopub.execute_input": "2024-06-25T23:13:21.052411Z",
+ "iopub.status.busy": "2024-06-25T23:13:21.052111Z",
+ "iopub.status.idle": "2024-06-25T23:13:21.054810Z",
+ "shell.execute_reply": "2024-06-25T23:13:21.054263Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:29.216761Z",
- "iopub.status.busy": "2024-06-25T19:31:29.216593Z",
- "iopub.status.idle": "2024-06-25T19:31:29.731597Z",
- "shell.execute_reply": "2024-06-25T19:31:29.730952Z"
+ "iopub.execute_input": "2024-06-25T23:13:21.056799Z",
+ "iopub.status.busy": "2024-06-25T23:13:21.056479Z",
+ "iopub.status.idle": "2024-06-25T23:13:21.584928Z",
+ "shell.execute_reply": "2024-06-25T23:13:21.584385Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:29.733935Z",
- "iopub.status.busy": "2024-06-25T19:31:29.733740Z",
- "iopub.status.idle": "2024-06-25T19:31:31.552423Z",
- "shell.execute_reply": "2024-06-25T19:31:31.551801Z"
+ "iopub.execute_input": "2024-06-25T23:13:21.587427Z",
+ "iopub.status.busy": "2024-06-25T23:13:21.587080Z",
+ "iopub.status.idle": "2024-06-25T23:13:23.402116Z",
+ "shell.execute_reply": "2024-06-25T23:13:23.401472Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:31.554814Z",
- "iopub.status.busy": "2024-06-25T19:31:31.554296Z",
- "iopub.status.idle": "2024-06-25T19:31:31.564323Z",
- "shell.execute_reply": "2024-06-25T19:31:31.563854Z"
+ "iopub.execute_input": "2024-06-25T23:13:23.404837Z",
+ "iopub.status.busy": "2024-06-25T23:13:23.404191Z",
+ "iopub.status.idle": "2024-06-25T23:13:23.414068Z",
+ "shell.execute_reply": "2024-06-25T23:13:23.413559Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:31.566389Z",
- "iopub.status.busy": "2024-06-25T19:31:31.566065Z",
- "iopub.status.idle": "2024-06-25T19:31:31.570002Z",
- "shell.execute_reply": "2024-06-25T19:31:31.569569Z"
+ "iopub.execute_input": "2024-06-25T23:13:23.416257Z",
+ "iopub.status.busy": "2024-06-25T23:13:23.415941Z",
+ "iopub.status.idle": "2024-06-25T23:13:23.420056Z",
+ "shell.execute_reply": "2024-06-25T23:13:23.419521Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:31.572029Z",
- "iopub.status.busy": "2024-06-25T19:31:31.571709Z",
- "iopub.status.idle": "2024-06-25T19:31:31.579030Z",
- "shell.execute_reply": "2024-06-25T19:31:31.578475Z"
+ "iopub.execute_input": "2024-06-25T23:13:23.422287Z",
+ "iopub.status.busy": "2024-06-25T23:13:23.421904Z",
+ "iopub.status.idle": "2024-06-25T23:13:23.429186Z",
+ "shell.execute_reply": "2024-06-25T23:13:23.428630Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:31.581187Z",
- "iopub.status.busy": "2024-06-25T19:31:31.580887Z",
- "iopub.status.idle": "2024-06-25T19:31:31.691824Z",
- "shell.execute_reply": "2024-06-25T19:31:31.691204Z"
+ "iopub.execute_input": "2024-06-25T23:13:23.431342Z",
+ "iopub.status.busy": "2024-06-25T23:13:23.431023Z",
+ "iopub.status.idle": "2024-06-25T23:13:23.542534Z",
+ "shell.execute_reply": "2024-06-25T23:13:23.542044Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:31.694170Z",
- "iopub.status.busy": "2024-06-25T19:31:31.693686Z",
- "iopub.status.idle": "2024-06-25T19:31:31.696628Z",
- "shell.execute_reply": "2024-06-25T19:31:31.696102Z"
+ "iopub.execute_input": "2024-06-25T23:13:23.544624Z",
+ "iopub.status.busy": "2024-06-25T23:13:23.544286Z",
+ "iopub.status.idle": "2024-06-25T23:13:23.546943Z",
+ "shell.execute_reply": "2024-06-25T23:13:23.546515Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:31.698847Z",
- "iopub.status.busy": "2024-06-25T19:31:31.698415Z",
- "iopub.status.idle": "2024-06-25T19:31:33.679358Z",
- "shell.execute_reply": "2024-06-25T19:31:33.678623Z"
+ "iopub.execute_input": "2024-06-25T23:13:23.548943Z",
+ "iopub.status.busy": "2024-06-25T23:13:23.548635Z",
+ "iopub.status.idle": "2024-06-25T23:13:25.510005Z",
+ "shell.execute_reply": "2024-06-25T23:13:25.509395Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:33.682516Z",
- "iopub.status.busy": "2024-06-25T19:31:33.681890Z",
- "iopub.status.idle": "2024-06-25T19:31:33.693245Z",
- "shell.execute_reply": "2024-06-25T19:31:33.692694Z"
+ "iopub.execute_input": "2024-06-25T23:13:25.513097Z",
+ "iopub.status.busy": "2024-06-25T23:13:25.512371Z",
+ "iopub.status.idle": "2024-06-25T23:13:25.523496Z",
+ "shell.execute_reply": "2024-06-25T23:13:25.522944Z"
}
},
"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:33.695397Z",
- "iopub.status.busy": "2024-06-25T19:31:33.695096Z",
- "iopub.status.idle": "2024-06-25T19:31:33.841440Z",
- "shell.execute_reply": "2024-06-25T19:31:33.840949Z"
+ "iopub.execute_input": "2024-06-25T23:13:25.525641Z",
+ "iopub.status.busy": "2024-06-25T23:13:25.525323Z",
+ "iopub.status.idle": "2024-06-25T23:13:25.545176Z",
+ "shell.execute_reply": "2024-06-25T23:13:25.544739Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
index a83013185..9af680b6f 100644
--- a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
@@ -115,10 +115,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:37.218802Z",
- "iopub.status.busy": "2024-06-25T19:31:37.218626Z",
- "iopub.status.idle": "2024-06-25T19:31:40.132819Z",
- "shell.execute_reply": "2024-06-25T19:31:40.132198Z"
+ "iopub.execute_input": "2024-06-25T23:13:28.905676Z",
+ "iopub.status.busy": "2024-06-25T23:13:28.905503Z",
+ "iopub.status.idle": "2024-06-25T23:13:31.555296Z",
+ "shell.execute_reply": "2024-06-25T23:13:31.554730Z"
},
"nbsphinx": "hidden"
},
@@ -135,7 +135,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -160,10 +160,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:40.135382Z",
- "iopub.status.busy": "2024-06-25T19:31:40.135098Z",
- "iopub.status.idle": "2024-06-25T19:31:40.138344Z",
- "shell.execute_reply": "2024-06-25T19:31:40.137917Z"
+ "iopub.execute_input": "2024-06-25T23:13:31.557860Z",
+ "iopub.status.busy": "2024-06-25T23:13:31.557469Z",
+ "iopub.status.idle": "2024-06-25T23:13:31.560897Z",
+ "shell.execute_reply": "2024-06-25T23:13:31.560352Z"
}
},
"outputs": [],
@@ -185,10 +185,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:40.140291Z",
- "iopub.status.busy": "2024-06-25T19:31:40.139985Z",
- "iopub.status.idle": "2024-06-25T19:31:40.143618Z",
- "shell.execute_reply": "2024-06-25T19:31:40.143162Z"
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@@ -342,7 +342,7 @@
"output_type": "stream",
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"This dataset has 10 classes.\n",
- "Classes: {'beneficiary_not_allowed', 'card_about_to_expire', 'getting_spare_card', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'change_pin', 'apple_pay_or_google_pay', 'cancel_transfer', 'lost_or_stolen_phone', 'visa_or_mastercard'}\n"
+ "Classes: {'card_about_to_expire', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'getting_spare_card', 'visa_or_mastercard', 'cancel_transfer', 'lost_or_stolen_phone', 'change_pin', 'card_payment_fee_charged'}\n"
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- "ff59fb88d4aa46d481bfabbfb12c3c08": {
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@@ -3632,6 +3591,47 @@
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"width": null
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+ "fd9acfb5facf4aeaa71d2088d344d085": {
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+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
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+ "_view_module": "@jupyter-widgets/base",
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+ "value": "pytorch_model.bin: 100%"
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
index 411158583..d40b1db54 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
@@ -78,10 +78,10 @@
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@@ -97,7 +97,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -131,10 +131,10 @@
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@@ -157,10 +157,10 @@
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@@ -208,10 +208,10 @@
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@@ -242,10 +242,10 @@
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@@ -329,10 +329,10 @@
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@@ -380,10 +380,10 @@
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@@ -435,10 +435,10 @@
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@@ -474,10 +474,10 @@
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@@ -557,10 +557,10 @@
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@@ -582,10 +582,10 @@
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@@ -627,10 +627,10 @@
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@@ -727,10 +727,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
index 33af481ea..17bb19429 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
@@ -80,10 +80,10 @@
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@@ -93,7 +93,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
index 701b2fb18..4fb163767 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
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@@ -91,7 +91,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
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@@ -1578,10 +1578,10 @@
"execution_count": 17,
"metadata": {
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
index a549a7040..da3ecdeb8 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
@@ -71,10 +71,10 @@
"execution_count": 1,
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- "shell.execute_reply": "2024-06-25T19:32:37.482729Z"
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+ "shell.execute_reply": "2024-06-25T23:14:25.155231Z"
},
"nbsphinx": "hidden"
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@@ -112,10 +112,10 @@
"execution_count": 2,
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+ "shell.execute_reply": "2024-06-25T23:14:25.161043Z"
}
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"outputs": [],
@@ -152,10 +152,10 @@
"execution_count": 3,
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+ "shell.execute_reply": "2024-06-25T23:14:35.756685Z"
}
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@@ -172,7 +172,7 @@
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- "model_id": "e85af83531bc4182b052d4cfe7f1020e",
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@@ -186,7 +186,7 @@
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+ "model_id": "cacaca4358c34e93a46a3e2019d188d4",
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@@ -200,7 +200,7 @@
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@@ -214,7 +214,7 @@
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+ "model_id": "cc7010cd50844e48a3db713a6ea5f850",
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@@ -228,7 +228,7 @@
{
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+ "model_id": "1e806f052f23419ba6ec80aa76644ed5",
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@@ -242,7 +242,7 @@
{
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+ "model_id": "3590fcc9756749e0b9130b8809114216",
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@@ -256,7 +256,7 @@
{
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+ "model_id": "489746a2a7db4406b7ebfd5f2a155361",
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@@ -270,7 +270,7 @@
{
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@@ -312,10 +312,10 @@
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@@ -340,17 +340,17 @@
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+ "shell.execute_reply": "2024-06-25T23:14:46.666518Z"
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+ "model_id": "e075f5bd416a447eb67433e0d225370f",
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@@ -388,10 +388,10 @@
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@@ -424,10 +424,10 @@
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@@ -465,10 +465,10 @@
"execution_count": 8,
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@@ -605,10 +605,10 @@
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@@ -733,10 +733,10 @@
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@@ -773,10 +773,10 @@
"execution_count": 11,
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+ "shell.execute_reply": "2024-06-25T23:15:37.032511Z"
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"outputs": [
@@ -792,21 +792,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.704\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.649\n"
]
},
{
"name": "stdout",
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"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.525\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.481\n",
"Computing feature embeddings ...\n"
]
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@@ -827,7 +827,7 @@
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@@ -850,21 +850,21 @@
"name": "stdout",
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"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.714\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.663\n"
]
},
{
"name": "stdout",
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"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.460\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.663\n",
"Computing feature embeddings ...\n"
]
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@@ -885,7 +885,7 @@
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@@ -908,21 +908,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.742\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.680\n"
]
},
{
"name": "stdout",
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"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.468\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.450\n",
"Computing feature embeddings ...\n"
]
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@@ -943,7 +943,7 @@
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@@ -1022,10 +1022,10 @@
"execution_count": 12,
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- "shell.execute_reply": "2024-06-25T19:33:54.943339Z"
+ "iopub.execute_input": "2024-06-25T23:15:37.035751Z",
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+ "shell.execute_reply": "2024-06-25T23:15:37.049035Z"
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@@ -1050,10 +1050,10 @@
"execution_count": 13,
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- "shell.execute_reply": "2024-06-25T19:33:55.402981Z"
+ "iopub.execute_input": "2024-06-25T23:15:37.051460Z",
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+ "shell.execute_reply": "2024-06-25T23:15:37.533181Z"
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@@ -1073,10 +1073,10 @@
"execution_count": 14,
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- "iopub.execute_input": "2024-06-25T19:33:55.406220Z",
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- "shell.execute_reply": "2024-06-25T19:35:30.534808Z"
+ "iopub.execute_input": "2024-06-25T23:15:37.536010Z",
+ "iopub.status.busy": "2024-06-25T23:15:37.535826Z",
+ "iopub.status.idle": "2024-06-25T23:17:13.081610Z",
+ "shell.execute_reply": "2024-06-25T23:17:13.080989Z"
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@@ -1123,7 +1123,7 @@
{
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+ "model_id": "55c0a386d760485f92009bb75259396b",
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@@ -1162,10 +1162,10 @@
"execution_count": 15,
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- "shell.execute_reply": "2024-06-25T19:35:30.983121Z"
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+ "shell.execute_reply": "2024-06-25T23:17:13.530038Z"
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@@ -1311,10 +1311,10 @@
"execution_count": 16,
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@@ -1418,10 +1418,10 @@
"execution_count": 17,
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+ "shell.execute_reply": "2024-06-25T23:17:13.606218Z"
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@@ -1551,10 +1551,10 @@
"execution_count": 18,
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+ "iopub.execute_input": "2024-06-25T23:17:13.609099Z",
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@@ -1600,10 +1600,10 @@
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@@ -2248,47 +2248,47 @@
" \n",
" \n",
" | \n",
- " dark_score | \n",
" is_dark_issue | \n",
+ " dark_score | \n",
"
\n",
" \n",
"
\n",
" \n",
" 34848 | \n",
- " 0.203922 | \n",
" True | \n",
+ " 0.203922 | \n",
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\n",
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" 50270 | \n",
- " 0.204588 | \n",
" True | \n",
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" 3936 | \n",
- " 0.213098 | \n",
" True | \n",
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" True | \n",
+ " 0.217686 | \n",
"
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" \n",
" 8094 | \n",
- " 0.230118 | \n",
" True | \n",
+ " 0.230118 | \n",
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""
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- " dark_score is_dark_issue\n",
- "34848 0.203922 True\n",
- "50270 0.204588 True\n",
- "3936 0.213098 True\n",
- "733 0.217686 True\n",
- "8094 0.230118 True"
+ " is_dark_issue dark_score\n",
