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diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle
index ee6494c10..f9884755c 100644
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diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree
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diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree
index 7e0f0ca37..1c66920dc 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 524ef1091..813d71348 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": "2024-08-08T18:53:00.543675Z",
- "iopub.status.busy": "2024-08-08T18:53:00.543262Z",
- "iopub.status.idle": "2024-08-08T18:53:02.024210Z",
- "shell.execute_reply": "2024-08-08T18:53:02.023643Z"
+ "iopub.execute_input": "2024-08-12T10:31:00.463356Z",
+ "iopub.status.busy": "2024-08-12T10:31:00.462851Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.047524Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.046835Z"
},
"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@ed1943228cd408bbef2343ae07f897ac0f8c96bd\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@399938be1f46b62c047276c21928e3071ce4ba6d\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": "2024-08-08T18:53:02.026764Z",
- "iopub.status.busy": "2024-08-08T18:53:02.026455Z",
- "iopub.status.idle": "2024-08-08T18:53:02.045763Z",
- "shell.execute_reply": "2024-08-08T18:53:02.045207Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.050367Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.049990Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.069900Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.069290Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:02.048477Z",
- "iopub.status.busy": "2024-08-08T18:53:02.047929Z",
- "iopub.status.idle": "2024-08-08T18:53:02.259654Z",
- "shell.execute_reply": "2024-08-08T18:53:02.259026Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.072544Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.072099Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.302446Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.301780Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:02.294496Z",
- "iopub.status.busy": "2024-08-08T18:53:02.294073Z",
- "iopub.status.idle": "2024-08-08T18:53:02.297700Z",
- "shell.execute_reply": "2024-08-08T18:53:02.297264Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.334639Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.334107Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.338211Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.337651Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:02.299789Z",
- "iopub.status.busy": "2024-08-08T18:53:02.299487Z",
- "iopub.status.idle": "2024-08-08T18:53:02.308052Z",
- "shell.execute_reply": "2024-08-08T18:53:02.307601Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.340501Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.340140Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.348891Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.348288Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:02.310321Z",
- "iopub.status.busy": "2024-08-08T18:53:02.309983Z",
- "iopub.status.idle": "2024-08-08T18:53:02.312486Z",
- "shell.execute_reply": "2024-08-08T18:53:02.312044Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.351479Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.351121Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.353720Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.353249Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:02.314538Z",
- "iopub.status.busy": "2024-08-08T18:53:02.314203Z",
- "iopub.status.idle": "2024-08-08T18:53:02.834221Z",
- "shell.execute_reply": "2024-08-08T18:53:02.833675Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.355946Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.355599Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.883586Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.883087Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:02.836846Z",
- "iopub.status.busy": "2024-08-08T18:53:02.836453Z",
- "iopub.status.idle": "2024-08-08T18:53:04.867166Z",
- "shell.execute_reply": "2024-08-08T18:53:04.866464Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.886048Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.885693Z",
+ "iopub.status.idle": "2024-08-12T10:31:05.027547Z",
+ "shell.execute_reply": "2024-08-12T10:31:05.026915Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:04.869789Z",
- "iopub.status.busy": "2024-08-08T18:53:04.869146Z",
- "iopub.status.idle": "2024-08-08T18:53:04.879653Z",
- "shell.execute_reply": "2024-08-08T18:53:04.879122Z"
+ "iopub.execute_input": "2024-08-12T10:31:05.030654Z",
+ "iopub.status.busy": "2024-08-12T10:31:05.029701Z",
+ "iopub.status.idle": "2024-08-12T10:31:05.040437Z",
+ "shell.execute_reply": "2024-08-12T10:31:05.039970Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:04.881845Z",
- "iopub.status.busy": "2024-08-08T18:53:04.881386Z",
- "iopub.status.idle": "2024-08-08T18:53:04.885661Z",
- "shell.execute_reply": "2024-08-08T18:53:04.885109Z"
+ "iopub.execute_input": "2024-08-12T10:31:05.042737Z",
+ "iopub.status.busy": "2024-08-12T10:31:05.042378Z",
+ "iopub.status.idle": "2024-08-12T10:31:05.046796Z",
+ "shell.execute_reply": "2024-08-12T10:31:05.046322Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:04.888005Z",
- "iopub.status.busy": "2024-08-08T18:53:04.887662Z",
- "iopub.status.idle": "2024-08-08T18:53:04.894381Z",
- "shell.execute_reply": "2024-08-08T18:53:04.893952Z"
+ "iopub.execute_input": "2024-08-12T10:31:05.048880Z",
+ "iopub.status.busy": "2024-08-12T10:31:05.048557Z",
+ "iopub.status.idle": "2024-08-12T10:31:05.056263Z",
+ "shell.execute_reply": "2024-08-12T10:31:05.055716Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:04.896556Z",
- "iopub.status.busy": "2024-08-08T18:53:04.896226Z",
- "iopub.status.idle": "2024-08-08T18:53:05.009071Z",
- "shell.execute_reply": "2024-08-08T18:53:05.008556Z"
+ "iopub.execute_input": "2024-08-12T10:31:05.058466Z",
+ "iopub.status.busy": "2024-08-12T10:31:05.058117Z",
+ "iopub.status.idle": "2024-08-12T10:31:05.172389Z",
+ "shell.execute_reply": "2024-08-12T10:31:05.171830Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:05.011216Z",
- "iopub.status.busy": "2024-08-08T18:53:05.010890Z",
- "iopub.status.idle": "2024-08-08T18:53:05.013680Z",
- "shell.execute_reply": "2024-08-08T18:53:05.013228Z"
+ "iopub.execute_input": "2024-08-12T10:31:05.174639Z",
+ "iopub.status.busy": "2024-08-12T10:31:05.174253Z",
+ "iopub.status.idle": "2024-08-12T10:31:05.177235Z",
+ "shell.execute_reply": "2024-08-12T10:31:05.176793Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:05.015527Z",
- "iopub.status.busy": "2024-08-08T18:53:05.015354Z",
- "iopub.status.idle": "2024-08-08T18:53:07.128537Z",
- "shell.execute_reply": "2024-08-08T18:53:07.127880Z"
+ "iopub.execute_input": "2024-08-12T10:31:05.179303Z",
+ "iopub.status.busy": "2024-08-12T10:31:05.178961Z",