+ "34848 True 0.203922\n",
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+ "3936 True 0.213098\n",
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@@ -2351,10 +2351,10 @@
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@@ -2464,47 +2464,47 @@
" \n",
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" | \n",
- " is_low_information_issue | \n",
" low_information_score | \n",
+ " is_low_information_issue | \n",
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\n",
" \n",
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" 53050 | \n",
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" 40875 | \n",
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" 9594 | \n",
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" 34825 | \n",
- " True | \n",
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+ " True | \n",
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" 37530 | \n",
- " True | \n",
" 0.108516 | \n",
+ " True | \n",
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- "40875 True 0.089929\n",
- "9594 True 0.092601\n",
- "34825 True 0.107744\n",
- "37530 True 0.108516"
+ " low_information_score is_low_information_issue\n",
+ "53050 0.067975 True\n",
+ "40875 0.089929 True\n",
+ "9594 0.092601 True\n",
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@@ -2525,10 +2525,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
index d470496b0..7f5df08d9 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
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"dependencies = [\"cleanlab\", \"datasets\"]\n",
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
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- "iopub.execute_input": "2024-06-25T19:35:40.866333Z",
- "iopub.status.busy": "2024-06-25T19:35:40.865865Z",
- "iopub.status.idle": "2024-06-25T19:35:40.875269Z",
- "shell.execute_reply": "2024-06-25T19:35:40.874719Z"
+ "iopub.execute_input": "2024-06-25T23:17:23.606640Z",
+ "iopub.status.busy": "2024-06-25T23:17:23.606173Z",
+ "iopub.status.idle": "2024-06-25T23:17:23.615532Z",
+ "shell.execute_reply": "2024-06-25T23:17:23.614991Z"
}
},
"outputs": [],
@@ -436,10 +436,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:40.877815Z",
- "iopub.status.busy": "2024-06-25T19:35:40.877412Z",
- "iopub.status.idle": "2024-06-25T19:35:42.759452Z",
- "shell.execute_reply": "2024-06-25T19:35:42.758773Z"
+ "iopub.execute_input": "2024-06-25T23:17:23.617787Z",
+ "iopub.status.busy": "2024-06-25T23:17:23.617408Z",
+ "iopub.status.idle": "2024-06-25T23:17:25.503397Z",
+ "shell.execute_reply": "2024-06-25T23:17:25.502726Z"
}
},
"outputs": [
@@ -484,10 +484,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:42.761931Z",
- "iopub.status.busy": "2024-06-25T19:35:42.761498Z",
- "iopub.status.idle": "2024-06-25T19:35:42.780225Z",
- "shell.execute_reply": "2024-06-25T19:35:42.779777Z"
+ "iopub.execute_input": "2024-06-25T23:17:25.506132Z",
+ "iopub.status.busy": "2024-06-25T23:17:25.505476Z",
+ "iopub.status.idle": "2024-06-25T23:17:25.524117Z",
+ "shell.execute_reply": "2024-06-25T23:17:25.523676Z"
},
"scrolled": true
},
@@ -617,10 +617,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:42.782418Z",
- "iopub.status.busy": "2024-06-25T19:35:42.782011Z",
- "iopub.status.idle": "2024-06-25T19:35:42.789930Z",
- "shell.execute_reply": "2024-06-25T19:35:42.789485Z"
+ "iopub.execute_input": "2024-06-25T23:17:25.526096Z",
+ "iopub.status.busy": "2024-06-25T23:17:25.525830Z",
+ "iopub.status.idle": "2024-06-25T23:17:25.533770Z",
+ "shell.execute_reply": "2024-06-25T23:17:25.533230Z"
}
},
"outputs": [
@@ -724,10 +724,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:42.791897Z",
- "iopub.status.busy": "2024-06-25T19:35:42.791568Z",
- "iopub.status.idle": "2024-06-25T19:35:42.800098Z",
- "shell.execute_reply": "2024-06-25T19:35:42.799646Z"
+ "iopub.execute_input": "2024-06-25T23:17:25.535755Z",
+ "iopub.status.busy": "2024-06-25T23:17:25.535435Z",
+ "iopub.status.idle": "2024-06-25T23:17:25.544816Z",
+ "shell.execute_reply": "2024-06-25T23:17:25.544397Z"
}
},
"outputs": [
@@ -856,10 +856,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:42.802085Z",
- "iopub.status.busy": "2024-06-25T19:35:42.801908Z",
- "iopub.status.idle": "2024-06-25T19:35:42.809958Z",
- "shell.execute_reply": "2024-06-25T19:35:42.809510Z"
+ "iopub.execute_input": "2024-06-25T23:17:25.546828Z",
+ "iopub.status.busy": "2024-06-25T23:17:25.546524Z",
+ "iopub.status.idle": "2024-06-25T23:17:25.554523Z",
+ "shell.execute_reply": "2024-06-25T23:17:25.554077Z"
}
},
"outputs": [
@@ -973,10 +973,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:42.811794Z",
- "iopub.status.busy": "2024-06-25T19:35:42.811623Z",
- "iopub.status.idle": "2024-06-25T19:35:42.820374Z",
- "shell.execute_reply": "2024-06-25T19:35:42.819927Z"
+ "iopub.execute_input": "2024-06-25T23:17:25.556497Z",
+ "iopub.status.busy": "2024-06-25T23:17:25.556176Z",
+ "iopub.status.idle": "2024-06-25T23:17:25.564618Z",
+ "shell.execute_reply": "2024-06-25T23:17:25.564170Z"
}
},
"outputs": [
@@ -1087,10 +1087,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:42.822187Z",
- "iopub.status.busy": "2024-06-25T19:35:42.822017Z",
- "iopub.status.idle": "2024-06-25T19:35:42.829544Z",
- "shell.execute_reply": "2024-06-25T19:35:42.829102Z"
+ "iopub.execute_input": "2024-06-25T23:17:25.566583Z",
+ "iopub.status.busy": "2024-06-25T23:17:25.566262Z",
+ "iopub.status.idle": "2024-06-25T23:17:25.573703Z",
+ "shell.execute_reply": "2024-06-25T23:17:25.573162Z"
}
},
"outputs": [
@@ -1205,10 +1205,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:42.831790Z",
- "iopub.status.busy": "2024-06-25T19:35:42.831383Z",
- "iopub.status.idle": "2024-06-25T19:35:42.838578Z",
- "shell.execute_reply": "2024-06-25T19:35:42.838124Z"
+ "iopub.execute_input": "2024-06-25T23:17:25.575840Z",
+ "iopub.status.busy": "2024-06-25T23:17:25.575524Z",
+ "iopub.status.idle": "2024-06-25T23:17:25.582660Z",
+ "shell.execute_reply": "2024-06-25T23:17:25.582224Z"
}
},
"outputs": [
@@ -1308,10 +1308,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:42.840523Z",
- "iopub.status.busy": "2024-06-25T19:35:42.840354Z",
- "iopub.status.idle": "2024-06-25T19:35:42.848877Z",
- "shell.execute_reply": "2024-06-25T19:35:42.848311Z"
+ "iopub.execute_input": "2024-06-25T23:17:25.584694Z",
+ "iopub.status.busy": "2024-06-25T23:17:25.584373Z",
+ "iopub.status.idle": "2024-06-25T23:17:25.592350Z",
+ "shell.execute_reply": "2024-06-25T23:17:25.591901Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
index 5e2df2074..47d0847e3 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
@@ -75,10 +75,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:45.390789Z",
- "iopub.status.busy": "2024-06-25T19:35:45.390619Z",
- "iopub.status.idle": "2024-06-25T19:35:48.008658Z",
- "shell.execute_reply": "2024-06-25T19:35:48.008097Z"
+ "iopub.execute_input": "2024-06-25T23:17:28.279893Z",
+ "iopub.status.busy": "2024-06-25T23:17:28.279723Z",
+ "iopub.status.idle": "2024-06-25T23:17:30.902204Z",
+ "shell.execute_reply": "2024-06-25T23:17:30.901649Z"
},
"nbsphinx": "hidden"
},
@@ -96,7 +96,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -121,10 +121,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:48.011088Z",
- "iopub.status.busy": "2024-06-25T19:35:48.010783Z",
- "iopub.status.idle": "2024-06-25T19:35:48.014230Z",
- "shell.execute_reply": "2024-06-25T19:35:48.013782Z"
+ "iopub.execute_input": "2024-06-25T23:17:30.904858Z",
+ "iopub.status.busy": "2024-06-25T23:17:30.904404Z",
+ "iopub.status.idle": "2024-06-25T23:17:30.907555Z",
+ "shell.execute_reply": "2024-06-25T23:17:30.907124Z"
}
},
"outputs": [],
@@ -145,10 +145,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:48.016267Z",
- "iopub.status.busy": "2024-06-25T19:35:48.015916Z",
- "iopub.status.idle": "2024-06-25T19:35:48.019094Z",
- "shell.execute_reply": "2024-06-25T19:35:48.018529Z"
+ "iopub.execute_input": "2024-06-25T23:17:30.909531Z",
+ "iopub.status.busy": "2024-06-25T23:17:30.909235Z",
+ "iopub.status.idle": "2024-06-25T23:17:30.912305Z",
+ "shell.execute_reply": "2024-06-25T23:17:30.911777Z"
},
"nbsphinx": "hidden"
},
@@ -178,10 +178,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:48.021232Z",
- "iopub.status.busy": "2024-06-25T19:35:48.020813Z",
- "iopub.status.idle": "2024-06-25T19:35:48.073023Z",
- "shell.execute_reply": "2024-06-25T19:35:48.072456Z"
+ "iopub.execute_input": "2024-06-25T23:17:30.914377Z",
+ "iopub.status.busy": "2024-06-25T23:17:30.913988Z",
+ "iopub.status.idle": "2024-06-25T23:17:30.934290Z",
+ "shell.execute_reply": "2024-06-25T23:17:30.933773Z"
}
},
"outputs": [
@@ -271,10 +271,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:48.075330Z",
- "iopub.status.busy": "2024-06-25T19:35:48.074995Z",
- "iopub.status.idle": "2024-06-25T19:35:48.078963Z",
- "shell.execute_reply": "2024-06-25T19:35:48.078513Z"
+ "iopub.execute_input": "2024-06-25T23:17:30.936266Z",
+ "iopub.status.busy": "2024-06-25T23:17:30.935961Z",
+ "iopub.status.idle": "2024-06-25T23:17:30.939627Z",
+ "shell.execute_reply": "2024-06-25T23:17:30.939095Z"
}
},
"outputs": [
@@ -283,7 +283,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'card_about_to_expire', 'visa_or_mastercard', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'getting_spare_card', 'cancel_transfer', 'change_pin', 'card_payment_fee_charged', 'supported_cards_and_currencies'}\n"
+ "Classes: {'beneficiary_not_allowed', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'card_about_to_expire', 'getting_spare_card', 'change_pin', 'card_payment_fee_charged', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'cancel_transfer'}\n"
]
}
],
@@ -307,10 +307,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:48.080913Z",
- "iopub.status.busy": "2024-06-25T19:35:48.080733Z",
- "iopub.status.idle": "2024-06-25T19:35:48.083997Z",
- "shell.execute_reply": "2024-06-25T19:35:48.083535Z"
+ "iopub.execute_input": "2024-06-25T23:17:30.941560Z",
+ "iopub.status.busy": "2024-06-25T23:17:30.941250Z",
+ "iopub.status.idle": "2024-06-25T23:17:30.944331Z",
+ "shell.execute_reply": "2024-06-25T23:17:30.943818Z"
}
},
"outputs": [
@@ -365,10 +365,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:48.086043Z",
- "iopub.status.busy": "2024-06-25T19:35:48.085869Z",
- "iopub.status.idle": "2024-06-25T19:35:52.539336Z",
- "shell.execute_reply": "2024-06-25T19:35:52.538772Z"
+ "iopub.execute_input": "2024-06-25T23:17:30.946381Z",
+ "iopub.status.busy": "2024-06-25T23:17:30.946063Z",
+ "iopub.status.idle": "2024-06-25T23:17:34.606408Z",
+ "shell.execute_reply": "2024-06-25T23:17:34.605752Z"
}
},
"outputs": [
@@ -424,10 +424,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:52.541851Z",
- "iopub.status.busy": "2024-06-25T19:35:52.541641Z",
- "iopub.status.idle": "2024-06-25T19:35:53.417381Z",
- "shell.execute_reply": "2024-06-25T19:35:53.416793Z"
+ "iopub.execute_input": "2024-06-25T23:17:34.609229Z",
+ "iopub.status.busy": "2024-06-25T23:17:34.608851Z",
+ "iopub.status.idle": "2024-06-25T23:17:35.466411Z",
+ "shell.execute_reply": "2024-06-25T23:17:35.465834Z"
},
"scrolled": true
},
@@ -459,10 +459,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:53.420304Z",
- "iopub.status.busy": "2024-06-25T19:35:53.419913Z",
- "iopub.status.idle": "2024-06-25T19:35:53.422789Z",
- "shell.execute_reply": "2024-06-25T19:35:53.422303Z"
+ "iopub.execute_input": "2024-06-25T23:17:35.469450Z",
+ "iopub.status.busy": "2024-06-25T23:17:35.469026Z",
+ "iopub.status.idle": "2024-06-25T23:17:35.471951Z",
+ "shell.execute_reply": "2024-06-25T23:17:35.471467Z"
}
},
"outputs": [],
@@ -482,10 +482,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:53.425167Z",
- "iopub.status.busy": "2024-06-25T19:35:53.424776Z",
- "iopub.status.idle": "2024-06-25T19:35:55.333188Z",
- "shell.execute_reply": "2024-06-25T19:35:55.332528Z"
+ "iopub.execute_input": "2024-06-25T23:17:35.474346Z",
+ "iopub.status.busy": "2024-06-25T23:17:35.473954Z",
+ "iopub.status.idle": "2024-06-25T23:17:37.379211Z",
+ "shell.execute_reply": "2024-06-25T23:17:37.378561Z"
},
"scrolled": true
},
@@ -537,10 +537,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:55.336733Z",
- "iopub.status.busy": "2024-06-25T19:35:55.336306Z",
- "iopub.status.idle": "2024-06-25T19:35:55.363099Z",
- "shell.execute_reply": "2024-06-25T19:35:55.362613Z"
+ "iopub.execute_input": "2024-06-25T23:17:37.383383Z",
+ "iopub.status.busy": "2024-06-25T23:17:37.382233Z",
+ "iopub.status.idle": "2024-06-25T23:17:37.408704Z",
+ "shell.execute_reply": "2024-06-25T23:17:37.408212Z"
},
"scrolled": true
},
@@ -670,10 +670,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:55.366640Z",
- "iopub.status.busy": "2024-06-25T19:35:55.365705Z",
- "iopub.status.idle": "2024-06-25T19:35:55.376030Z",
- "shell.execute_reply": "2024-06-25T19:35:55.375622Z"
+ "iopub.execute_input": "2024-06-25T23:17:37.412193Z",
+ "iopub.status.busy": "2024-06-25T23:17:37.411277Z",
+ "iopub.status.idle": "2024-06-25T23:17:37.421651Z",
+ "shell.execute_reply": "2024-06-25T23:17:37.421256Z"
},
"scrolled": true
},
@@ -783,10 +783,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:55.378837Z",
- "iopub.status.busy": "2024-06-25T19:35:55.378517Z",
- "iopub.status.idle": "2024-06-25T19:35:55.382599Z",
- "shell.execute_reply": "2024-06-25T19:35:55.382206Z"
+ "iopub.execute_input": "2024-06-25T23:17:37.424437Z",
+ "iopub.status.busy": "2024-06-25T23:17:37.423704Z",
+ "iopub.status.idle": "2024-06-25T23:17:37.428917Z",
+ "shell.execute_reply": "2024-06-25T23:17:37.428520Z"
}
},
"outputs": [
@@ -824,10 +824,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:55.384693Z",
- "iopub.status.busy": "2024-06-25T19:35:55.384439Z",
- "iopub.status.idle": "2024-06-25T19:35:55.390208Z",
- "shell.execute_reply": "2024-06-25T19:35:55.389819Z"
+ "iopub.execute_input": "2024-06-25T23:17:37.430883Z",
+ "iopub.status.busy": "2024-06-25T23:17:37.430707Z",
+ "iopub.status.idle": "2024-06-25T23:17:37.438445Z",
+ "shell.execute_reply": "2024-06-25T23:17:37.437883Z"
}
},
"outputs": [
@@ -944,10 +944,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:55.392385Z",
- "iopub.status.busy": "2024-06-25T19:35:55.392130Z",
- "iopub.status.idle": "2024-06-25T19:35:55.398230Z",
- "shell.execute_reply": "2024-06-25T19:35:55.397669Z"
+ "iopub.execute_input": "2024-06-25T23:17:37.440387Z",
+ "iopub.status.busy": "2024-06-25T23:17:37.440214Z",
+ "iopub.status.idle": "2024-06-25T23:17:37.446599Z",
+ "shell.execute_reply": "2024-06-25T23:17:37.446157Z"
}
},
"outputs": [
@@ -1030,10 +1030,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:55.400097Z",
- "iopub.status.busy": "2024-06-25T19:35:55.399777Z",
- "iopub.status.idle": "2024-06-25T19:35:55.405709Z",
- "shell.execute_reply": "2024-06-25T19:35:55.405249Z"
+ "iopub.execute_input": "2024-06-25T23:17:37.448520Z",
+ "iopub.status.busy": "2024-06-25T23:17:37.448196Z",
+ "iopub.status.idle": "2024-06-25T23:17:37.454046Z",
+ "shell.execute_reply": "2024-06-25T23:17:37.453485Z"
}
},
"outputs": [
@@ -1141,10 +1141,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:35:55.407786Z",
- "iopub.status.busy": "2024-06-25T19:35:55.407389Z",
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- "shell.execute_reply": "2024-06-25T19:35:55.415484Z"
+ "iopub.execute_input": "2024-06-25T23:17:37.456157Z",
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+ "shell.execute_reply": "2024-06-25T23:17:37.463796Z"
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"outputs": [
@@ -1255,10 +1255,10 @@
"execution_count": 18,
"metadata": {
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}
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"outputs": [
@@ -1326,10 +1326,10 @@
"execution_count": 19,
"metadata": {
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"outputs": [
@@ -1408,10 +1408,10 @@
"execution_count": 20,
"metadata": {
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"outputs": [
@@ -1459,10 +1459,10 @@
"execution_count": 21,
"metadata": {
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},
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},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
index 073e233c2..05570c79a 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
@@ -38,10 +38,10 @@
"execution_count": 1,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:35:59.467250Z",
- "iopub.status.busy": "2024-06-25T19:35:59.467073Z",
- "iopub.status.idle": "2024-06-25T19:35:59.885710Z",
- "shell.execute_reply": "2024-06-25T19:35:59.885107Z"
+ "iopub.execute_input": "2024-06-25T23:17:40.853361Z",
+ "iopub.status.busy": "2024-06-25T23:17:40.852930Z",
+ "iopub.status.idle": "2024-06-25T23:17:41.272322Z",
+ "shell.execute_reply": "2024-06-25T23:17:41.271713Z"
}
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"outputs": [],
@@ -87,10 +87,10 @@
"execution_count": 2,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:35:59.888637Z",
- "iopub.status.busy": "2024-06-25T19:35:59.888151Z",
- "iopub.status.idle": "2024-06-25T19:36:00.014649Z",
- "shell.execute_reply": "2024-06-25T19:36:00.014148Z"
+ "iopub.execute_input": "2024-06-25T23:17:41.275299Z",