+ "iopub.status.idle": "2024-08-12T10:31:07.400050Z",
+ "shell.execute_reply": "2024-08-12T10:31:07.399213Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:07.131691Z",
- "iopub.status.busy": "2024-08-08T18:53:07.130868Z",
- "iopub.status.idle": "2024-08-08T18:53:07.142106Z",
- "shell.execute_reply": "2024-08-08T18:53:07.141539Z"
+ "iopub.execute_input": "2024-08-12T10:31:07.403380Z",
+ "iopub.status.busy": "2024-08-12T10:31:07.402738Z",
+ "iopub.status.idle": "2024-08-12T10:31:07.414900Z",
+ "shell.execute_reply": "2024-08-12T10:31:07.414301Z"
}
},
"outputs": [
@@ -786,10 +786,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:07.144209Z",
- "iopub.status.busy": "2024-08-08T18:53:07.143893Z",
- "iopub.status.idle": "2024-08-08T18:53:07.205051Z",
- "shell.execute_reply": "2024-08-08T18:53:07.204457Z"
+ "iopub.execute_input": "2024-08-12T10:31:07.417422Z",
+ "iopub.status.busy": "2024-08-12T10:31:07.417172Z",
+ "iopub.status.idle": "2024-08-12T10:31:07.521745Z",
+ "shell.execute_reply": "2024-08-12T10:31:07.521237Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
index 6911507a3..14bfb32ea 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": "2024-08-08T18:53:10.467009Z",
- "iopub.status.busy": "2024-08-08T18:53:10.466580Z",
- "iopub.status.idle": "2024-08-08T18:53:13.885731Z",
- "shell.execute_reply": "2024-08-08T18:53:13.885078Z"
+ "iopub.execute_input": "2024-08-12T10:31:11.599918Z",
+ "iopub.status.busy": "2024-08-12T10:31:11.599739Z",
+ "iopub.status.idle": "2024-08-12T10:31:14.792390Z",
+ "shell.execute_reply": "2024-08-12T10:31:14.791829Z"
},
"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@ed1943228cd408bbef2343ae07f897ac0f8c96bd\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@399938be1f46b62c047276c21928e3071ce4ba6d\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": "2024-08-08T18:53:13.888448Z",
- "iopub.status.busy": "2024-08-08T18:53:13.887972Z",
- "iopub.status.idle": "2024-08-08T18:53:13.891865Z",
- "shell.execute_reply": "2024-08-08T18:53:13.891431Z"
+ "iopub.execute_input": "2024-08-12T10:31:14.794853Z",
+ "iopub.status.busy": "2024-08-12T10:31:14.794551Z",
+ "iopub.status.idle": "2024-08-12T10:31:14.797789Z",
+ "shell.execute_reply": "2024-08-12T10:31:14.797355Z"
}
},
"outputs": [],
@@ -185,10 +185,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:13.893972Z",
- "iopub.status.busy": "2024-08-08T18:53:13.893556Z",
- "iopub.status.idle": "2024-08-08T18:53:13.896736Z",
- "shell.execute_reply": "2024-08-08T18:53:13.896267Z"
+ "iopub.execute_input": "2024-08-12T10:31:14.799835Z",
+ "iopub.status.busy": "2024-08-12T10:31:14.799654Z",
+ "iopub.status.idle": "2024-08-12T10:31:14.803159Z",
+ "shell.execute_reply": "2024-08-12T10:31:14.802724Z"
},
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@@ -342,7 +342,7 @@
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- "_model_name": "HTMLStyleModel",
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- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
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- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_ef38a47462b242638331cd976aefeae2",
+ "placeholder": "",
+ "style": "IPY_MODEL_b95ea2b32e7d4b66a2a0e2873a01046d",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "README.md: 100%"
}
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@@ -3587,57 +3622,22 @@
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+ "model_name": "HTMLStyleModel",
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+ "_model_module": "@jupyter-widgets/controls",
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+ "_view_name": "StyleView",
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
index 65c0346d8..be2c44c4c 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@ed1943228cd408bbef2343ae07f897ac0f8c96bd\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@399938be1f46b62c047276c21928e3071ce4ba6d\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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@@ -617,10 +617,10 @@
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@@ -680,10 +680,10 @@
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@@ -717,10 +717,10 @@
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@@ -807,10 +807,10 @@
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@@ -870,10 +870,10 @@
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@@ -977,10 +977,10 @@
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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 323ce22c5..3b981fe19 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
@@ -80,10 +80,10 @@
"execution_count": 1,
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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@ed1943228cd408bbef2343ae07f897ac0f8c96bd\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@399938be1f46b62c047276c21928e3071ce4ba6d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
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@@ -118,10 +118,10 @@
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@@ -252,10 +252,10 @@
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@@ -353,10 +353,10 @@
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+ "iopub.execute_input": "2024-08-12T10:32:01.196739Z",
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@@ -445,10 +445,10 @@
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@@ -517,10 +517,10 @@
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@@ -569,10 +569,10 @@
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@@ -608,10 +608,10 @@
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@@ -642,10 +642,10 @@
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@@ -714,10 +714,10 @@
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@@ -830,10 +830,10 @@
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@@ -937,10 +937,10 @@
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- "value": " 132/132 [00:00<00:00, 11354.09 examples/s]"
+ "value": " 132/132 [00:00<00:00, 12983.33 examples/s]"
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@@ -1765,7 +1691,30 @@
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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 0b3e3aaf0..5a8714bfc 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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- "shell.execute_reply": "2024-08-08T18:54:01.882096Z"
+ "iopub.execute_input": "2024-08-12T10:32:06.983365Z",
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},
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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@ed1943228cd408bbef2343ae07f897ac0f8c96bd\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@399938be1f46b62c047276c21928e3071ce4ba6d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -116,10 +116,10 @@
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@@ -250,10 +250,10 @@
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@@ -356,10 +356,10 @@
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@@ -448,10 +448,10 @@
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@@ -520,10 +520,10 @@
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@@ -559,10 +559,10 @@
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@@ -638,10 +638,10 @@
"execution_count": 9,
"metadata": {
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@@ -685,10 +685,10 @@
"execution_count": 10,
"metadata": {
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}
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@@ -821,10 +821,10 @@
"execution_count": 11,
"metadata": {
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@@ -935,10 +935,10 @@
"execution_count": 12,
"metadata": {
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"outputs": [
@@ -1005,10 +1005,10 @@
"execution_count": 13,
"metadata": {
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}
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@@ -1200,10 +1200,10 @@
"execution_count": 14,
"metadata": {
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- "shell.execute_reply": "2024-08-08T18:54:04.542816Z"
+ "iopub.execute_input": "2024-08-12T10:32:11.150984Z",
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+ "shell.execute_reply": "2024-08-12T10:32:11.160022Z"
}
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"outputs": [
@@ -1319,10 +1319,10 @@
"execution_count": 15,
"metadata": {
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- "iopub.execute_input": "2024-08-08T18:54:04.545549Z",
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- "shell.execute_reply": "2024-08-08T18:54:04.551568Z"
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+ "shell.execute_reply": "2024-08-12T10:32:11.169691Z"
},
"scrolled": true
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@@ -1447,10 +1447,10 @@
"execution_count": 16,
"metadata": {
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- "iopub.execute_input": "2024-08-08T18:54:04.554444Z",
- "iopub.status.busy": "2024-08-08T18:54:04.554013Z",
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- "shell.execute_reply": "2024-08-08T18:54:04.563132Z"
+ "iopub.execute_input": "2024-08-12T10:32:11.172574Z",
+ "iopub.status.busy": "2024-08-12T10:32:11.172225Z",
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+ "shell.execute_reply": "2024-08-12T10:32:11.182213Z"
}
},
"outputs": [
@@ -1553,10 +1553,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:54:04.565603Z",
- "iopub.status.busy": "2024-08-08T18:54:04.565425Z",
- "iopub.status.idle": "2024-08-08T18:54:04.581683Z",
- "shell.execute_reply": "2024-08-08T18:54:04.581197Z"
+ "iopub.execute_input": "2024-08-12T10:32:11.184974Z",
+ "iopub.status.busy": "2024-08-12T10:32:11.184656Z",
+ "iopub.status.idle": "2024-08-12T10:32:11.201470Z",
+ "shell.execute_reply": "2024-08-12T10:32:11.200965Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
index 688ba5df2..4e6eaa9a6 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
@@ -71,10 +71,10 @@
"execution_count": 1,
"metadata": {
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- "iopub.execute_input": "2024-08-08T18:54:07.617460Z",
- "iopub.status.busy": "2024-08-08T18:54:07.617283Z",
- "iopub.status.idle": "2024-08-08T18:54:10.626935Z",
- "shell.execute_reply": "2024-08-08T18:54:10.626277Z"
+ "iopub.execute_input": "2024-08-12T10:32:14.108295Z",
+ "iopub.status.busy": "2024-08-12T10:32:14.107801Z",
+ "iopub.status.idle": "2024-08-12T10:32:17.199789Z",
+ "shell.execute_reply": "2024-08-12T10:32:17.199158Z"
},
"nbsphinx": "hidden"
},
@@ -112,10 +112,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:54:10.629473Z",
- "iopub.status.busy": "2024-08-08T18:54:10.629174Z",
- "iopub.status.idle": "2024-08-08T18:54:10.632947Z",
- "shell.execute_reply": "2024-08-08T18:54:10.632379Z"
+ "iopub.execute_input": "2024-08-12T10:32:17.202525Z",
+ "iopub.status.busy": "2024-08-12T10:32:17.201957Z",
+ "iopub.status.idle": "2024-08-12T10:32:17.205593Z",
+ "shell.execute_reply": "2024-08-12T10:32:17.205132Z"
}
},
"outputs": [],
@@ -152,17 +152,17 @@
"execution_count": 3,
"metadata": {
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- "iopub.execute_input": "2024-08-08T18:54:10.634959Z",
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- "iopub.status.idle": "2024-08-08T18:54:13.553657Z",
- "shell.execute_reply": "2024-08-08T18:54:13.553098Z"
+ "iopub.execute_input": "2024-08-12T10:32:17.207722Z",
+ "iopub.status.busy": "2024-08-12T10:32:17.207391Z",
+ "iopub.status.idle": "2024-08-12T10:32:22.832822Z",
+ "shell.execute_reply": "2024-08-12T10:32:22.832329Z"
}
},
"outputs": [
{
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- "model_id": "c9b4b8e5c57b4261b88992e28dd46275",
+ "model_id": "24950e57ccc94aaaa079c9d5b86c6053",
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"version_minor": 0
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@@ -176,7 +176,7 @@
{
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+ "model_id": "ac8e9ae825dd46cda3caaf717ab3f457",
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"version_minor": 0
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@@ -190,7 +190,7 @@
{
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+ "model_id": "d71df3a63bda4854a0d7e51c67182ab4",
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"version_minor": 0
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@@ -204,7 +204,7 @@
{
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+ "model_id": "df25fafe8e5749a69234408c18364b66",
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@@ -218,7 +218,7 @@
{
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+ "model_id": "659ce9293f064abe8640605a65b6aeb5",
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"version_minor": 0
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@@ -260,10 +260,10 @@
"execution_count": 4,
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- "shell.execute_reply": "2024-08-08T18:54:13.558864Z"
+ "iopub.execute_input": "2024-08-12T10:32:22.835079Z",
+ "iopub.status.busy": "2024-08-12T10:32:22.834720Z",
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+ "shell.execute_reply": "2024-08-12T10:32:22.838043Z"
}
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"outputs": [
@@ -288,17 +288,17 @@
"execution_count": 5,
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- "shell.execute_reply": "2024-08-08T18:54:25.209506Z"
+ "iopub.execute_input": "2024-08-12T10:32:22.840738Z",
+ "iopub.status.busy": "2024-08-12T10:32:22.840420Z",
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+ "shell.execute_reply": "2024-08-12T10:32:34.876879Z"
}
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- "model_id": "12503c89e5554d0c8b1cda02ea9c897f",
+ "model_id": "b7ff9d3e760d46ab9410530722e86f1c",
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@@ -336,10 +336,10 @@
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- "shell.execute_reply": "2024-08-08T18:54:43.497413Z"