+ "iopub.status.busy": "2024-06-25T23:17:41.274749Z",
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+ "shell.execute_reply": "2024-06-25T23:17:41.402663Z"
}
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"outputs": [
@@ -181,10 +181,10 @@
"execution_count": 3,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:00.016873Z",
- "iopub.status.busy": "2024-06-25T19:36:00.016623Z",
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- "shell.execute_reply": "2024-06-25T19:36:00.039305Z"
+ "iopub.execute_input": "2024-06-25T23:17:41.405438Z",
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+ "shell.execute_reply": "2024-06-25T23:17:41.427281Z"
}
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"outputs": [],
@@ -210,10 +210,10 @@
"execution_count": 4,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:00.042285Z",
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- "shell.execute_reply": "2024-06-25T19:36:02.696318Z"
+ "iopub.execute_input": "2024-06-25T23:17:41.430652Z",
+ "iopub.status.busy": "2024-06-25T23:17:41.430206Z",
+ "iopub.status.idle": "2024-06-25T23:17:44.079438Z",
+ "shell.execute_reply": "2024-06-25T23:17:44.078785Z"
}
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"outputs": [
@@ -716,10 +716,10 @@
"execution_count": 5,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:02.699546Z",
- "iopub.status.busy": "2024-06-25T19:36:02.698988Z",
- "iopub.status.idle": "2024-06-25T19:36:11.210546Z",
- "shell.execute_reply": "2024-06-25T19:36:11.209947Z"
+ "iopub.execute_input": "2024-06-25T23:17:44.082102Z",
+ "iopub.status.busy": "2024-06-25T23:17:44.081500Z",
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+ "shell.execute_reply": "2024-06-25T23:17:51.710550Z"
}
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"outputs": [
@@ -820,10 +820,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:11.212912Z",
- "iopub.status.busy": "2024-06-25T19:36:11.212489Z",
- "iopub.status.idle": "2024-06-25T19:36:11.354224Z",
- "shell.execute_reply": "2024-06-25T19:36:11.353605Z"
+ "iopub.execute_input": "2024-06-25T23:17:51.713313Z",
+ "iopub.status.busy": "2024-06-25T23:17:51.713127Z",
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+ "shell.execute_reply": "2024-06-25T23:17:51.856753Z"
}
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"outputs": [],
@@ -854,10 +854,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:11.356684Z",
- "iopub.status.busy": "2024-06-25T19:36:11.356497Z",
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- "shell.execute_reply": "2024-06-25T19:36:12.691867Z"
+ "iopub.execute_input": "2024-06-25T23:17:51.860009Z",
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+ "shell.execute_reply": "2024-06-25T23:17:53.181004Z"
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"outputs": [
@@ -1016,10 +1016,10 @@
"execution_count": 8,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:12.694507Z",
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- "shell.execute_reply": "2024-06-25T19:36:13.110403Z"
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+ "shell.execute_reply": "2024-06-25T23:17:53.613154Z"
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"outputs": [
@@ -1098,10 +1098,10 @@
"execution_count": 9,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:13.113354Z",
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- "shell.execute_reply": "2024-06-25T19:36:13.121426Z"
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"outputs": [],
@@ -1131,10 +1131,10 @@
"execution_count": 10,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:13.123927Z",
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- "shell.execute_reply": "2024-06-25T19:36:13.142805Z"
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+ "shell.execute_reply": "2024-06-25T23:17:53.646870Z"
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"outputs": [],
@@ -1162,10 +1162,10 @@
"execution_count": 11,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:13.145167Z",
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+ "shell.execute_reply": "2024-06-25T23:17:53.876376Z"
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"outputs": [],
@@ -1205,10 +1205,10 @@
"execution_count": 12,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:13.372709Z",
- "iopub.status.busy": "2024-06-25T19:36:13.372266Z",
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- "shell.execute_reply": "2024-06-25T19:36:13.390786Z"
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+ "shell.execute_reply": "2024-06-25T23:17:53.897956Z"
}
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"outputs": [
@@ -1406,10 +1406,10 @@
"execution_count": 13,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:13.393275Z",
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- "shell.execute_reply": "2024-06-25T19:36:13.561518Z"
+ "iopub.execute_input": "2024-06-25T23:17:53.900637Z",
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+ "shell.execute_reply": "2024-06-25T23:17:54.066325Z"
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"outputs": [
@@ -1476,10 +1476,10 @@
"execution_count": 14,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:13.564551Z",
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- "shell.execute_reply": "2024-06-25T19:36:13.573705Z"
+ "iopub.execute_input": "2024-06-25T23:17:54.069491Z",
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+ "shell.execute_reply": "2024-06-25T23:17:54.079594Z"
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"outputs": [
@@ -1745,10 +1745,10 @@
"execution_count": 15,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:13.576275Z",
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- "shell.execute_reply": "2024-06-25T19:36:13.584885Z"
+ "iopub.execute_input": "2024-06-25T23:17:54.083209Z",
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+ "shell.execute_reply": "2024-06-25T23:17:54.092040Z"
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"outputs": [
@@ -1935,10 +1935,10 @@
"execution_count": 16,
"metadata": {
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- "shell.execute_reply": "2024-06-25T19:36:13.628478Z"
+ "iopub.execute_input": "2024-06-25T23:17:54.094651Z",
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+ "shell.execute_reply": "2024-06-25T23:17:54.122177Z"
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"outputs": [],
@@ -1972,10 +1972,10 @@
"execution_count": 17,
"metadata": {
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- "shell.execute_reply": "2024-06-25T19:36:13.632931Z"
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+ "shell.execute_reply": "2024-06-25T23:17:54.130269Z"
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"outputs": [],
@@ -1997,10 +1997,10 @@
"execution_count": 18,
"metadata": {
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- "shell.execute_reply": "2024-06-25T19:36:13.654546Z"
+ "iopub.execute_input": "2024-06-25T23:17:54.132753Z",
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+ "shell.execute_reply": "2024-06-25T23:17:54.151107Z"
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"outputs": [
@@ -2158,10 +2158,10 @@
"execution_count": 19,
"metadata": {
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"outputs": [],
@@ -2194,10 +2194,10 @@
"execution_count": 20,
"metadata": {
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- "shell.execute_reply": "2024-06-25T19:36:13.690034Z"
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+ "shell.execute_reply": "2024-06-25T23:17:54.186748Z"
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"outputs": [
@@ -2343,10 +2343,10 @@
"execution_count": 21,
"metadata": {
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"outputs": [
@@ -2413,10 +2413,10 @@
"execution_count": 22,
"metadata": {
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"outputs": [
@@ -2467,10 +2467,10 @@
"execution_count": 23,
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"outputs": [
@@ -2749,10 +2749,10 @@
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@@ -3019,10 +3019,10 @@
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"metadata": {
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@@ -3047,10 +3047,10 @@
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@@ -3222,10 +3222,10 @@
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"metadata": {
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@@ -3257,10 +3257,10 @@
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@@ -3567,10 +3567,10 @@
"execution_count": 29,
"metadata": {
"execution": {
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- "iopub.status.busy": "2024-06-25T19:36:14.179018Z",
- "iopub.status.idle": "2024-06-25T19:36:14.184786Z",
- "shell.execute_reply": "2024-06-25T19:36:14.184224Z"
+ "iopub.execute_input": "2024-06-25T23:17:54.686913Z",
+ "iopub.status.busy": "2024-06-25T23:17:54.686475Z",
+ "iopub.status.idle": "2024-06-25T23:17:54.692178Z",
+ "shell.execute_reply": "2024-06-25T23:17:54.691645Z"
}
},
"outputs": [],
@@ -3609,10 +3609,10 @@
"execution_count": 30,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:14.186887Z",
- "iopub.status.busy": "2024-06-25T19:36:14.186471Z",
- "iopub.status.idle": "2024-06-25T19:36:14.196806Z",
- "shell.execute_reply": "2024-06-25T19:36:14.196244Z"
+ "iopub.execute_input": "2024-06-25T23:17:54.694316Z",
+ "iopub.status.busy": "2024-06-25T23:17:54.693981Z",
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+ "shell.execute_reply": "2024-06-25T23:17:54.704802Z"
}
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"outputs": [
@@ -3648,10 +3648,10 @@
"execution_count": 31,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:14.198752Z",
- "iopub.status.busy": "2024-06-25T19:36:14.198440Z",
- "iopub.status.idle": "2024-06-25T19:36:14.412825Z",
- "shell.execute_reply": "2024-06-25T19:36:14.412259Z"
+ "iopub.execute_input": "2024-06-25T23:17:54.707234Z",
+ "iopub.status.busy": "2024-06-25T23:17:54.707059Z",
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+ "shell.execute_reply": "2024-06-25T23:17:54.923350Z"
}
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"outputs": [
@@ -3703,10 +3703,10 @@
"execution_count": 32,
"metadata": {
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- "shell.execute_reply": "2024-06-25T19:36:14.421663Z"
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+ "shell.execute_reply": "2024-06-25T23:17:54.932869Z"
},
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diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
index 6a954c6b0..d462fdaea 100644
--- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
@@ -70,10 +70,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:17.536909Z",
- "iopub.status.busy": "2024-06-25T19:36:17.536739Z",
- "iopub.status.idle": "2024-06-25T19:36:18.659278Z",
- "shell.execute_reply": "2024-06-25T19:36:18.658730Z"
+ "iopub.execute_input": "2024-06-25T23:17:58.501344Z",
+ "iopub.status.busy": "2024-06-25T23:17:58.500004Z",
+ "iopub.status.idle": "2024-06-25T23:17:59.801482Z",
+ "shell.execute_reply": "2024-06-25T23:17:59.800950Z"
},
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@@ -85,7 +85,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -110,10 +110,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:18.661733Z",
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- "iopub.status.idle": "2024-06-25T19:36:18.664275Z",
- "shell.execute_reply": "2024-06-25T19:36:18.663748Z"
+ "iopub.execute_input": "2024-06-25T23:17:59.804004Z",
+ "iopub.status.busy": "2024-06-25T23:17:59.803708Z",
+ "iopub.status.idle": "2024-06-25T23:17:59.806736Z",
+ "shell.execute_reply": "2024-06-25T23:17:59.806281Z"
},
"id": "_UvI80l42iyi"
},
@@ -203,10 +203,10 @@
"execution_count": 3,
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- "iopub.status.busy": "2024-06-25T19:36:18.666027Z",
- "iopub.status.idle": "2024-06-25T19:36:18.678092Z",
- "shell.execute_reply": "2024-06-25T19:36:18.677567Z"
+ "iopub.execute_input": "2024-06-25T23:17:59.808933Z",
+ "iopub.status.busy": "2024-06-25T23:17:59.808705Z",
+ "iopub.status.idle": "2024-06-25T23:17:59.821999Z",
+ "shell.execute_reply": "2024-06-25T23:17:59.821381Z"
},
"nbsphinx": "hidden"
},
@@ -285,10 +285,10 @@
"execution_count": 4,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:18.680164Z",
- "iopub.status.busy": "2024-06-25T19:36:18.679860Z",
- "iopub.status.idle": "2024-06-25T19:36:28.874863Z",
- "shell.execute_reply": "2024-06-25T19:36:28.874371Z"
+ "iopub.execute_input": "2024-06-25T23:17:59.824481Z",
+ "iopub.status.busy": "2024-06-25T23:17:59.824047Z",
+ "iopub.status.idle": "2024-06-25T23:18:03.535596Z",
+ "shell.execute_reply": "2024-06-25T23:18:03.535061Z"
},
"id": "dhTHOg8Pyv5G"
},
@@ -694,13 +694,7 @@
"\n",
"\n",
"🎯 Mnist_test_set 🎯\n",
- "\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "\n",
"\n",
"Loaded the 'mnist_test_set' dataset with predicted probabilities of shape (10000, 10)\n",
"\n",
@@ -2565,7 +2559,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
" * Overall, about 18% (1,846 of the 10,000) labels in your dataset have potential issues.\n",
" ** The overall label health score for this dataset is: 0.82.\n",
"\n",
diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
index 649612439..713861397 100644
--- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
@@ -18,10 +18,10 @@
"id": "2a4efdde",
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- "shell.execute_reply": "2024-06-25T19:36:32.183056Z"
+ "iopub.execute_input": "2024-06-25T23:18:05.926443Z",
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+ "iopub.status.idle": "2024-06-25T23:18:07.103304Z",
+ "shell.execute_reply": "2024-06-25T23:18:07.102799Z"
},
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@@ -137,10 +137,10 @@
"id": "239d5ee7",
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- "shell.execute_reply": "2024-06-25T19:36:32.189148Z"
+ "iopub.execute_input": "2024-06-25T23:18:07.106148Z",
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+ "shell.execute_reply": "2024-06-25T23:18:07.108679Z"
}
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@@ -176,10 +176,10 @@
"id": "28b324aa",
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}
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@@ -202,10 +202,10 @@
"id": "28b324ab",
"metadata": {
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+ "shell.execute_reply": "2024-06-25T23:18:10.407723Z"
}
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@@ -228,10 +228,10 @@
"id": "90c10e18",
"metadata": {
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- "shell.execute_reply": "2024-06-25T19:36:35.488560Z"
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}
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@@ -253,10 +253,10 @@
"id": "88839519",
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}
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@@ -278,10 +278,10 @@
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@@ -363,10 +363,10 @@
"id": "41714b51",
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+ "shell.execute_reply": "2024-06-25T23:18:10.483202Z"
}
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@@ -380,7 +380,7 @@
{
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"application/vnd.jupyter.widget-view+json": {
- "model_id": "d8af54b634f1457680edc574c7fcb110",
+ "model_id": "558d7887a3b248ccbc78e41ae8f6a2ad",
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"version_minor": 0
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@@ -394,7 +394,7 @@
{
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+ "model_id": "633ecf7c235f443883ad78f8a1d748cd",
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@@ -452,10 +452,10 @@
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@@ -486,10 +486,10 @@
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@@ -512,10 +512,10 @@
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- "shell.execute_reply": "2024-06-25T19:36:35.551260Z"
+ "iopub.execute_input": "2024-06-25T23:18:10.502533Z",
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+ "shell.execute_reply": "2024-06-25T23:18:10.507959Z"
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@@ -565,10 +565,10 @@
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+ "iopub.execute_input": "2024-06-25T23:18:10.510615Z",
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@@ -585,10 +585,10 @@
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- "shell.execute_reply": "2024-06-25T19:36:35.627508Z"
+ "iopub.execute_input": "2024-06-25T23:18:10.548998Z",
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@@ -667,10 +667,10 @@
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@@ -737,10 +737,10 @@
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@@ -826,10 +826,10 @@
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@@ -1285,10 +1285,10 @@
"id": "af3052ac",
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"outputs": [
@@ -1319,7 +1319,7 @@
},
{
"cell_type": "markdown",
- "id": "91d13c0b",
+ "id": "411cb3b4",
"metadata": {},
"source": [
"### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?"