+ "iopub.execute_input": "2024-08-12T10:32:34.880019Z",
+ "iopub.status.busy": "2024-08-12T10:32:34.879767Z",
+ "iopub.status.idle": "2024-08-12T10:32:53.521520Z",
+ "shell.execute_reply": "2024-08-12T10:32:53.520954Z"
}
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@@ -372,10 +372,10 @@
"execution_count": 7,
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- "shell.execute_reply": "2024-08-08T18:54:43.505568Z"
+ "iopub.execute_input": "2024-08-12T10:32:53.524328Z",
+ "iopub.status.busy": "2024-08-12T10:32:53.523932Z",
+ "iopub.status.idle": "2024-08-12T10:32:53.529696Z",
+ "shell.execute_reply": "2024-08-12T10:32:53.529222Z"
}
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@@ -413,10 +413,10 @@
"execution_count": 8,
"metadata": {
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- "iopub.status.idle": "2024-08-08T18:54:43.512417Z",
- "shell.execute_reply": "2024-08-08T18:54:43.511992Z"
+ "iopub.execute_input": "2024-08-12T10:32:53.531672Z",
+ "iopub.status.busy": "2024-08-12T10:32:53.531382Z",
+ "iopub.status.idle": "2024-08-12T10:32:53.535613Z",
+ "shell.execute_reply": "2024-08-12T10:32:53.535051Z"
},
"nbsphinx": "hidden"
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@@ -553,10 +553,10 @@
"execution_count": 9,
"metadata": {
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- "iopub.status.busy": "2024-08-08T18:54:43.514357Z",
- "iopub.status.idle": "2024-08-08T18:54:43.523235Z",
- "shell.execute_reply": "2024-08-08T18:54:43.522721Z"
+ "iopub.execute_input": "2024-08-12T10:32:53.537903Z",
+ "iopub.status.busy": "2024-08-12T10:32:53.537489Z",
+ "iopub.status.idle": "2024-08-12T10:32:53.546584Z",
+ "shell.execute_reply": "2024-08-12T10:32:53.546030Z"
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"nbsphinx": "hidden"
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@@ -681,10 +681,10 @@
"execution_count": 10,
"metadata": {
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- "shell.execute_reply": "2024-08-08T18:54:43.550807Z"
+ "iopub.execute_input": "2024-08-12T10:32:53.548705Z",
+ "iopub.status.busy": "2024-08-12T10:32:53.548357Z",
+ "iopub.status.idle": "2024-08-12T10:32:53.576832Z",
+ "shell.execute_reply": "2024-08-12T10:32:53.576340Z"
}
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"outputs": [],
@@ -721,10 +721,10 @@
"execution_count": 11,
"metadata": {
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- "iopub.execute_input": "2024-08-08T18:54:43.553582Z",
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- "iopub.status.idle": "2024-08-08T18:55:16.306900Z",
- "shell.execute_reply": "2024-08-08T18:55:16.306225Z"
+ "iopub.execute_input": "2024-08-12T10:32:53.579268Z",
+ "iopub.status.busy": "2024-08-12T10:32:53.578915Z",
+ "iopub.status.idle": "2024-08-12T10:33:28.300304Z",
+ "shell.execute_reply": "2024-08-12T10:33:28.299664Z"
}
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"outputs": [
@@ -740,21 +740,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.909\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.112\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.600\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.743\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
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- "model_id": "58e473c5367e4a1b9a53bb8a96929532",
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@@ -775,7 +775,7 @@
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+ "model_id": "3f27976bac3c458aa1b2cc0fb4b52d2c",
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},
@@ -798,21 +798,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.885\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.025\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.612\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.947\n",
"Computing feature embeddings ...\n"
]
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- "model_id": "e1fe80d24ad54c8c9915159f98eab61d",
+ "model_id": "791dc4f1fa2e4226ae567d555fc24805",
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@@ -833,7 +833,7 @@
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- "model_id": "c9d9ed6ef9604c189eaa8d491ed7f431",
+ "model_id": "31a3859cc0e34abe854259d21e40f2b5",
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@@ -856,21 +856,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.765\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.366\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.617\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.925\n",
"Computing feature embeddings ...\n"
]
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- "model_id": "fdc592e8c3fe4972a89cb71e5b08f902",
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@@ -891,7 +891,7 @@
{
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+ "model_id": "35bfdb221038403b86fc1c1dfba20630",
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"version_minor": 0
},
@@ -970,10 +970,10 @@
"execution_count": 12,
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- "iopub.execute_input": "2024-08-08T18:55:16.309502Z",
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@@ -998,10 +998,10 @@
"execution_count": 13,
"metadata": {
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- "shell.execute_reply": "2024-08-08T18:55:16.789362Z"
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+ "shell.execute_reply": "2024-08-12T10:33:28.802112Z"
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"outputs": [],
@@ -1021,10 +1021,10 @@
"execution_count": 14,
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-08-08T18:57:07.452805Z"
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+ "iopub.status.busy": "2024-08-12T10:33:28.804800Z",
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+ "shell.execute_reply": "2024-08-12T10:35:20.178277Z"
}
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"outputs": [
@@ -1063,7 +1063,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "4ee5125ee6464710b79aa17cdcd4da98",
+ "model_id": "34f053a0e82b47f29b9b2f6a619f4c72",
"version_major": 2,
"version_minor": 0
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@@ -1108,10 +1108,10 @@
"execution_count": 15,
"metadata": {
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- "iopub.status.busy": "2024-08-08T18:57:07.455445Z",
- "iopub.status.idle": "2024-08-08T18:57:07.930283Z",
- "shell.execute_reply": "2024-08-08T18:57:07.929742Z"
+ "iopub.execute_input": "2024-08-12T10:35:20.181523Z",
+ "iopub.status.busy": "2024-08-12T10:35:20.181116Z",
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+ "shell.execute_reply": "2024-08-12T10:35:20.636521Z"
}
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"outputs": [
@@ -1257,10 +1257,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:57:07.933098Z",
- "iopub.status.busy": "2024-08-08T18:57:07.932614Z",
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- "shell.execute_reply": "2024-08-08T18:57:07.994153Z"
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}
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"outputs": [
@@ -1364,10 +1364,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:57:07.996907Z",
- "iopub.status.busy": "2024-08-08T18:57:07.996624Z",
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- "shell.execute_reply": "2024-08-08T18:57:08.004641Z"
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+ "iopub.status.busy": "2024-08-12T10:35:20.702336Z",
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}
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"outputs": [
@@ -1497,10 +1497,10 @@
"execution_count": 18,
"metadata": {
"execution": {
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@@ -1546,10 +1546,10 @@
"execution_count": 19,
"metadata": {
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- "shell.execute_reply": "2024-08-08T18:57:08.521457Z"
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"outputs": [
@@ -1584,10 +1584,10 @@
"execution_count": 20,
"metadata": {
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- "shell.execute_reply": "2024-08-08T18:57:08.531618Z"
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+ "iopub.status.busy": "2024-08-12T10:35:21.248103Z",
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}
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"outputs": [
@@ -1754,10 +1754,10 @@
"execution_count": 21,
"metadata": {
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@@ -1833,10 +1833,10 @@
"execution_count": 22,
"metadata": {
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- "shell.execute_reply": "2024-08-08T18:57:09.289312Z"
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}
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"outputs": [
@@ -1873,10 +1873,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:57:09.292436Z",
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- "iopub.status.idle": "2024-08-08T18:57:09.307223Z",
- "shell.execute_reply": "2024-08-08T18:57:09.306667Z"
+ "iopub.execute_input": "2024-08-12T10:35:22.018300Z",
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+ "shell.execute_reply": "2024-08-12T10:35:22.032325Z"
}
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"outputs": [
@@ -2033,10 +2033,10 @@
"execution_count": 24,
"metadata": {
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- "iopub.execute_input": "2024-08-08T18:57:09.309352Z",
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- "shell.execute_reply": "2024-08-08T18:57:09.313983Z"
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@@ -2081,10 +2081,10 @@
"execution_count": 25,
"metadata": {
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- "shell.execute_reply": "2024-08-08T18:57:09.778921Z"
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"outputs": [
@@ -2166,10 +2166,10 @@
"execution_count": 26,
"metadata": {
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- "shell.execute_reply": "2024-08-08T18:57:09.791345Z"
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+ "shell.execute_reply": "2024-08-12T10:35:22.515169Z"
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"outputs": [
@@ -2297,10 +2297,10 @@
"execution_count": 27,
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-08-08T18:57:09.799352Z"
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@@ -2337,10 +2337,10 @@
"execution_count": 28,
"metadata": {
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- "shell.execute_reply": "2024-08-08T18:57:10.005710Z"
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+ "shell.execute_reply": "2024-08-12T10:35:22.732666Z"
}
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"outputs": [
@@ -2382,10 +2382,10 @@
"execution_count": 29,
"metadata": {
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- "shell.execute_reply": "2024-08-08T18:57:10.015534Z"
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+ "shell.execute_reply": "2024-08-12T10:35:22.742595Z"
}
},
"outputs": [
@@ -2410,47 +2410,47 @@
" \n",
" \n",
" \n",
" \n",
" \n",
- " is_low_information_issue \n",
" low_information_score \n",
+ " is_low_information_issue \n",
"
\n", - " | Age | \n", - "Gender | \n", - "Location | \n", - "Annual_Spending | \n", - "Number_of_Transactions | \n", - "Last_Purchase_Date | \n", - "| | \n", - "is_null_issue | \n", - "null_score | \n", + "Age | \n", + "Gender | \n", + "Location | \n", + "Annual_Spending | \n", + "Number_of_Transactions | \n", + "Last_Purchase_Date | \n", + "| | \n", + "is_null_issue | \n", + "null_score | \n", "|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8 | \n", - "nan | \n", - "nan | \n", - "nan | \n", - "nan | \n", - "nan | \n", - "NaT | \n", - "\n", - " | True | \n", - "0.000000 | \n", - "||||||||||
1 | \n", - "nan | \n", - "Female | \n", - "Rural | \n", - "6421.160000 | \n", - "5.000000 | \n", - "NaT | \n", - "\n", - " | False | \n", - "0.666667 | \n", - "||||||||||
9 | \n", - "nan | \n", - "Male | \n", - "Rural | \n", - "4655.820000 | \n", - "1.000000 | \n", - "NaT | \n", - "\n", - " | False | \n", - "0.666667 | \n", - "||||||||||
14 | \n", - "nan | \n", - "Male | \n", - "Rural | \n", - "6790.460000 | \n", - "3.000000 | \n", - "NaT | \n", - "\n", - " | False | \n", - "0.666667 | \n", - "||||||||||
13 | \n", - "nan | \n", - "Male | \n", - "Urban | \n", - "9167.470000 | \n", - "4.000000 | \n", - "2024-01-02 00:00:00 | \n", - "\n", - " | False | \n", - "0.833333 | \n", - "||||||||||
15 | \n", - "nan | \n", - "Other | \n", - "Rural | \n", - "5327.960000 | \n", - "8.000000 | \n", - "2024-01-03 00:00:00 | \n", - "\n", - " | False | \n", - "0.833333 | \n", - "||||||||||
0 | \n", - "56.000000 | \n", - "Other | \n", - "Rural | \n", - "4099.620000 | \n", - "3.000000 | \n", - "2024-01-03 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
2 | \n", - "46.000000 | \n", - "Male | \n", - "Suburban | \n", - "5436.550000 | \n", - "3.000000 | \n", - "2024-02-26 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
3 | \n", - "32.000000 | \n", - "Female | \n", - "Rural | \n", - "4046.660000 | \n", - "3.000000 | \n", - "2024-03-23 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
4 | \n", - "60.000000 | \n", - "Female | \n", - "Suburban | \n", - "3467.670000 | \n", - "6.000000 | \n", - "2024-03-01 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
5 | \n", - "25.000000 | \n", - "Female | \n", - "Suburban | \n", - "4757.370000 | \n", - "4.000000 | \n", - "2024-01-03 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
6 | \n", - "38.000000 | \n", - "Female | \n", - "Rural | \n", - "4199.530000 | \n", - "6.000000 | \n", - "2024-01-03 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
7 | \n", - "56.000000 | \n", - "Male | \n", - "Suburban | \n", - "4991.710000 | \n", - "6.000000 | \n", - "2024-04-03 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
10 | \n", - "40.000000 | \n", - "Female | \n", - "Rural | \n", - "5584.020000 | \n", - "7.000000 | \n", - "2024-03-29 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