@@ -1327,7 +1327,7 @@
},
{
"cell_type": "markdown",
- "id": "838b0e29",
+ "id": "c0fc51ac",
"metadata": {},
"source": [
"The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n",
@@ -1338,7 +1338,7 @@
},
{
"cell_type": "markdown",
- "id": "72c82160",
+ "id": "31d0af7b",
"metadata": {},
"source": [
"### How to handle near-duplicate data identified by Datalab?\n",
@@ -1349,13 +1349,13 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "c8ef0e49",
+ "id": "ddefd054",
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@@ -1457,7 +1457,7 @@
},
{
"cell_type": "markdown",
- "id": "bfd8eea7",
+ "id": "96a1ec22",
"metadata": {},
"source": [
"The functions above collect sets of near-duplicate examples. Within each\n",
@@ -1472,13 +1472,13 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "7515c699",
+ "id": "d478ad17",
"metadata": {
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+ "shell.execute_reply": "2024-06-25T23:18:13.989245Z"
}
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@@ -1495,7 +1495,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_7655/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+ "/tmp/ipykernel_7878/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
" to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values\n"
]
}
@@ -1529,13 +1529,13 @@
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@@ -1683,7 +1751,7 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
index 3a6310e80..902b836cf 100644
--- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
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"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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"nbsphinx": "hidden"
},
@@ -340,10 +340,10 @@
"execution_count": 4,
"metadata": {
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- "shell.execute_reply": "2024-06-25T19:36:43.521428Z"
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+ "iopub.status.busy": "2024-06-25T23:18:18.614888Z",
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+ "shell.execute_reply": "2024-06-25T23:18:18.849023Z"
}
},
"outputs": [
@@ -393,10 +393,10 @@
"execution_count": 5,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:43.524390Z",
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+ "iopub.execute_input": "2024-06-25T23:18:18.851953Z",
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+ "shell.execute_reply": "2024-06-25T23:18:18.877017Z"
}
},
"outputs": [],
@@ -428,10 +428,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:43.552184Z",
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- "shell.execute_reply": "2024-06-25T19:36:45.542976Z"
+ "iopub.execute_input": "2024-06-25T23:18:18.879561Z",
+ "iopub.status.busy": "2024-06-25T23:18:18.879211Z",
+ "iopub.status.idle": "2024-06-25T23:18:20.899666Z",
+ "shell.execute_reply": "2024-06-25T23:18:20.898976Z"
}
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"outputs": [
@@ -482,10 +482,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:45.546502Z",
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- "shell.execute_reply": "2024-06-25T19:36:45.563096Z"
+ "iopub.execute_input": "2024-06-25T23:18:20.901960Z",
+ "iopub.status.busy": "2024-06-25T23:18:20.901648Z",
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+ "shell.execute_reply": "2024-06-25T23:18:20.918920Z"
},
"scrolled": true
},
@@ -615,10 +615,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:45.565656Z",
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- "shell.execute_reply": "2024-06-25T19:36:46.994691Z"
+ "iopub.execute_input": "2024-06-25T23:18:20.921440Z",
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+ "shell.execute_reply": "2024-06-25T23:18:22.360462Z"
},
"id": "AaHC5MRKjruT"
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@@ -737,10 +737,10 @@
"execution_count": 9,
"metadata": {
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- "shell.execute_reply": "2024-06-25T19:36:47.010231Z"
+ "iopub.execute_input": "2024-06-25T23:18:22.363669Z",
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"id": "Wy27rvyhjruU"
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@@ -789,10 +789,10 @@
"execution_count": 10,
"metadata": {
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- "shell.execute_reply": "2024-06-25T19:36:47.091384Z"
+ "iopub.execute_input": "2024-06-25T23:18:22.378792Z",
+ "iopub.status.busy": "2024-06-25T23:18:22.378469Z",
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},
"id": "Db8YHnyVjruU"
},
@@ -899,10 +899,10 @@
"execution_count": 11,
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:36:47.094203Z",
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- "shell.execute_reply": "2024-06-25T19:36:47.305822Z"
+ "iopub.execute_input": "2024-06-25T23:18:22.454608Z",
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+ "shell.execute_reply": "2024-06-25T23:18:22.661824Z"
},
"id": "iJqAHuS2jruV"
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@@ -939,10 +939,10 @@
"execution_count": 12,
"metadata": {
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- "shell.execute_reply": "2024-06-25T19:36:47.324401Z"
+ "iopub.execute_input": "2024-06-25T23:18:22.664593Z",
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+ "shell.execute_reply": "2024-06-25T23:18:22.680722Z"
},
"id": "PcPTZ_JJG3Cx"
},
@@ -1408,10 +1408,10 @@
"execution_count": 13,
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-06-25T19:36:47.335352Z"
+ "iopub.execute_input": "2024-06-25T23:18:22.683372Z",
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+ "shell.execute_reply": "2024-06-25T23:18:22.691897Z"
},
"id": "0lonvOYvjruV"
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@@ -1558,10 +1558,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:47.337943Z",
- "iopub.status.busy": "2024-06-25T19:36:47.337629Z",
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- "shell.execute_reply": "2024-06-25T19:36:47.418522Z"
+ "iopub.execute_input": "2024-06-25T23:18:22.694507Z",
+ "iopub.status.busy": "2024-06-25T23:18:22.694073Z",
+ "iopub.status.idle": "2024-06-25T23:18:22.776192Z",
+ "shell.execute_reply": "2024-06-25T23:18:22.775639Z"
},
"id": "MfqTCa3kjruV"
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@@ -1642,10 +1642,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:47.421368Z",
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- "shell.execute_reply": "2024-06-25T19:36:47.537601Z"
+ "iopub.execute_input": "2024-06-25T23:18:22.778586Z",
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+ "shell.execute_reply": "2024-06-25T23:18:22.893472Z"
},
"id": "9ZtWAYXqMAPL"
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@@ -1705,10 +1705,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:47.540745Z",
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- "shell.execute_reply": "2024-06-25T19:36:47.543812Z"
+ "iopub.execute_input": "2024-06-25T23:18:22.896294Z",
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},
"id": "0rXP3ZPWjruW"
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@@ -1746,10 +1746,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:47.546251Z",
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- "shell.execute_reply": "2024-06-25T19:36:47.549356Z"
+ "iopub.execute_input": "2024-06-25T23:18:22.901918Z",
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+ "shell.execute_reply": "2024-06-25T23:18:22.904793Z"
},
"id": "-iRPe8KXjruW"
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@@ -1804,10 +1804,10 @@
"execution_count": 18,
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-06-25T19:36:47.587995Z",
- "shell.execute_reply": "2024-06-25T19:36:47.587566Z"
+ "iopub.execute_input": "2024-06-25T23:18:22.907395Z",
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+ "shell.execute_reply": "2024-06-25T23:18:22.943295Z"
},
"id": "ZpipUliyjruW"
},
@@ -1858,10 +1858,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:47.589852Z",
- "iopub.status.busy": "2024-06-25T19:36:47.589680Z",
- "iopub.status.idle": "2024-06-25T19:36:47.630699Z",
- "shell.execute_reply": "2024-06-25T19:36:47.630137Z"
+ "iopub.execute_input": "2024-06-25T23:18:22.945705Z",
+ "iopub.status.busy": "2024-06-25T23:18:22.945390Z",
+ "iopub.status.idle": "2024-06-25T23:18:22.987000Z",
+ "shell.execute_reply": "2024-06-25T23:18:22.986556Z"
},
"id": "SLq-3q4xjruX"
},
@@ -1930,10 +1930,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:47.632515Z",
- "iopub.status.busy": "2024-06-25T19:36:47.632349Z",
- "iopub.status.idle": "2024-06-25T19:36:47.720647Z",
- "shell.execute_reply": "2024-06-25T19:36:47.719956Z"
+ "iopub.execute_input": "2024-06-25T23:18:22.989099Z",
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+ "shell.execute_reply": "2024-06-25T23:18:23.078808Z"
},
"id": "g5LHhhuqFbXK"
},
@@ -1965,10 +1965,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:47.723084Z",
- "iopub.status.busy": "2024-06-25T19:36:47.722899Z",
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- "shell.execute_reply": "2024-06-25T19:36:47.801549Z"
+ "iopub.execute_input": "2024-06-25T23:18:23.081992Z",
+ "iopub.status.busy": "2024-06-25T23:18:23.081632Z",
+ "iopub.status.idle": "2024-06-25T23:18:23.163660Z",
+ "shell.execute_reply": "2024-06-25T23:18:23.163108Z"
},
"id": "p7w8F8ezBcet"
},
@@ -2025,10 +2025,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:47.804647Z",
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- "iopub.status.idle": "2024-06-25T19:36:48.012610Z",
- "shell.execute_reply": "2024-06-25T19:36:48.012009Z"
+ "iopub.execute_input": "2024-06-25T23:18:23.166170Z",
+ "iopub.status.busy": "2024-06-25T23:18:23.165696Z",
+ "iopub.status.idle": "2024-06-25T23:18:23.373652Z",
+ "shell.execute_reply": "2024-06-25T23:18:23.373076Z"
},
"id": "WETRL74tE_sU"
},
@@ -2063,10 +2063,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:48.014974Z",
- "iopub.status.busy": "2024-06-25T19:36:48.014733Z",
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- "shell.execute_reply": "2024-06-25T19:36:48.197109Z"
+ "iopub.execute_input": "2024-06-25T23:18:23.375920Z",
+ "iopub.status.busy": "2024-06-25T23:18:23.375563Z",
+ "iopub.status.idle": "2024-06-25T23:18:23.542133Z",
+ "shell.execute_reply": "2024-06-25T23:18:23.541601Z"
},
"id": "kCfdx2gOLmXS"
},
@@ -2228,10 +2228,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:48.200133Z",
- "iopub.status.busy": "2024-06-25T19:36:48.199890Z",
- "iopub.status.idle": "2024-06-25T19:36:48.206211Z",
- "shell.execute_reply": "2024-06-25T19:36:48.205745Z"
+ "iopub.execute_input": "2024-06-25T23:18:23.544310Z",
+ "iopub.status.busy": "2024-06-25T23:18:23.544080Z",
+ "iopub.status.idle": "2024-06-25T23:18:23.550244Z",
+ "shell.execute_reply": "2024-06-25T23:18:23.549696Z"
},
"id": "-uogYRWFYnuu"
},
@@ -2285,10 +2285,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:48.208373Z",
- "iopub.status.busy": "2024-06-25T19:36:48.207949Z",
- "iopub.status.idle": "2024-06-25T19:36:48.423251Z",
- "shell.execute_reply": "2024-06-25T19:36:48.422679Z"
+ "iopub.execute_input": "2024-06-25T23:18:23.552552Z",
+ "iopub.status.busy": "2024-06-25T23:18:23.552102Z",
+ "iopub.status.idle": "2024-06-25T23:18:23.765551Z",
+ "shell.execute_reply": "2024-06-25T23:18:23.764971Z"
},
"id": "pG-ljrmcYp9Q"
},
@@ -2335,10 +2335,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:48.425569Z",
- "iopub.status.busy": "2024-06-25T19:36:48.425133Z",
- "iopub.status.idle": "2024-06-25T19:36:49.482076Z",
- "shell.execute_reply": "2024-06-25T19:36:49.481529Z"
+ "iopub.execute_input": "2024-06-25T23:18:23.767794Z",
+ "iopub.status.busy": "2024-06-25T23:18:23.767426Z",
+ "iopub.status.idle": "2024-06-25T23:18:24.838654Z",
+ "shell.execute_reply": "2024-06-25T23:18:24.838036Z"
},
"id": "wL3ngCnuLEWd"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
index 906c55fbe..b4c4a33f9 100644
--- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
@@ -88,10 +88,10 @@
"id": "a3ddc95f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:52.983005Z",
- "iopub.status.busy": "2024-06-25T19:36:52.982831Z",
- "iopub.status.idle": "2024-06-25T19:36:54.092198Z",
- "shell.execute_reply": "2024-06-25T19:36:54.091645Z"
+ "iopub.execute_input": "2024-06-25T23:18:28.410867Z",
+ "iopub.status.busy": "2024-06-25T23:18:28.410704Z",
+ "iopub.status.idle": "2024-06-25T23:18:29.523341Z",
+ "shell.execute_reply": "2024-06-25T23:18:29.522804Z"
},
"nbsphinx": "hidden"
},
@@ -101,7 +101,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -135,10 +135,10 @@
"id": "c4efd119",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:54.094787Z",
- "iopub.status.busy": "2024-06-25T19:36:54.094431Z",
- "iopub.status.idle": "2024-06-25T19:36:54.097617Z",
- "shell.execute_reply": "2024-06-25T19:36:54.097173Z"
+ "iopub.execute_input": "2024-06-25T23:18:29.525967Z",
+ "iopub.status.busy": "2024-06-25T23:18:29.525510Z",
+ "iopub.status.idle": "2024-06-25T23:18:29.528645Z",
+ "shell.execute_reply": "2024-06-25T23:18:29.528187Z"
}
},
"outputs": [],
@@ -263,10 +263,10 @@
"id": "c37c0a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:54.099720Z",
- "iopub.status.busy": "2024-06-25T19:36:54.099372Z",
- "iopub.status.idle": "2024-06-25T19:36:54.107610Z",
- "shell.execute_reply": "2024-06-25T19:36:54.107140Z"
+ "iopub.execute_input": "2024-06-25T23:18:29.530912Z",
+ "iopub.status.busy": "2024-06-25T23:18:29.530502Z",
+ "iopub.status.idle": "2024-06-25T23:18:29.538778Z",
+ "shell.execute_reply": "2024-06-25T23:18:29.538338Z"
},
"nbsphinx": "hidden"
},
@@ -350,10 +350,10 @@
"id": "99f69523",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:54.109674Z",
- "iopub.status.busy": "2024-06-25T19:36:54.109247Z",
- "iopub.status.idle": "2024-06-25T19:36:54.157412Z",
- "shell.execute_reply": "2024-06-25T19:36:54.156840Z"
+ "iopub.execute_input": "2024-06-25T23:18:29.540895Z",
+ "iopub.status.busy": "2024-06-25T23:18:29.540489Z",
+ "iopub.status.idle": "2024-06-25T23:18:29.587259Z",
+ "shell.execute_reply": "2024-06-25T23:18:29.586733Z"
}
},
"outputs": [],
@@ -379,10 +379,10 @@
"id": "8f241c16",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:54.159654Z",
- "iopub.status.busy": "2024-06-25T19:36:54.159472Z",
- "iopub.status.idle": "2024-06-25T19:36:54.177229Z",
- "shell.execute_reply": "2024-06-25T19:36:54.176762Z"
+ "iopub.execute_input": "2024-06-25T23:18:29.589466Z",
+ "iopub.status.busy": "2024-06-25T23:18:29.589277Z",
+ "iopub.status.idle": "2024-06-25T23:18:29.606524Z",
+ "shell.execute_reply": "2024-06-25T23:18:29.606095Z"
}
},
"outputs": [
@@ -597,10 +597,10 @@
"id": "4f0819ba",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:54.179344Z",
- "iopub.status.busy": "2024-06-25T19:36:54.179010Z",
- "iopub.status.idle": "2024-06-25T19:36:54.182993Z",
- "shell.execute_reply": "2024-06-25T19:36:54.182561Z"
+ "iopub.execute_input": "2024-06-25T23:18:29.608443Z",
+ "iopub.status.busy": "2024-06-25T23:18:29.608267Z",
+ "iopub.status.idle": "2024-06-25T23:18:29.612218Z",
+ "shell.execute_reply": "2024-06-25T23:18:29.611771Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "d009f347",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:54.185097Z",
- "iopub.status.busy": "2024-06-25T19:36:54.184777Z",
- "iopub.status.idle": "2024-06-25T19:36:54.198824Z",
- "shell.execute_reply": "2024-06-25T19:36:54.198358Z"
+ "iopub.execute_input": "2024-06-25T23:18:29.614226Z",
+ "iopub.status.busy": "2024-06-25T23:18:29.614054Z",
+ "iopub.status.idle": "2024-06-25T23:18:29.631367Z",