11 | \n", - "28.000000 | \n", - "Female | \n", - "Urban | \n", - "3102.320000 | \n", - "2.000000 | \n", - "2024-04-07 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
12 | \n", - "28.000000 | \n", - "Male | \n", - "Rural | \n", - "6637.990000 | \n", - "11.000000 | \n", - "2024-04-08 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", + "8 | \n", + "nan | \n", + "nan | \n", + "nan | \n", + "nan | \n", + "nan | \n", + "NaT | \n", + "\n", + " | True | \n", + "0.000000 | \n", + "
1 | \n", + "nan | \n", + "Female | \n", + "Rural | \n", + "6421.160000 | \n", + "5.000000 | \n", + "NaT | \n", + "\n", + " | False | \n", + "0.666667 | \n", + "||||||||||
9 | \n", + "nan | \n", + "Male | \n", + "Rural | \n", + "4655.820000 | \n", + "1.000000 | \n", + "NaT | \n", + "\n", + " | False | \n", + "0.666667 | \n", + "||||||||||
14 | \n", + "nan | \n", + "Male | \n", + "Rural | \n", + "6790.460000 | \n", + "3.000000 | \n", + "NaT | \n", + "\n", + " | False | \n", + "0.666667 | \n", + "||||||||||
13 | \n", + "nan | \n", + "Male | \n", + "Urban | \n", + "9167.470000 | \n", + "4.000000 | \n", + "2024-01-02 00:00:00 | \n", + "\n", + " | False | \n", + "0.833333 | \n", + "||||||||||
15 | \n", + "nan | \n", + "Other | \n", + "Rural | \n", + "5327.960000 | \n", + "8.000000 | \n", + "2024-01-03 00:00:00 | \n", + "\n", + " | False | \n", + "0.833333 | \n", + "||||||||||
0 | \n", + "56.000000 | \n", + "Other | \n", + "Rural | \n", + "4099.620000 | \n", + "3.000000 | \n", + "2024-01-03 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
2 | \n", + "46.000000 | \n", + "Male | \n", + "Suburban | \n", + "5436.550000 | \n", + "3.000000 | \n", + "2024-02-26 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
3 | \n", + "32.000000 | \n", + "Female | \n", + "Rural | \n", + "4046.660000 | \n", + "3.000000 | \n", + "2024-03-23 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
4 | \n", + "60.000000 | \n", + "Female | \n", + "Suburban | \n", + "3467.670000 | \n", + "6.000000 | \n", + "2024-03-01 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
5 | \n", + "25.000000 | \n", + "Female | \n", + "Suburban | \n", + "4757.370000 | \n", + "4.000000 | \n", + "2024-01-03 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
6 | \n", + "38.000000 | \n", + "Female | \n", + "Rural | \n", + "4199.530000 | \n", + "6.000000 | \n", + "2024-01-03 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
7 | \n", + "56.000000 | \n", + "Male | \n", + "Suburban | \n", + "4991.710000 | \n", + "6.000000 | \n", + "2024-04-03 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
10 | \n", + "40.000000 | \n", + "Female | \n", + "Rural | \n", + "5584.020000 | \n", + "7.000000 | \n", + "2024-03-29 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
11 | \n", + "28.000000 | \n", + "Female | \n", + "Urban | \n", + "3102.320000 | \n", + "2.000000 | \n", + "2024-04-07 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
12 | \n", + "28.000000 | \n", + "Male | \n", + "Rural | \n", + "6637.990000 | \n", + "11.000000 | \n", + "2024-04-08 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", "
clea
-
-2. Find common issues in your data#
-cleanlab automatically detects various issues in any dataset that a classifier can be trained on. The cleanlab package works with any ML model by operating on model outputs (predicted class probabilities or feature embeddings) – it doesn’t require that a particular model created those outputs. For any classification dataset, use your trained model to produce pred_probs
(predicted class probabilities) and/or feature_embeddings
(numeric vector representations of each datapoint). Then, these few lines of code can detect common real-world issues in your dataset like label errors, outliers, near duplicates, etc:
+
+2. Check your data for all sorts of issues#
+cleanlab automatically detects various issues in any dataset that a classifier can be trained on. The cleanlab package works with any ML model by operating on model outputs (predicted class probabilities or feature embeddings) – it doesn’t require that a particular model created those outputs. For any classification dataset, use your trained model to produce pred_probs
(predicted class probabilities) and/or feature_embeddings
(numeric vector representations of each datapoint). To automatically check your dataset for common real-world issues (like label errors, outliers, near duplicates, IID violations, underperforming groups, …), simply run these few lines of code:
from cleanlab import Datalab
lab = Datalab(data=your_dataset, label_name="column_name_of_labels")
lab.find_issues(features=feature_embeddings, pred_probs=pred_probs)
-lab.report() # summarize issues in dataset, how severe they are, ...
+lab.report() # summarize issues in dataset, how severe they are in each data point, ...
+While other data quality tools only catch limited types of data issues based on manually pre-defined validation rules, cleanlab applies automated data-centric AI techniques using your trained ML model to detect many more types of data issues that would otherwise be hard to catch. Don’t dive into ML model improvement without first using AI to help check your data!
3. Handle label errors and train robust models with noisy labels#
@@ -672,7 +673,7 @@ 3. Handle label errors and train robust models with noisy labelslabel_issues_info
= CleanLearning(clf=sklearn_compatible_model).find_label_issues(data, labels)
-CleanLearning
also works with models from most standard ML frameworks by wrapping the model for scikit-learn compliance, e.g. pytorch (can use skorch package), tensorflow/keras (can use our :py:class:`KerasWrapperModel <cleanlab/models/keras>`_), etc.
+CleanLearning
also works with models from most standard ML frameworks by wrapping the model for scikit-learn compliance, e.g. pytorch (can use skorch package), tensorflow/keras (can use our KerasWrapperModel
), etc.
find_label_issues
returns a boolean mask flagging which examples have label issues and a numeric label quality score for each example quantifying our confidence that its label is correct.
Beyond standard classification tasks, cleanlab can also detect mislabeled examples in: multi-label data (e.g. image/document tagging), sequence prediction (e.g. entity recognition), and data labeled by multiple annotators (e.g. crowdsourcing).
@@ -720,7 +721,7 @@ Contributing#
While this open-source library finds data issues, its utility depends on you having a good ML model and interface to efficiently fix these issues in your dataset. Providing all these pieces, Cleanlab Studio is a no-code platform to find and fix problems in image/text/tabular datasets. Cleanlab Studio integrates the data quality algorithms from this library on top of cutting-edge AutoML & Foundation models fit to your data, and presents detected issues in a smart data editing interface.
-There is no easier way to turn unreliable raw data into reliable models/analytics. Try it for free!
+There is no faster way to turn unreliable raw data into reliable models/analytics. Try it for free!