+ "shell.execute_reply": "2024-06-25T23:18:29.630956Z"
}
},
"outputs": [],
@@ -698,10 +698,10 @@
"id": "cbd1e415",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:54.200845Z",
- "iopub.status.busy": "2024-06-25T19:36:54.200664Z",
- "iopub.status.idle": "2024-06-25T19:36:54.227151Z",
- "shell.execute_reply": "2024-06-25T19:36:54.226585Z"
+ "iopub.execute_input": "2024-06-25T23:18:29.633306Z",
+ "iopub.status.busy": "2024-06-25T23:18:29.632964Z",
+ "iopub.status.idle": "2024-06-25T23:18:29.658440Z",
+ "shell.execute_reply": "2024-06-25T23:18:29.658012Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"id": "6ca92617",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:54.229370Z",
- "iopub.status.busy": "2024-06-25T19:36:54.228984Z",
- "iopub.status.idle": "2024-06-25T19:36:56.088954Z",
- "shell.execute_reply": "2024-06-25T19:36:56.088321Z"
+ "iopub.execute_input": "2024-06-25T23:18:29.660435Z",
+ "iopub.status.busy": "2024-06-25T23:18:29.660092Z",
+ "iopub.status.idle": "2024-06-25T23:18:31.561212Z",
+ "shell.execute_reply": "2024-06-25T23:18:31.560640Z"
}
},
"outputs": [],
@@ -771,10 +771,10 @@
"id": "bf945113",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:56.091797Z",
- "iopub.status.busy": "2024-06-25T19:36:56.091365Z",
- "iopub.status.idle": "2024-06-25T19:36:56.098121Z",
- "shell.execute_reply": "2024-06-25T19:36:56.097667Z"
+ "iopub.execute_input": "2024-06-25T23:18:31.563955Z",
+ "iopub.status.busy": "2024-06-25T23:18:31.563327Z",
+ "iopub.status.idle": "2024-06-25T23:18:31.570324Z",
+ "shell.execute_reply": "2024-06-25T23:18:31.569880Z"
},
"scrolled": true
},
@@ -885,10 +885,10 @@
"id": "14251ee0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:56.100176Z",
- "iopub.status.busy": "2024-06-25T19:36:56.099747Z",
- "iopub.status.idle": "2024-06-25T19:36:56.112314Z",
- "shell.execute_reply": "2024-06-25T19:36:56.111779Z"
+ "iopub.execute_input": "2024-06-25T23:18:31.572276Z",
+ "iopub.status.busy": "2024-06-25T23:18:31.571950Z",
+ "iopub.status.idle": "2024-06-25T23:18:31.584255Z",
+ "shell.execute_reply": "2024-06-25T23:18:31.583817Z"
}
},
"outputs": [
@@ -1138,10 +1138,10 @@
"id": "efe16638",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:56.114307Z",
- "iopub.status.busy": "2024-06-25T19:36:56.113989Z",
- "iopub.status.idle": "2024-06-25T19:36:56.120308Z",
- "shell.execute_reply": "2024-06-25T19:36:56.119759Z"
+ "iopub.execute_input": "2024-06-25T23:18:31.586203Z",
+ "iopub.status.busy": "2024-06-25T23:18:31.585878Z",
+ "iopub.status.idle": "2024-06-25T23:18:31.591999Z",
+ "shell.execute_reply": "2024-06-25T23:18:31.591576Z"
},
"scrolled": true
},
@@ -1315,10 +1315,10 @@
"id": "abd0fb0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:56.122321Z",
- "iopub.status.busy": "2024-06-25T19:36:56.122009Z",
- "iopub.status.idle": "2024-06-25T19:36:56.124766Z",
- "shell.execute_reply": "2024-06-25T19:36:56.124216Z"
+ "iopub.execute_input": "2024-06-25T23:18:31.594128Z",
+ "iopub.status.busy": "2024-06-25T23:18:31.593809Z",
+ "iopub.status.idle": "2024-06-25T23:18:31.596328Z",
+ "shell.execute_reply": "2024-06-25T23:18:31.595895Z"
}
},
"outputs": [],
@@ -1340,10 +1340,10 @@
"id": "cdf061df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:56.126666Z",
- "iopub.status.busy": "2024-06-25T19:36:56.126364Z",
- "iopub.status.idle": "2024-06-25T19:36:56.129930Z",
- "shell.execute_reply": "2024-06-25T19:36:56.129387Z"
+ "iopub.execute_input": "2024-06-25T23:18:31.598281Z",
+ "iopub.status.busy": "2024-06-25T23:18:31.597974Z",
+ "iopub.status.idle": "2024-06-25T23:18:31.601541Z",
+ "shell.execute_reply": "2024-06-25T23:18:31.600983Z"
},
"scrolled": true
},
@@ -1395,10 +1395,10 @@
"id": "08949890",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:56.132039Z",
- "iopub.status.busy": "2024-06-25T19:36:56.131738Z",
- "iopub.status.idle": "2024-06-25T19:36:56.134411Z",
- "shell.execute_reply": "2024-06-25T19:36:56.133864Z"
+ "iopub.execute_input": "2024-06-25T23:18:31.603595Z",
+ "iopub.status.busy": "2024-06-25T23:18:31.603264Z",
+ "iopub.status.idle": "2024-06-25T23:18:31.605889Z",
+ "shell.execute_reply": "2024-06-25T23:18:31.605456Z"
}
},
"outputs": [],
@@ -1422,10 +1422,10 @@
"id": "6948b073",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:56.136459Z",
- "iopub.status.busy": "2024-06-25T19:36:56.136150Z",
- "iopub.status.idle": "2024-06-25T19:36:56.140438Z",
- "shell.execute_reply": "2024-06-25T19:36:56.139976Z"
+ "iopub.execute_input": "2024-06-25T23:18:31.607856Z",
+ "iopub.status.busy": "2024-06-25T23:18:31.607558Z",
+ "iopub.status.idle": "2024-06-25T23:18:31.611501Z",
+ "shell.execute_reply": "2024-06-25T23:18:31.611048Z"
}
},
"outputs": [
@@ -1480,10 +1480,10 @@
"id": "6f8e6914",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:56.142440Z",
- "iopub.status.busy": "2024-06-25T19:36:56.142121Z",
- "iopub.status.idle": "2024-06-25T19:36:56.170976Z",
- "shell.execute_reply": "2024-06-25T19:36:56.170425Z"
+ "iopub.execute_input": "2024-06-25T23:18:31.613408Z",
+ "iopub.status.busy": "2024-06-25T23:18:31.613238Z",
+ "iopub.status.idle": "2024-06-25T23:18:31.641822Z",
+ "shell.execute_reply": "2024-06-25T23:18:31.641266Z"
}
},
"outputs": [],
@@ -1526,10 +1526,10 @@
"id": "b806d2ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:56.173162Z",
- "iopub.status.busy": "2024-06-25T19:36:56.172858Z",
- "iopub.status.idle": "2024-06-25T19:36:56.177426Z",
- "shell.execute_reply": "2024-06-25T19:36:56.176864Z"
+ "iopub.execute_input": "2024-06-25T23:18:31.644000Z",
+ "iopub.status.busy": "2024-06-25T23:18:31.643674Z",
+ "iopub.status.idle": "2024-06-25T23:18:31.648272Z",
+ "shell.execute_reply": "2024-06-25T23:18:31.647708Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
index 9e634f2f3..9593cdb90 100644
--- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
@@ -64,10 +64,10 @@
"id": "7383d024-8273-4039-bccd-aab3020d331f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:36:58.919980Z",
- "iopub.status.busy": "2024-06-25T19:36:58.919807Z",
- "iopub.status.idle": "2024-06-25T19:37:00.071287Z",
- "shell.execute_reply": "2024-06-25T19:37:00.070749Z"
+ "iopub.execute_input": "2024-06-25T23:18:34.388005Z",
+ "iopub.status.busy": "2024-06-25T23:18:34.387509Z",
+ "iopub.status.idle": "2024-06-25T23:18:35.555688Z",
+ "shell.execute_reply": "2024-06-25T23:18:35.555141Z"
},
"nbsphinx": "hidden"
},
@@ -79,7 +79,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:37:00.073825Z",
- "iopub.status.busy": "2024-06-25T19:37:00.073418Z",
- "iopub.status.idle": "2024-06-25T19:37:00.265456Z",
- "shell.execute_reply": "2024-06-25T19:37:00.264849Z"
+ "iopub.execute_input": "2024-06-25T23:18:35.558285Z",
+ "iopub.status.busy": "2024-06-25T23:18:35.557842Z",
+ "iopub.status.idle": "2024-06-25T23:18:35.751397Z",
+ "shell.execute_reply": "2024-06-25T23:18:35.750860Z"
}
},
"outputs": [],
@@ -268,10 +268,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:00.268256Z",
- "iopub.status.busy": "2024-06-25T19:37:00.267860Z",
- "iopub.status.idle": "2024-06-25T19:37:00.281177Z",
- "shell.execute_reply": "2024-06-25T19:37:00.280743Z"
+ "iopub.execute_input": "2024-06-25T23:18:35.754209Z",
+ "iopub.status.busy": "2024-06-25T23:18:35.753733Z",
+ "iopub.status.idle": "2024-06-25T23:18:35.767096Z",
+ "shell.execute_reply": "2024-06-25T23:18:35.766635Z"
},
"nbsphinx": "hidden"
},
@@ -407,10 +407,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:00.283272Z",
- "iopub.status.busy": "2024-06-25T19:37:00.282948Z",
- "iopub.status.idle": "2024-06-25T19:37:02.915319Z",
- "shell.execute_reply": "2024-06-25T19:37:02.914720Z"
+ "iopub.execute_input": "2024-06-25T23:18:35.769292Z",
+ "iopub.status.busy": "2024-06-25T23:18:35.768939Z",
+ "iopub.status.idle": "2024-06-25T23:18:38.460798Z",
+ "shell.execute_reply": "2024-06-25T23:18:38.460293Z"
}
},
"outputs": [
@@ -454,10 +454,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:02.917655Z",
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- "iopub.status.idle": "2024-06-25T19:37:04.262113Z",
- "shell.execute_reply": "2024-06-25T19:37:04.261389Z"
+ "iopub.execute_input": "2024-06-25T23:18:38.463138Z",
+ "iopub.status.busy": "2024-06-25T23:18:38.462688Z",
+ "iopub.status.idle": "2024-06-25T23:18:39.817391Z",
+ "shell.execute_reply": "2024-06-25T23:18:39.816843Z"
}
},
"outputs": [],
@@ -499,10 +499,10 @@
"id": "ac1a60df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:04.264665Z",
- "iopub.status.busy": "2024-06-25T19:37:04.264273Z",
- "iopub.status.idle": "2024-06-25T19:37:04.268776Z",
- "shell.execute_reply": "2024-06-25T19:37:04.268171Z"
+ "iopub.execute_input": "2024-06-25T23:18:39.819916Z",
+ "iopub.status.busy": "2024-06-25T23:18:39.819475Z",
+ "iopub.status.idle": "2024-06-25T23:18:39.823477Z",
+ "shell.execute_reply": "2024-06-25T23:18:39.822931Z"
}
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"outputs": [
@@ -544,10 +544,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:04.271017Z",
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- "iopub.status.idle": "2024-06-25T19:37:06.209152Z",
- "shell.execute_reply": "2024-06-25T19:37:06.208542Z"
+ "iopub.execute_input": "2024-06-25T23:18:39.825523Z",
+ "iopub.status.busy": "2024-06-25T23:18:39.825189Z",
+ "iopub.status.idle": "2024-06-25T23:18:41.816360Z",
+ "shell.execute_reply": "2024-06-25T23:18:41.815747Z"
}
},
"outputs": [
@@ -594,10 +594,10 @@
"id": "c18dd83b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:06.211688Z",
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- "iopub.status.idle": "2024-06-25T19:37:06.218564Z",
- "shell.execute_reply": "2024-06-25T19:37:06.218036Z"
+ "iopub.execute_input": "2024-06-25T23:18:41.818930Z",
+ "iopub.status.busy": "2024-06-25T23:18:41.818429Z",
+ "iopub.status.idle": "2024-06-25T23:18:41.826321Z",
+ "shell.execute_reply": "2024-06-25T23:18:41.825851Z"
}
},
"outputs": [
@@ -633,10 +633,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:06.220591Z",
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- "shell.execute_reply": "2024-06-25T19:37:08.792970Z"
+ "iopub.execute_input": "2024-06-25T23:18:41.828406Z",
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+ "iopub.status.idle": "2024-06-25T23:18:44.431218Z",
+ "shell.execute_reply": "2024-06-25T23:18:44.430687Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:08.795901Z",
- "iopub.status.busy": "2024-06-25T19:37:08.795549Z",
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- "shell.execute_reply": "2024-06-25T19:37:08.798350Z"
+ "iopub.execute_input": "2024-06-25T23:18:44.433395Z",
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+ "shell.execute_reply": "2024-06-25T23:18:44.435934Z"
}
},
"outputs": [
@@ -721,10 +721,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:08.800984Z",
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- "shell.execute_reply": "2024-06-25T19:37:08.803635Z"
+ "iopub.execute_input": "2024-06-25T23:18:44.438430Z",
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+ "shell.execute_reply": "2024-06-25T23:18:44.441125Z"
}
},
"outputs": [],
@@ -752,10 +752,10 @@
"id": "d1a2c008",
"metadata": {
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- "shell.execute_reply": "2024-06-25T19:37:08.808609Z"
+ "iopub.execute_input": "2024-06-25T23:18:44.443586Z",
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+ "iopub.status.idle": "2024-06-25T23:18:44.446272Z",
+ "shell.execute_reply": "2024-06-25T23:18:44.445845Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
index aebe787bb..ceb7220d6 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": "2024-06-25T19:37:11.308794Z",
- "iopub.status.busy": "2024-06-25T19:37:11.308627Z",
- "iopub.status.idle": "2024-06-25T19:37:12.452711Z",
- "shell.execute_reply": "2024-06-25T19:37:12.452159Z"
+ "iopub.execute_input": "2024-06-25T23:18:46.821534Z",
+ "iopub.status.busy": "2024-06-25T23:18:46.821356Z",
+ "iopub.status.idle": "2024-06-25T23:18:47.991566Z",
+ "shell.execute_reply": "2024-06-25T23:18:47.991020Z"
},
"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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:37:12.455258Z",
- "iopub.status.busy": "2024-06-25T19:37:12.454827Z",
- "iopub.status.idle": "2024-06-25T19:37:14.890620Z",
- "shell.execute_reply": "2024-06-25T19:37:14.889969Z"
+ "iopub.execute_input": "2024-06-25T23:18:47.994044Z",
+ "iopub.status.busy": "2024-06-25T23:18:47.993746Z",
+ "iopub.status.idle": "2024-06-25T23:18:49.077383Z",
+ "shell.execute_reply": "2024-06-25T23:18:49.076740Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:14.893309Z",
- "iopub.status.busy": "2024-06-25T19:37:14.892942Z",
- "iopub.status.idle": "2024-06-25T19:37:14.896049Z",
- "shell.execute_reply": "2024-06-25T19:37:14.895624Z"
+ "iopub.execute_input": "2024-06-25T23:18:49.079931Z",
+ "iopub.status.busy": "2024-06-25T23:18:49.079715Z",
+ "iopub.status.idle": "2024-06-25T23:18:49.083128Z",
+ "shell.execute_reply": "2024-06-25T23:18:49.082576Z"
}
},
"outputs": [],
@@ -169,10 +169,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:14.898040Z",
- "iopub.status.busy": "2024-06-25T19:37:14.897713Z",
- "iopub.status.idle": "2024-06-25T19:37:14.903653Z",
- "shell.execute_reply": "2024-06-25T19:37:14.903185Z"
+ "iopub.execute_input": "2024-06-25T23:18:49.085315Z",
+ "iopub.status.busy": "2024-06-25T23:18:49.084875Z",
+ "iopub.status.idle": "2024-06-25T23:18:49.090995Z",
+ "shell.execute_reply": "2024-06-25T23:18:49.090565Z"
}
},
"outputs": [],
@@ -198,10 +198,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:14.905688Z",
- "iopub.status.busy": "2024-06-25T19:37:14.905360Z",
- "iopub.status.idle": "2024-06-25T19:37:15.391751Z",
- "shell.execute_reply": "2024-06-25T19:37:15.391128Z"
+ "iopub.execute_input": "2024-06-25T23:18:49.092987Z",
+ "iopub.status.busy": "2024-06-25T23:18:49.092664Z",
+ "iopub.status.idle": "2024-06-25T23:18:49.578049Z",
+ "shell.execute_reply": "2024-06-25T23:18:49.577480Z"
},
"scrolled": true
},
@@ -242,10 +242,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:15.394507Z",
- "iopub.status.busy": "2024-06-25T19:37:15.394142Z",
- "iopub.status.idle": "2024-06-25T19:37:15.399398Z",
- "shell.execute_reply": "2024-06-25T19:37:15.398860Z"
+ "iopub.execute_input": "2024-06-25T23:18:49.581141Z",
+ "iopub.status.busy": "2024-06-25T23:18:49.580804Z",
+ "iopub.status.idle": "2024-06-25T23:18:49.586187Z",
+ "shell.execute_reply": "2024-06-25T23:18:49.585728Z"
}
},
"outputs": [
@@ -497,10 +497,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:15.401545Z",
- "iopub.status.busy": "2024-06-25T19:37:15.401225Z",
- "iopub.status.idle": "2024-06-25T19:37:15.404995Z",
- "shell.execute_reply": "2024-06-25T19:37:15.404569Z"
+ "iopub.execute_input": "2024-06-25T23:18:49.588207Z",
+ "iopub.status.busy": "2024-06-25T23:18:49.587912Z",
+ "iopub.status.idle": "2024-06-25T23:18:49.592364Z",
+ "shell.execute_reply": "2024-06-25T23:18:49.591919Z"
}
},
"outputs": [
@@ -557,10 +557,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:15.407039Z",
- "iopub.status.busy": "2024-06-25T19:37:15.406711Z",
- "iopub.status.idle": "2024-06-25T19:37:16.295944Z",
- "shell.execute_reply": "2024-06-25T19:37:16.295383Z"
+ "iopub.execute_input": "2024-06-25T23:18:49.594287Z",
+ "iopub.status.busy": "2024-06-25T23:18:49.594112Z",
+ "iopub.status.idle": "2024-06-25T23:18:50.586165Z",
+ "shell.execute_reply": "2024-06-25T23:18:50.585507Z"
}
},
"outputs": [
@@ -616,10 +616,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:16.298196Z",
- "iopub.status.busy": "2024-06-25T19:37:16.297999Z",
- "iopub.status.idle": "2024-06-25T19:37:16.525061Z",
- "shell.execute_reply": "2024-06-25T19:37:16.524590Z"
+ "iopub.execute_input": "2024-06-25T23:18:50.588520Z",
+ "iopub.status.busy": "2024-06-25T23:18:50.588324Z",
+ "iopub.status.idle": "2024-06-25T23:18:50.808698Z",
+ "shell.execute_reply": "2024-06-25T23:18:50.808228Z"
}
},
"outputs": [
@@ -660,10 +660,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:16.527288Z",
- "iopub.status.busy": "2024-06-25T19:37:16.526859Z",
- "iopub.status.idle": "2024-06-25T19:37:16.531244Z",
- "shell.execute_reply": "2024-06-25T19:37:16.530747Z"
+ "iopub.execute_input": "2024-06-25T23:18:50.810921Z",
+ "iopub.status.busy": "2024-06-25T23:18:50.810585Z",
+ "iopub.status.idle": "2024-06-25T23:18:50.815013Z",
+ "shell.execute_reply": "2024-06-25T23:18:50.814577Z"
}
},
"outputs": [
@@ -700,10 +700,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:16.533265Z",
- "iopub.status.busy": "2024-06-25T19:37:16.533088Z",
- "iopub.status.idle": "2024-06-25T19:37:16.979069Z",
- "shell.execute_reply": "2024-06-25T19:37:16.978477Z"
+ "iopub.execute_input": "2024-06-25T23:18:50.816841Z",
+ "iopub.status.busy": "2024-06-25T23:18:50.816666Z",
+ "iopub.status.idle": "2024-06-25T23:18:51.264514Z",
+ "shell.execute_reply": "2024-06-25T23:18:51.263937Z"
}
},
"outputs": [
@@ -762,10 +762,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:16.981738Z",
- "iopub.status.busy": "2024-06-25T19:37:16.981547Z",
- "iopub.status.idle": "2024-06-25T19:37:17.310927Z",
- "shell.execute_reply": "2024-06-25T19:37:17.310336Z"
+ "iopub.execute_input": "2024-06-25T23:18:51.267205Z",
+ "iopub.status.busy": "2024-06-25T23:18:51.266984Z",
+ "iopub.status.idle": "2024-06-25T23:18:51.597569Z",
+ "shell.execute_reply": "2024-06-25T23:18:51.596965Z"
}
},
"outputs": [
@@ -812,10 +812,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:17.313292Z",
- "iopub.status.busy": "2024-06-25T19:37:17.312887Z",
- "iopub.status.idle": "2024-06-25T19:37:17.645849Z",
- "shell.execute_reply": "2024-06-25T19:37:17.645269Z"
+ "iopub.execute_input": "2024-06-25T23:18:51.599806Z",
+ "iopub.status.busy": "2024-06-25T23:18:51.599595Z",
+ "iopub.status.idle": "2024-06-25T23:18:51.933374Z",
+ "shell.execute_reply": "2024-06-25T23:18:51.932766Z"
}
},
"outputs": [
@@ -862,10 +862,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:17.649071Z",
- "iopub.status.busy": "2024-06-25T19:37:17.648711Z",
- "iopub.status.idle": "2024-06-25T19:37:18.056258Z",
- "shell.execute_reply": "2024-06-25T19:37:18.055723Z"
+ "iopub.execute_input": "2024-06-25T23:18:51.936579Z",
+ "iopub.status.busy": "2024-06-25T23:18:51.936094Z",
+ "iopub.status.idle": "2024-06-25T23:18:52.348181Z",
+ "shell.execute_reply": "2024-06-25T23:18:52.347588Z"
}
},
"outputs": [
@@ -925,10 +925,10 @@
"id": "7e770d23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:18.060462Z",
- "iopub.status.busy": "2024-06-25T19:37:18.060093Z",
- "iopub.status.idle": "2024-06-25T19:37:18.505775Z",
- "shell.execute_reply": "2024-06-25T19:37:18.505169Z"
+ "iopub.execute_input": "2024-06-25T23:18:52.352428Z",
+ "iopub.status.busy": "2024-06-25T23:18:52.351994Z",
+ "iopub.status.idle": "2024-06-25T23:18:52.773521Z",
+ "shell.execute_reply": "2024-06-25T23:18:52.772929Z"
}
},
"outputs": [
@@ -971,10 +971,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:18.508548Z",
- "iopub.status.busy": "2024-06-25T19:37:18.508203Z",
- "iopub.status.idle": "2024-06-25T19:37:18.698418Z",
- "shell.execute_reply": "2024-06-25T19:37:18.697831Z"
+ "iopub.execute_input": "2024-06-25T23:18:52.776870Z",
+ "iopub.status.busy": "2024-06-25T23:18:52.776447Z",
+ "iopub.status.idle": "2024-06-25T23:18:52.965633Z",
+ "shell.execute_reply": "2024-06-25T23:18:52.965014Z"
}
},
"outputs": [
@@ -1017,10 +1017,10 @@
"id": "0302818a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:18.700790Z",
- "iopub.status.busy": "2024-06-25T19:37:18.700610Z",
- "iopub.status.idle": "2024-06-25T19:37:18.880703Z",
- "shell.execute_reply": "2024-06-25T19:37:18.880186Z"
+ "iopub.execute_input": "2024-06-25T23:18:52.968518Z",
+ "iopub.status.busy": "2024-06-25T23:18:52.968035Z",
+ "iopub.status.idle": "2024-06-25T23:18:53.169696Z",
+ "shell.execute_reply": "2024-06-25T23:18:53.169139Z"
}
},
"outputs": [
@@ -1067,10 +1067,10 @@
"id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:18.882941Z",
- "iopub.status.busy": "2024-06-25T19:37:18.882765Z",
- "iopub.status.idle": "2024-06-25T19:37:18.885792Z",
- "shell.execute_reply": "2024-06-25T19:37:18.885246Z"
+ "iopub.execute_input": "2024-06-25T23:18:53.171908Z",
+ "iopub.status.busy": "2024-06-25T23:18:53.171701Z",
+ "iopub.status.idle": "2024-06-25T23:18:53.174679Z",
+ "shell.execute_reply": "2024-06-25T23:18:53.174135Z"
}
},
"outputs": [],
@@ -1090,10 +1090,10 @@
"id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:18.887722Z",
- "iopub.status.busy": "2024-06-25T19:37:18.887391Z",
- "iopub.status.idle": "2024-06-25T19:37:19.791276Z",
- "shell.execute_reply": "2024-06-25T19:37:19.790730Z"
+ "iopub.execute_input": "2024-06-25T23:18:53.176658Z",
+ "iopub.status.busy": "2024-06-25T23:18:53.176332Z",
+ "iopub.status.idle": "2024-06-25T23:18:54.151841Z",
+ "shell.execute_reply": "2024-06-25T23:18:54.151257Z"
}
},
"outputs": [
@@ -1172,10 +1172,10 @@
"id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:19.793943Z",
- "iopub.status.busy": "2024-06-25T19:37:19.793573Z",
- "iopub.status.idle": "2024-06-25T19:37:19.935555Z",
- "shell.execute_reply": "2024-06-25T19:37:19.935101Z"
+ "iopub.execute_input": "2024-06-25T23:18:54.153970Z",
+ "iopub.status.busy": "2024-06-25T23:18:54.153788Z",
+ "iopub.status.idle": "2024-06-25T23:18:54.367334Z",
+ "shell.execute_reply": "2024-06-25T23:18:54.366782Z"
}
},
"outputs": [
@@ -1214,10 +1214,10 @@
"id": "59d7ee39-3785-434b-8680-9133014851cd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:19.937552Z",
- "iopub.status.busy": "2024-06-25T19:37:19.937378Z",
- "iopub.status.idle": "2024-06-25T19:37:20.088397Z",
- "shell.execute_reply": "2024-06-25T19:37:20.087796Z"
+ "iopub.execute_input": "2024-06-25T23:18:54.369532Z",
+ "iopub.status.busy": "2024-06-25T23:18:54.369222Z",
+ "iopub.status.idle": "2024-06-25T23:18:54.583472Z",
+ "shell.execute_reply": "2024-06-25T23:18:54.582875Z"
}
},
"outputs": [],
@@ -1266,10 +1266,10 @@
"id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:20.090556Z",
- "iopub.status.busy": "2024-06-25T19:37:20.090235Z",
- "iopub.status.idle": "2024-06-25T19:37:20.751985Z",
- "shell.execute_reply": "2024-06-25T19:37:20.751385Z"
+ "iopub.execute_input": "2024-06-25T23:18:54.585760Z",
+ "iopub.status.busy": "2024-06-25T23:18:54.585359Z",
+ "iopub.status.idle": "2024-06-25T23:18:55.323353Z",
+ "shell.execute_reply": "2024-06-25T23:18:55.322814Z"
}
},
"outputs": [
@@ -1351,10 +1351,10 @@
"id": "8ce74938",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:20.754413Z",
- "iopub.status.busy": "2024-06-25T19:37:20.753942Z",
- "iopub.status.idle": "2024-06-25T19:37:20.757882Z",
- "shell.execute_reply": "2024-06-25T19:37:20.757342Z"
+ "iopub.execute_input": "2024-06-25T23:18:55.325548Z",
+ "iopub.status.busy": "2024-06-25T23:18:55.325207Z",
+ "iopub.status.idle": "2024-06-25T23:18:55.329284Z",
+ "shell.execute_reply": "2024-06-25T23:18:55.328852Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
index 3359ecfd0..4aeee095a 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": "2024-06-25T19:37:22.937714Z",
- "iopub.status.busy": "2024-06-25T19:37:22.937546Z",
- "iopub.status.idle": "2024-06-25T19:37:25.620183Z",
- "shell.execute_reply": "2024-06-25T19:37:25.619593Z"
+ "iopub.execute_input": "2024-06-25T23:18:57.455185Z",
+ "iopub.status.busy": "2024-06-25T23:18:57.455007Z",
+ "iopub.status.idle": "2024-06-25T23:19:00.140522Z",
+ "shell.execute_reply": "2024-06-25T23:19:00.139964Z"
},
"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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:37:25.622737Z",
- "iopub.status.busy": "2024-06-25T19:37:25.622414Z",
- "iopub.status.idle": "2024-06-25T19:37:25.936079Z",
- "shell.execute_reply": "2024-06-25T19:37:25.935452Z"
+ "iopub.execute_input": "2024-06-25T23:19:00.143299Z",
+ "iopub.status.busy": "2024-06-25T23:19:00.142777Z",
+ "iopub.status.idle": "2024-06-25T23:19:00.459330Z",
+ "shell.execute_reply": "2024-06-25T23:19:00.458710Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "3792f82e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:37:25.938723Z",
- "iopub.status.busy": "2024-06-25T19:37:25.938422Z",
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- "shell.execute_reply": "2024-06-25T19:37:25.942185Z"
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@@ -252,7 +252,7 @@
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+ " 1%| | 1867776/170498071 [00:00<00:09, 18674661.14it/s]"
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+ " 8%|▊ | 13533184/170498071 [00:00<00:02, 76238255.65it/s]"
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@@ -666,10 +490,10 @@
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@@ -720,10 +544,10 @@
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@@ -939,10 +763,10 @@
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@@ -978,10 +802,10 @@
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@@ -1031,10 +855,10 @@
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@@ -1082,10 +906,10 @@
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@@ -1141,10 +965,10 @@
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@@ -1165,10 +989,10 @@
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@@ -1205,10 +1029,10 @@
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@@ -1239,10 +1063,10 @@
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@@ -1554,7 +1437,30 @@
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@@ -1606,88 +1512,6 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
index 1e51dfcdc..4dccd9a0a 100644
--- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
@@ -102,10 +102,10 @@
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- "shell.execute_reply": "2024-06-25T19:38:08.721811Z"
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@@ -116,7 +116,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -142,10 +142,10 @@
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@@ -164,10 +164,10 @@
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@@ -198,10 +198,10 @@
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@@ -374,10 +374,10 @@
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@@ -417,10 +417,10 @@
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@@ -456,10 +456,10 @@
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@@ -477,10 +477,10 @@
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@@ -527,10 +527,10 @@
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@@ -545,10 +545,10 @@
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- "iopub.status.idle": "2024-06-25T19:38:18.068771Z",
- "shell.execute_reply": "2024-06-25T19:38:18.068131Z"
+ "iopub.execute_input": "2024-06-25T23:19:40.421960Z",
+ "iopub.status.busy": "2024-06-25T23:19:40.421649Z",
+ "iopub.status.idle": "2024-06-25T23:19:48.997759Z",
+ "shell.execute_reply": "2024-06-25T23:19:48.997063Z"
}
},
"outputs": [],
@@ -572,10 +572,10 @@
"id": "f407bd69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.071591Z",
- "iopub.status.busy": "2024-06-25T19:38:18.071196Z",
- "iopub.status.idle": "2024-06-25T19:38:18.078371Z",
- "shell.execute_reply": "2024-06-25T19:38:18.077824Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.000433Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.000048Z",
+ "iopub.status.idle": "2024-06-25T23:19:49.007281Z",
+ "shell.execute_reply": "2024-06-25T23:19:49.006704Z"
}
},
"outputs": [
@@ -678,10 +678,10 @@
"id": "f7385336",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.080333Z",
- "iopub.status.busy": "2024-06-25T19:38:18.080152Z",
- "iopub.status.idle": "2024-06-25T19:38:18.083810Z",
- "shell.execute_reply": "2024-06-25T19:38:18.083366Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.009612Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.009171Z",
+ "iopub.status.idle": "2024-06-25T23:19:49.013898Z",
+ "shell.execute_reply": "2024-06-25T23:19:49.013343Z"
}
},
"outputs": [],
@@ -696,10 +696,10 @@
"id": "59fc3091",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.085821Z",
- "iopub.status.busy": "2024-06-25T19:38:18.085497Z",
- "iopub.status.idle": "2024-06-25T19:38:18.088621Z",
- "shell.execute_reply": "2024-06-25T19:38:18.088109Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.016095Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.015919Z",
+ "iopub.status.idle": "2024-06-25T23:19:49.019068Z",
+ "shell.execute_reply": "2024-06-25T23:19:49.018547Z"
}
},
"outputs": [
@@ -734,10 +734,10 @@
"id": "00949977",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.090576Z",
- "iopub.status.busy": "2024-06-25T19:38:18.090262Z",
- "iopub.status.idle": "2024-06-25T19:38:18.093389Z",
- "shell.execute_reply": "2024-06-25T19:38:18.092821Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.020914Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.020745Z",
+ "iopub.status.idle": "2024-06-25T23:19:49.023808Z",
+ "shell.execute_reply": "2024-06-25T23:19:49.023350Z"
}
},
"outputs": [],
@@ -756,10 +756,10 @@
"id": "b6c1ae3a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.095470Z",
- "iopub.status.busy": "2024-06-25T19:38:18.095154Z",
- "iopub.status.idle": "2024-06-25T19:38:18.103228Z",
- "shell.execute_reply": "2024-06-25T19:38:18.102775Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.025803Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.025488Z",
+ "iopub.status.idle": "2024-06-25T23:19:49.033564Z",
+ "shell.execute_reply": "2024-06-25T23:19:49.033138Z"
}
},
"outputs": [
@@ -883,10 +883,10 @@
"id": "9131d82d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.105003Z",
- "iopub.status.busy": "2024-06-25T19:38:18.104832Z",
- "iopub.status.idle": "2024-06-25T19:38:18.107625Z",
- "shell.execute_reply": "2024-06-25T19:38:18.107128Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.035573Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.035256Z",
+ "iopub.status.idle": "2024-06-25T23:19:49.037707Z",
+ "shell.execute_reply": "2024-06-25T23:19:49.037270Z"
},
"nbsphinx": "hidden"
},
@@ -921,10 +921,10 @@
"id": "31c704e7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.109671Z",
- "iopub.status.busy": "2024-06-25T19:38:18.109367Z",
- "iopub.status.idle": "2024-06-25T19:38:18.236233Z",
- "shell.execute_reply": "2024-06-25T19:38:18.235732Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.039639Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.039383Z",
+ "iopub.status.idle": "2024-06-25T23:19:49.162747Z",
+ "shell.execute_reply": "2024-06-25T23:19:49.162268Z"
}
},
"outputs": [
@@ -963,10 +963,10 @@
"id": "0bcc43db",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.238299Z",
- "iopub.status.busy": "2024-06-25T19:38:18.237942Z",
- "iopub.status.idle": "2024-06-25T19:38:18.347132Z",
- "shell.execute_reply": "2024-06-25T19:38:18.346641Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.164799Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.164444Z",
+ "iopub.status.idle": "2024-06-25T23:19:49.269361Z",
+ "shell.execute_reply": "2024-06-25T23:19:49.268836Z"
}
},
"outputs": [