Link to Cleanlab Studio docs: help.cleanlab.ai
diff --git a/master/objects.inv b/master/objects.inv
index a67bbb38d..20de89e70 100644
Binary files a/master/objects.inv and b/master/objects.inv differ
diff --git a/master/searchindex.js b/master/searchindex.js
index 2612ac4df..d66f30c25 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
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Train a more robust model from noisy labels": [[86, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Spending too much time on data quality?": [[86, "Spending-too-much-time-on-data-quality?"], [87, "Spending-too-much-time-on-data-quality?"], [90, "Spending-too-much-time-on-data-quality?"], [93, "Spending-too-much-time-on-data-quality?"], [94, "Spending-too-much-time-on-data-quality?"], [96, "Spending-too-much-time-on-data-quality?"], [99, "Spending-too-much-time-on-data-quality?"], [102, "Spending-too-much-time-on-data-quality?"], [104, "Spending-too-much-time-on-data-quality?"], [105, "spending-too-much-time-on-data-quality"], [106, "Spending-too-much-time-on-data-quality?"]], "Text Classification with Noisy Labels": [[87, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[87, "2.-Load-and-format-the-text-dataset"], [94, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[87, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[87, "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": [[88, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[88, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[88, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[88, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[88, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[88, "5.-Use-cleanlab-to-find-label-issues"], [93, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[89, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[89, "Install-and-import-required-dependencies"]], "Create and load the data": [[89, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[89, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[89, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[89, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[89, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[89, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[89, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[90, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[90, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[90, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[90, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[90, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[90, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[90, "Get-additional-information"]], "Near duplicate issues": [[90, "Near-duplicate-issues"], [91, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[91, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[91, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[91, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[91, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[91, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[91, "7.-Use-cleanlab-to-find-issues"]], "View report": [[91, "View-report"]], "Label issues": [[91, "Label-issues"], [93, "Label-issues"], [94, "Label-issues"]], "View most likely examples with label errors": [[91, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[91, "Outlier-issues"], [93, "Outlier-issues"], [94, "Outlier-issues"]], "View most severe outliers": [[91, "View-most-severe-outliers"]], "View sets of near duplicate images": [[91, "View-sets-of-near-duplicate-images"]], "Dark images": [[91, "Dark-images"]], "View top examples of dark images": [[91, "View-top-examples-of-dark-images"]], "Low information images": [[91, "Low-information-images"]], "Datalab Tutorials": [[92, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[93, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[93, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[93, "Near-duplicate-issues"], [94, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[94, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[94, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[94, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[94, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[95, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[95, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[95, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[95, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[95, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[95, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[95, "Explanation:"]], "Data Valuation": [[95, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[95, "1.-Load-and-Prepare-the-Dataset"], [95, "id2"], [95, "id5"]], "2. Vectorize the Text Data": [[95, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[95, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[95, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[95, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[95, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[95, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [95, "id3"]], "3. (Optional) Cluster the Data": [[95, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[95, "4.-Identify-Underperforming-Groups-with-Datalab"], [95, "id4"]], "5. (Optional) Visualize the Results": [[95, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[95, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[95, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[95, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[95, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[95, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[95, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[95, "1.-Load-the-Dataset"], [95, "id8"]], "2: Encode Categorical Values": [[95, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[95, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[95, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[95, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[95, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[95, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[95, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[95, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[95, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[95, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[95, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[95, "3.-Interpret-the-Results"]], "4. (Optional) Compare with a Dataset Without Spurious Correlations": [[95, "4.-(Optional)-Compare-with-a-Dataset-Without-Spurious-Correlations"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "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?": [[97, "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?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[97, "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?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[98, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[98, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[98, "1.-Install-dependencies"]], "2. Preprocess the data": [[98, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[98, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[98, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[98, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[98, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[98, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[98, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[98, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[98, "9.-Conclusion"]], "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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"cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, 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"latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "models": [[59, "models"]], "keras": [[60, "module-cleanlab.models.keras"]], "multiannotator": [[61, "module-cleanlab.multiannotator"]], "multilabel_classification": [[64, "multilabel-classification"]], "rank": [[65, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.rank"], [77, "module-cleanlab.segmentation.rank"], [81, "module-cleanlab.token_classification.rank"]], "object_detection": [[67, "object-detection"]], "summary": [[69, "summary"], [78, "module-cleanlab.segmentation.summary"], [82, "module-cleanlab.token_classification.summary"]], "regression.learn": [[73, "module-cleanlab.regression.learn"]], "regression.rank": [[74, "module-cleanlab.regression.rank"]], "segmentation": [[76, "segmentation"]], "token_classification": [[80, "token-classification"]], "cleanlab open-source documentation": [[83, "cleanlab-open-source-documentation"]], "Quickstart": [[83, "quickstart"]], "1. Install cleanlab": [[83, "install-cleanlab"]], "2. Check your data for all sorts of issues": [[83, "check-your-data-for-all-sorts-of-issues"]], "3. Handle label errors and train robust models with noisy labels": [[83, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[83, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[83, "improve-your-data-via-many-other-techniques"]], "Contributing": [[83, "contributing"]], "Easy Mode": [[83, "easy-mode"], [91, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[84, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[84, "function-and-class-name-changes"]], "Module name changes": [[84, "module-name-changes"]], "New modules": [[84, "new-modules"]], "Removed modules": [[84, "removed-modules"]], "Common argument and variable name changes": [[84, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[85, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[86, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[86, "1.-Install-required-dependencies"], [87, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[86, "2.-Load-and-process-the-data"], [93, "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": [[86, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [93, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[86, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[86, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Spending too much time on data quality?": [[86, "Spending-too-much-time-on-data-quality?"], [87, "Spending-too-much-time-on-data-quality?"], [90, "Spending-too-much-time-on-data-quality?"], [93, "Spending-too-much-time-on-data-quality?"], [94, "Spending-too-much-time-on-data-quality?"], [96, "Spending-too-much-time-on-data-quality?"], [99, "Spending-too-much-time-on-data-quality?"], [102, "Spending-too-much-time-on-data-quality?"], [104, "Spending-too-much-time-on-data-quality?"], [105, "spending-too-much-time-on-data-quality"], [106, "Spending-too-much-time-on-data-quality?"]], "Text Classification with Noisy Labels": [[87, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[87, "2.-Load-and-format-the-text-dataset"], [94, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[87, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[87, "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": [[88, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[88, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[88, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[88, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[88, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[88, "5.-Use-cleanlab-to-find-label-issues"], [93, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[89, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[89, "Install-and-import-required-dependencies"]], "Create and load the data": [[89, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[89, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[89, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[89, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[89, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[89, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[89, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[90, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[90, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[90, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[90, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[90, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[90, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[90, "Get-additional-information"]], "Near duplicate issues": [[90, "Near-duplicate-issues"], [91, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[91, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[91, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[91, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[91, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[91, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[91, "7.