@@ -1022,10 +1022,10 @@
"id": "7021bd68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.349402Z",
- "iopub.status.busy": "2024-06-25T19:38:18.349044Z",
- "iopub.status.idle": "2024-06-25T19:38:18.839672Z",
- "shell.execute_reply": "2024-06-25T19:38:18.839073Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.271927Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.271573Z",
+ "iopub.status.idle": "2024-06-25T23:19:49.761626Z",
+ "shell.execute_reply": "2024-06-25T23:19:49.760979Z"
}
},
"outputs": [],
@@ -1041,10 +1041,10 @@
"id": "d49c990b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.841931Z",
- "iopub.status.busy": "2024-06-25T19:38:18.841755Z",
- "iopub.status.idle": "2024-06-25T19:38:18.912662Z",
- "shell.execute_reply": "2024-06-25T19:38:18.912091Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.764216Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.763834Z",
+ "iopub.status.idle": "2024-06-25T23:19:49.843367Z",
+ "shell.execute_reply": "2024-06-25T23:19:49.842743Z"
}
},
"outputs": [
@@ -1079,10 +1079,10 @@
"id": "dbab6fb3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.915065Z",
- "iopub.status.busy": "2024-06-25T19:38:18.914579Z",
- "iopub.status.idle": "2024-06-25T19:38:18.923159Z",
- "shell.execute_reply": "2024-06-25T19:38:18.922730Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.845464Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.845235Z",
+ "iopub.status.idle": "2024-06-25T23:19:49.853788Z",
+ "shell.execute_reply": "2024-06-25T23:19:49.853340Z"
}
},
"outputs": [
@@ -1189,10 +1189,10 @@
"id": "5b39b8b5",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.925120Z",
- "iopub.status.busy": "2024-06-25T19:38:18.924947Z",
- "iopub.status.idle": "2024-06-25T19:38:18.927502Z",
- "shell.execute_reply": "2024-06-25T19:38:18.927067Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.855684Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.855513Z",
+ "iopub.status.idle": "2024-06-25T23:19:49.858498Z",
+ "shell.execute_reply": "2024-06-25T23:19:49.857932Z"
},
"nbsphinx": "hidden"
},
@@ -1217,10 +1217,10 @@
"id": "df06525b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:18.929453Z",
- "iopub.status.busy": "2024-06-25T19:38:18.929127Z",
- "iopub.status.idle": "2024-06-25T19:38:24.397527Z",
- "shell.execute_reply": "2024-06-25T19:38:24.396937Z"
+ "iopub.execute_input": "2024-06-25T23:19:49.860435Z",
+ "iopub.status.busy": "2024-06-25T23:19:49.860125Z",
+ "iopub.status.idle": "2024-06-25T23:19:55.315583Z",
+ "shell.execute_reply": "2024-06-25T23:19:55.315005Z"
}
},
"outputs": [
@@ -1264,10 +1264,10 @@
"id": "05282559",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:24.400077Z",
- "iopub.status.busy": "2024-06-25T19:38:24.399563Z",
- "iopub.status.idle": "2024-06-25T19:38:24.408142Z",
- "shell.execute_reply": "2024-06-25T19:38:24.407603Z"
+ "iopub.execute_input": "2024-06-25T23:19:55.317789Z",
+ "iopub.status.busy": "2024-06-25T23:19:55.317611Z",
+ "iopub.status.idle": "2024-06-25T23:19:55.326379Z",
+ "shell.execute_reply": "2024-06-25T23:19:55.325834Z"
}
},
"outputs": [
@@ -1376,10 +1376,10 @@
"id": "95531cda",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:24.410281Z",
- "iopub.status.busy": "2024-06-25T19:38:24.409820Z",
- "iopub.status.idle": "2024-06-25T19:38:24.473861Z",
- "shell.execute_reply": "2024-06-25T19:38:24.473281Z"
+ "iopub.execute_input": "2024-06-25T23:19:55.328528Z",
+ "iopub.status.busy": "2024-06-25T23:19:55.328125Z",
+ "iopub.status.idle": "2024-06-25T23:19:55.396039Z",
+ "shell.execute_reply": "2024-06-25T23:19:55.395563Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
index 253b92cf5..d70cfaf4e 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": "2024-06-25T19:38:27.445776Z",
- "iopub.status.busy": "2024-06-25T19:38:27.445616Z",
- "iopub.status.idle": "2024-06-25T19:38:29.357688Z",
- "shell.execute_reply": "2024-06-25T19:38:29.356961Z"
+ "iopub.execute_input": "2024-06-25T23:19:58.399516Z",
+ "iopub.status.busy": "2024-06-25T23:19:58.399339Z",
+ "iopub.status.idle": "2024-06-25T23:19:59.729255Z",
+ "shell.execute_reply": "2024-06-25T23:19:59.728521Z"
}
},
"outputs": [],
@@ -79,10 +79,10 @@
"id": "58fd4c55",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:38:29.360485Z",
- "iopub.status.busy": "2024-06-25T19:38:29.360106Z",
- "iopub.status.idle": "2024-06-25T19:39:24.167594Z",
- "shell.execute_reply": "2024-06-25T19:39:24.166933Z"
+ "iopub.execute_input": "2024-06-25T23:19:59.731986Z",
+ "iopub.status.busy": "2024-06-25T23:19:59.731605Z",
+ "iopub.status.idle": "2024-06-25T23:20:48.710370Z",
+ "shell.execute_reply": "2024-06-25T23:20:48.709721Z"
}
},
"outputs": [],
@@ -97,10 +97,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:39:24.170328Z",
- "iopub.status.busy": "2024-06-25T19:39:24.169968Z",
- "iopub.status.idle": "2024-06-25T19:39:25.274825Z",
- "shell.execute_reply": "2024-06-25T19:39:25.274283Z"
+ "iopub.execute_input": "2024-06-25T23:20:48.712844Z",
+ "iopub.status.busy": "2024-06-25T23:20:48.712648Z",
+ "iopub.status.idle": "2024-06-25T23:20:49.819754Z",
+ "shell.execute_reply": "2024-06-25T23:20:49.819207Z"
},
"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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:39:25.277334Z",
- "iopub.status.busy": "2024-06-25T19:39:25.276961Z",
- "iopub.status.idle": "2024-06-25T19:39:25.280274Z",
- "shell.execute_reply": "2024-06-25T19:39:25.279814Z"
+ "iopub.execute_input": "2024-06-25T23:20:49.822551Z",
+ "iopub.status.busy": "2024-06-25T23:20:49.821983Z",
+ "iopub.status.idle": "2024-06-25T23:20:49.825360Z",
+ "shell.execute_reply": "2024-06-25T23:20:49.824898Z"
}
},
"outputs": [],
@@ -203,10 +203,10 @@
"id": "07dc5678",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:39:25.282318Z",
- "iopub.status.busy": "2024-06-25T19:39:25.282060Z",
- "iopub.status.idle": "2024-06-25T19:39:25.285902Z",
- "shell.execute_reply": "2024-06-25T19:39:25.285458Z"
+ "iopub.execute_input": "2024-06-25T23:20:49.827409Z",
+ "iopub.status.busy": "2024-06-25T23:20:49.827081Z",
+ "iopub.status.idle": "2024-06-25T23:20:49.830739Z",
+ "shell.execute_reply": "2024-06-25T23:20:49.830321Z"
}
},
"outputs": [
@@ -247,10 +247,10 @@
"id": "25ebe22a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:39:25.287764Z",
- "iopub.status.busy": "2024-06-25T19:39:25.287595Z",
- "iopub.status.idle": "2024-06-25T19:39:25.291186Z",
- "shell.execute_reply": "2024-06-25T19:39:25.290735Z"
+ "iopub.execute_input": "2024-06-25T23:20:49.832814Z",
+ "iopub.status.busy": "2024-06-25T23:20:49.832481Z",
+ "iopub.status.idle": "2024-06-25T23:20:49.835986Z",
+ "shell.execute_reply": "2024-06-25T23:20:49.835546Z"
}
},
"outputs": [
@@ -290,10 +290,10 @@
"id": "3faedea9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:39:25.293004Z",
- "iopub.status.busy": "2024-06-25T19:39:25.292834Z",
- "iopub.status.idle": "2024-06-25T19:39:25.296491Z",
- "shell.execute_reply": "2024-06-25T19:39:25.296049Z"
+ "iopub.execute_input": "2024-06-25T23:20:49.837816Z",
+ "iopub.status.busy": "2024-06-25T23:20:49.837650Z",
+ "iopub.status.idle": "2024-06-25T23:20:49.841360Z",
+ "shell.execute_reply": "2024-06-25T23:20:49.840870Z"
}
},
"outputs": [],
@@ -333,17 +333,17 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:39:25.298372Z",
- "iopub.status.busy": "2024-06-25T19:39:25.298196Z",
- "iopub.status.idle": "2024-06-25T19:39:58.536591Z",
- "shell.execute_reply": "2024-06-25T19:39:58.535983Z"
+ "iopub.execute_input": "2024-06-25T23:20:49.843348Z",
+ "iopub.status.busy": "2024-06-25T23:20:49.843045Z",
+ "iopub.status.idle": "2024-06-25T23:21:23.312097Z",
+ "shell.execute_reply": "2024-06-25T23:21:23.311395Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "944591b9a0384c6388bc6a076330ac62",
+ "model_id": "198f978c68c04b42bb7f505400e75581",
"version_major": 2,
"version_minor": 0
},
@@ -357,7 +357,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "456e1a39f8a0484d84df60d119f7d9b3",
+ "model_id": "4b186141820047419c3ae004111754f6",
"version_major": 2,
"version_minor": 0
},
@@ -400,10 +400,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:39:58.539357Z",
- "iopub.status.busy": "2024-06-25T19:39:58.538990Z",
- "iopub.status.idle": "2024-06-25T19:39:59.206448Z",
- "shell.execute_reply": "2024-06-25T19:39:59.205970Z"
+ "iopub.execute_input": "2024-06-25T23:21:23.314740Z",
+ "iopub.status.busy": "2024-06-25T23:21:23.314519Z",
+ "iopub.status.idle": "2024-06-25T23:21:23.985247Z",
+ "shell.execute_reply": "2024-06-25T23:21:23.984646Z"
}
},
"outputs": [
@@ -446,10 +446,10 @@
"id": "57fed473",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:39:59.208781Z",
- "iopub.status.busy": "2024-06-25T19:39:59.208330Z",
- "iopub.status.idle": "2024-06-25T19:40:01.948266Z",
- "shell.execute_reply": "2024-06-25T19:40:01.947672Z"
+ "iopub.execute_input": "2024-06-25T23:21:23.987564Z",
+ "iopub.status.busy": "2024-06-25T23:21:23.987136Z",
+ "iopub.status.idle": "2024-06-25T23:21:26.705173Z",
+ "shell.execute_reply": "2024-06-25T23:21:26.704585Z"
}
},
"outputs": [
@@ -519,17 +519,17 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:40:01.950510Z",
- "iopub.status.busy": "2024-06-25T19:40:01.950173Z",
- "iopub.status.idle": "2024-06-25T19:40:34.744210Z",
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- "description_allow_html": false,
- "layout": "IPY_MODEL_6dabf219795b408dabf8afe1ed3b2ba9",
- "placeholder": "",
- "style": "IPY_MODEL_a61e682d08cd4b079011fff3976213c6",
- "tabbable": null,
- "tooltip": null,
- "value": "number of examples processed for estimating thresholds: 100%"
- }
- },
- "f0c67deadadc41a681e33253811fe3c3": {
+ "fcba50827939420b83ea40b9e3507089": {
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"model_name": "LayoutModel",
@@ -2430,53 +2477,6 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
index c6bf67460..28feac438 100644
--- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
@@ -75,10 +75,10 @@
"id": "ae8a08e0",
"metadata": {
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- "iopub.execute_input": "2024-06-25T19:41:02.971504Z",
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- "iopub.status.idle": "2024-06-25T19:41:04.919925Z",
- "shell.execute_reply": "2024-06-25T19:41:04.919315Z"
+ "iopub.execute_input": "2024-06-25T23:22:28.297877Z",
+ "iopub.status.busy": "2024-06-25T23:22:28.297692Z",
+ "iopub.status.idle": "2024-06-25T23:22:29.566144Z",
+ "shell.execute_reply": "2024-06-25T23:22:29.565466Z"
}
},
"outputs": [
@@ -86,7 +86,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-06-25 19:41:02-- https://data.deepai.org/conll2003.zip\r\n",
+ "--2024-06-25 23:22:28-- https://data.deepai.org/conll2003.zip\r\n",
"Resolving data.deepai.org (data.deepai.org)... "
]
},
@@ -94,16 +94,24 @@
"name": "stdout",
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- "169.150.249.162, 2400:52e0:1a01::984:1\r\n",
- "Connecting to data.deepai.org (data.deepai.org)|169.150.249.162|:443... connected.\r\n",
- "HTTP request sent, awaiting response... 200 OK\r\n",
- "Length: 982975 (960K) [application/zip]\r\n"
+ "185.93.1.250, 2400:52e0:1a00::1068:1\r\n",
+ "Connecting to data.deepai.org (data.deepai.org)|185.93.1.250|:443... "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "connected.\r\n",
+ "HTTP request sent, awaiting response... "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "200 OK\r\n",
+ "Length: 982975 (960K) [application/zip]\r\n",
"Saving to: ‘conll2003.zip’\r\n",
"\r\n",
"\r",
@@ -117,7 +125,7 @@
"\r",
"conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n",
"\r\n",
- "2024-06-25 19:41:03 (8.03 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+ "2024-06-25 23:22:28 (6.31 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
"\r\n",
"mkdir: cannot create directory ‘data’: File exists\r\n"
]
@@ -137,22 +145,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-06-25 19:41:03-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
- "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.196.49, 52.216.88.99, 3.5.9.136, ...\r\n",
- "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.196.49|:443... "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "connected.\r\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "--2024-06-25 23:22:29-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+ "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.25.196, 54.231.139.49, 52.216.48.57, ...\r\n",
+ "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.25.196|:443... connected.\r\n",
"HTTP request sent, awaiting response... "
]
},
@@ -173,15 +168,7 @@
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- "pred_probs.npz 1%[ ] 296.53K 1.27MB/s "
- ]
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- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 30%[=====> ] 4.94M 10.8MB/s "
+ "pred_probs.npz 58%[==========> ] 9.47M 47.3MB/s "
]
},
{
@@ -189,9 +176,9 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 100%[===================>] 16.26M 25.4MB/s in 0.6s \r\n",
+ "pred_probs.npz 100%[===================>] 16.26M 55.6MB/s in 0.3s \r\n",
"\r\n",
- "2024-06-25 19:41:04 (25.4 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+ "2024-06-25 23:22:29 (55.6 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
"\r\n"
]
}
@@ -208,10 +195,10 @@
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@@ -222,7 +209,7 @@
"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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -248,10 +235,10 @@
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@@ -322,10 +309,10 @@
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+ "iopub.execute_input": "2024-06-25T23:22:39.542320Z",
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+ "iopub.status.idle": "2024-06-25T23:22:39.547429Z",
+ "shell.execute_reply": "2024-06-25T23:22:39.546974Z"
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@@ -442,10 +429,10 @@
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@@ -482,10 +469,10 @@
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- "shell.execute_reply": "2024-06-25T19:41:15.434547Z"
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@@ -557,10 +544,10 @@
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@@ -582,10 +569,10 @@
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@@ -621,10 +608,10 @@
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- "shell.execute_reply": "2024-06-25T19:41:17.977348Z"
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@@ -802,10 +789,10 @@
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- "shell.execute_reply": "2024-06-25T19:41:18.005228Z"
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@@ -1159,10 +1146,10 @@
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diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 157e736d5..02da15562 100644
--- a/master/_sources/tutorials/clean_learning/tabular.ipynb
+++ b/master/_sources/tutorials/clean_learning/tabular.ipynb
@@ -120,7 +120,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/clean_learning/text.ipynb b/master/_sources/tutorials/clean_learning/text.ipynb
index 1c02419f2..1d4643a4c 100644
--- a/master/_sources/tutorials/clean_learning/text.ipynb
+++ b/master/_sources/tutorials/clean_learning/text.ipynb
@@ -129,7 +129,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/audio.ipynb b/master/_sources/tutorials/datalab/audio.ipynb
index cf4477477..e7aadf6ca 100644
--- a/master/_sources/tutorials/datalab/audio.ipynb
+++ b/master/_sources/tutorials/datalab/audio.ipynb
@@ -91,7 +91,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 4b526f0b4..7eaeed6b0 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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 55fb7ca5d..c7ddd7477 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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 258e19df6..16a8a36cf 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -80,7 +80,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 80446b3d9..b28ce2a19 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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 cef9bfd87..e14cdce07 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -79,7 +79,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 24305a95c..aa12460c1 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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 76838b1ed..2a6fe63a9 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -95,7 +95,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 73d78f419..1ea329f55 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -73,7 +73,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb
index 4e999fd56..b95a571a7 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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 c175a3d2d..010ae4316 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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 e418f0351..04c5f0872 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -110,7 +110,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 13d038510..1333c9749 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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 9f5e5caa9..745c17b57 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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 9ec7961a2..04d4a7903 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
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Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "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?": [[98, "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?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "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.": [[99, "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": [[99, "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": [[99, "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!": [[99, "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": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "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)": [[99, "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:": [[99, "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": [[99, "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.": [[99, "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.": [[99, "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.": [[99, "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.": [[99, "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?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "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": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], 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Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. 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Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. 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Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. 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Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "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?": [[98, "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?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "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.": [[99, "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": [[99, "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": [[99, "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!": [[99, "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": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "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)": [[99, "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:": [[99, "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": [[99, "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.": [[99, "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.": [[99, "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.": [[99, "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.": [[99, "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?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "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": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], 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"cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, 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cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb
index cc8956d28..f3d888536 100644
--- a/master/tutorials/clean_learning/tabular.ipynb
+++ b/master/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
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- "iopub.status.busy": "2024-06-25T19:31:27.766073Z",
- "iopub.status.idle": "2024-06-25T19:31:28.950995Z",
- "shell.execute_reply": "2024-06-25T19:31:28.950453Z"
+ "iopub.execute_input": "2024-06-25T23:13:19.683650Z",
+ "iopub.status.busy": "2024-06-25T23:13:19.683483Z",
+ "iopub.status.idle": "2024-06-25T23:13:20.876411Z",
+ "shell.execute_reply": "2024-06-25T23:13:20.875863Z"
},
"nbsphinx": "hidden"
},
@@ -126,7 +126,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:28.953618Z",
- "iopub.status.busy": "2024-06-25T19:31:28.953345Z",
- "iopub.status.idle": "2024-06-25T19:31:28.970797Z",
- "shell.execute_reply": "2024-06-25T19:31:28.970252Z"
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+ "iopub.status.busy": "2024-06-25T23:13:20.878582Z",
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+ "shell.execute_reply": "2024-06-25T23:13:20.895402Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:28.973223Z",
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- "iopub.status.idle": "2024-06-25T19:31:29.167625Z",
- "shell.execute_reply": "2024-06-25T19:31:29.167053Z"
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+ "iopub.status.busy": "2024-06-25T23:13:20.897628Z",
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}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:29.197486Z",
- "iopub.status.busy": "2024-06-25T19:31:29.197079Z",
- "iopub.status.idle": "2024-06-25T19:31:29.200622Z",
- "shell.execute_reply": "2024-06-25T19:31:29.200145Z"
+ "iopub.execute_input": "2024-06-25T23:13:21.037181Z",
+ "iopub.status.busy": "2024-06-25T23:13:21.036568Z",
+ "iopub.status.idle": "2024-06-25T23:13:21.040405Z",
+ "shell.execute_reply": "2024-06-25T23:13:21.039967Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
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"metadata": {
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- "iopub.status.idle": "2024-06-25T19:31:29.210646Z",
- "shell.execute_reply": "2024-06-25T19:31:29.210233Z"
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+ "shell.execute_reply": "2024-06-25T23:13:21.049993Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-06-25T19:31:29.214911Z",
- "shell.execute_reply": "2024-06-25T19:31:29.214495Z"
+ "iopub.execute_input": "2024-06-25T23:13:21.052411Z",
+ "iopub.status.busy": "2024-06-25T23:13:21.052111Z",
+ "iopub.status.idle": "2024-06-25T23:13:21.054810Z",
+ "shell.execute_reply": "2024-06-25T23:13:21.054263Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:29.216761Z",
- "iopub.status.busy": "2024-06-25T19:31:29.216593Z",
- "iopub.status.idle": "2024-06-25T19:31:29.731597Z",
- "shell.execute_reply": "2024-06-25T19:31:29.730952Z"
+ "iopub.execute_input": "2024-06-25T23:13:21.056799Z",
+ "iopub.status.busy": "2024-06-25T23:13:21.056479Z",
+ "iopub.status.idle": "2024-06-25T23:13:21.584928Z",
+ "shell.execute_reply": "2024-06-25T23:13:21.584385Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:29.733935Z",
- "iopub.status.busy": "2024-06-25T19:31:29.733740Z",
- "iopub.status.idle": "2024-06-25T19:31:31.552423Z",
- "shell.execute_reply": "2024-06-25T19:31:31.551801Z"
+ "iopub.execute_input": "2024-06-25T23:13:21.587427Z",
+ "iopub.status.busy": "2024-06-25T23:13:21.587080Z",
+ "iopub.status.idle": "2024-06-25T23:13:23.402116Z",
+ "shell.execute_reply": "2024-06-25T23:13:23.401472Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:31.554814Z",
- "iopub.status.busy": "2024-06-25T19:31:31.554296Z",
- "iopub.status.idle": "2024-06-25T19:31:31.564323Z",
- "shell.execute_reply": "2024-06-25T19:31:31.563854Z"
+ "iopub.execute_input": "2024-06-25T23:13:23.404837Z",
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+ "shell.execute_reply": "2024-06-25T23:13:23.413559Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:31.566389Z",
- "iopub.status.busy": "2024-06-25T19:31:31.566065Z",
- "iopub.status.idle": "2024-06-25T19:31:31.570002Z",
- "shell.execute_reply": "2024-06-25T19:31:31.569569Z"
+ "iopub.execute_input": "2024-06-25T23:13:23.416257Z",
+ "iopub.status.busy": "2024-06-25T23:13:23.415941Z",
+ "iopub.status.idle": "2024-06-25T23:13:23.420056Z",
+ "shell.execute_reply": "2024-06-25T23:13:23.419521Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
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- "iopub.status.busy": "2024-06-25T19:31:31.571709Z",
- "iopub.status.idle": "2024-06-25T19:31:31.579030Z",
- "shell.execute_reply": "2024-06-25T19:31:31.578475Z"
+ "iopub.execute_input": "2024-06-25T23:13:23.422287Z",
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}
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"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
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@@ -691,10 +691,10 @@
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"metadata": {
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"outputs": [],
@@ -715,10 +715,10 @@
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"metadata": {
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- "shell.execute_reply": "2024-06-25T19:31:33.678623Z"
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@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-06-25T19:31:33.692694Z"
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+ "shell.execute_reply": "2024-06-25T23:13:25.522944Z"
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"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T19:31:33.695397Z",
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- "iopub.status.idle": "2024-06-25T19:31:33.841440Z",
- "shell.execute_reply": "2024-06-25T19:31:33.840949Z"
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+ "shell.execute_reply": "2024-06-25T23:13:25.544739Z"
},
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},
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index 22a2112a2..bab4e39b3 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -817,7 +817,7 @@ 2. Load and format the text dataset
@@ -2079,35 +2079,35 @@ Low information images
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diff --git a/master/tutorials/datalab/workflows.ipynb b/master/tutorials/datalab/workflows.ipynb
index 073e233c2..05570c79a 100644
--- a/master/tutorials/datalab/workflows.ipynb
+++ b/master/tutorials/datalab/workflows.ipynb
@@ -38,10 +38,10 @@
"execution_count": 1,
"metadata": {
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- "iopub.status.idle": "2024-06-25T19:35:59.885710Z",
- "shell.execute_reply": "2024-06-25T19:35:59.885107Z"
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"outputs": [],
@@ -87,10 +87,10 @@
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@@ -181,10 +181,10 @@
"execution_count": 3,
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@@ -210,10 +210,10 @@
"execution_count": 4,
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@@ -716,10 +716,10 @@
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@@ -820,10 +820,10 @@
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@@ -854,10 +854,10 @@
"execution_count": 7,
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@@ -1016,10 +1016,10 @@
"execution_count": 8,
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@@ -1098,10 +1098,10 @@
"execution_count": 9,
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@@ -1131,10 +1131,10 @@
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@@ -1162,10 +1162,10 @@
"execution_count": 11,
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@@ -1205,10 +1205,10 @@
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@@ -1406,10 +1406,10 @@
"execution_count": 13,
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@@ -1476,10 +1476,10 @@
"execution_count": 14,
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@@ -1745,10 +1745,10 @@
"execution_count": 15,
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@@ -1935,10 +1935,10 @@
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@@ -1972,10 +1972,10 @@
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@@ -1997,10 +1997,10 @@
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"outputs": [
@@ -2158,10 +2158,10 @@
"execution_count": 19,
"metadata": {
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@@ -2194,10 +2194,10 @@
"execution_count": 20,
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"outputs": [
@@ -2343,10 +2343,10 @@
"execution_count": 21,
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@@ -2413,10 +2413,10 @@
"execution_count": 22,
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"outputs": [
@@ -2467,10 +2467,10 @@
"execution_count": 23,
"metadata": {
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"outputs": [
@@ -2749,10 +2749,10 @@
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"outputs": [
@@ -3019,10 +3019,10 @@
"execution_count": 25,
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@@ -3047,10 +3047,10 @@
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"outputs": [
@@ -3222,10 +3222,10 @@
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@@ -3257,10 +3257,10 @@
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@@ -3268,230 +3268,230 @@
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diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb
index 6a954c6b0..d462fdaea 100644
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"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@e604611b9bbdc89f91103c8112289faf56854619\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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"\n",
"\n",
"🎯 Mnist_test_set 🎯\n",
- "\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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+ "\n",
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"name": "stdout",
"output_type": "stream",
"text": [
- "\n",
+ "\n"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
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" * Overall, about 18% (1,846 of the 10,000) labels in your dataset have potential issues.\n",
" ** The overall label health score for this dataset is: 0.82.\n",
"\n",
diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html
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