-Use-cleanlab-to-find-issues"]], "View report": [[91, "View-report"]], "Label issues": [[91, "Label-issues"], [93, "Label-issues"], [94, "Label-issues"]], "View most likely examples with label errors": [[91, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[91, "Outlier-issues"], [93, "Outlier-issues"], [94, "Outlier-issues"]], "View most severe outliers": [[91, "View-most-severe-outliers"]], "View sets of near duplicate images": [[91, "View-sets-of-near-duplicate-images"]], "Dark images": [[91, "Dark-images"]], "View top examples of dark images": [[91, "View-top-examples-of-dark-images"]], "Low information images": [[91, "Low-information-images"]], "Datalab Tutorials": [[92, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[93, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[93, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[93, "Near-duplicate-issues"], [94, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[94, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[94, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[94, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[94, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[95, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[95, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[95, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[95, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[95, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[95, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[95, "Explanation:"]], "Data Valuation": [[95, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[95, "1.-Load-and-Prepare-the-Dataset"], [95, "id2"], [95, "id5"]], "2. Vectorize the Text Data": [[95, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[95, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[95, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[95, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[95, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[95, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [95, "id3"]], "3. (Optional) Cluster the Data": [[95, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[95, "4.-Identify-Underperforming-Groups-with-Datalab"], [95, "id4"]], "5. (Optional) Visualize the Results": [[95, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[95, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[95, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[95, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[95, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[95, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[95, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[95, "1.-Load-the-Dataset"], [95, "id8"]], "2: Encode Categorical Values": [[95, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[95, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[95, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[95, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[95, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[95, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[95, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[95, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[95, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[95, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[95, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[95, "3.-Interpret-the-Results"]], "4. (Optional) Compare with a Dataset Without Spurious Correlations": [[95, "4.-(Optional)-Compare-with-a-Dataset-Without-Spurious-Correlations"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "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?": [[97, "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?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[97, "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?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[98, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[98, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[98, "1.-Install-dependencies"]], "2. Preprocess the data": [[98, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[98, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[98, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[98, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[98, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[98, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[98, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[98, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[98, "9.-Conclusion"]], "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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"cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[59, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[60, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[60, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[60, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[61, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[62, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[63, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[63, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[63, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[64, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[65, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[65, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[65, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[66, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[66, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[67, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[68, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[69, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[70, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[70, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[71, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[71, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[71, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[72, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[73, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[73, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[73, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[74, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[74, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[75, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[75, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[76, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[77, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[77, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[77, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[78, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[79, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[79, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[80, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[81, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[81, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[81, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[82, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[82, "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 524ef1091..813d71348 100644
--- a/master/tutorials/clean_learning/tabular.ipynb
+++ b/master/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:00.543675Z",
- "iopub.status.busy": "2024-08-08T18:53:00.543262Z",
- "iopub.status.idle": "2024-08-08T18:53:02.024210Z",
- "shell.execute_reply": "2024-08-08T18:53:02.023643Z"
+ "iopub.execute_input": "2024-08-12T10:31:00.463356Z",
+ "iopub.status.busy": "2024-08-12T10:31:00.462851Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.047524Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.046835Z"
},
"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@ed1943228cd408bbef2343ae07f897ac0f8c96bd\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@399938be1f46b62c047276c21928e3071ce4ba6d\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": "2024-08-08T18:53:02.026764Z",
- "iopub.status.busy": "2024-08-08T18:53:02.026455Z",
- "iopub.status.idle": "2024-08-08T18:53:02.045763Z",
- "shell.execute_reply": "2024-08-08T18:53:02.045207Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.050367Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.049990Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.069900Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.069290Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:02.048477Z",
- "iopub.status.busy": "2024-08-08T18:53:02.047929Z",
- "iopub.status.idle": "2024-08-08T18:53:02.259654Z",
- "shell.execute_reply": "2024-08-08T18:53:02.259026Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.072544Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.072099Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.302446Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.301780Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:02.294496Z",
- "iopub.status.busy": "2024-08-08T18:53:02.294073Z",
- "iopub.status.idle": "2024-08-08T18:53:02.297700Z",
- "shell.execute_reply": "2024-08-08T18:53:02.297264Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.334639Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.334107Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.338211Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.337651Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:02.299789Z",
- "iopub.status.busy": "2024-08-08T18:53:02.299487Z",
- "iopub.status.idle": "2024-08-08T18:53:02.308052Z",
- "shell.execute_reply": "2024-08-08T18:53:02.307601Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.340501Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.340140Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.348891Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.348288Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:02.310321Z",
- "iopub.status.busy": "2024-08-08T18:53:02.309983Z",
- "iopub.status.idle": "2024-08-08T18:53:02.312486Z",
- "shell.execute_reply": "2024-08-08T18:53:02.312044Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.351479Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.351121Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.353720Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.353249Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:02.314538Z",
- "iopub.status.busy": "2024-08-08T18:53:02.314203Z",
- "iopub.status.idle": "2024-08-08T18:53:02.834221Z",
- "shell.execute_reply": "2024-08-08T18:53:02.833675Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.355946Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.355599Z",
+ "iopub.status.idle": "2024-08-12T10:31:02.883586Z",
+ "shell.execute_reply": "2024-08-12T10:31:02.883087Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:02.836846Z",
- "iopub.status.busy": "2024-08-08T18:53:02.836453Z",
- "iopub.status.idle": "2024-08-08T18:53:04.867166Z",
- "shell.execute_reply": "2024-08-08T18:53:04.866464Z"
+ "iopub.execute_input": "2024-08-12T10:31:02.886048Z",
+ "iopub.status.busy": "2024-08-12T10:31:02.885693Z",
+ "iopub.status.idle": "2024-08-12T10:31:05.027547Z",
+ "shell.execute_reply": "2024-08-12T10:31:05.026915Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:04.869789Z",
- "iopub.status.busy": "2024-08-08T18:53:04.869146Z",
- "iopub.status.idle": "2024-08-08T18:53:04.879653Z",
- "shell.execute_reply": "2024-08-08T18:53:04.879122Z"
+ "iopub.execute_input": "2024-08-12T10:31:05.030654Z",
+ "iopub.status.busy": "2024-08-12T10:31:05.029701Z",
+ "iopub.status.idle": "2024-08-12T10:31:05.040437Z",
+ "shell.execute_reply": "2024-08-12T10:31:05.039970Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:04.881845Z",
- "iopub.status.busy": "2024-08-08T18:53:04.881386Z",
- "iopub.status.idle": "2024-08-08T18:53:04.885661Z",
- "shell.execute_reply": "2024-08-08T18:53:04.885109Z"
+ "iopub.execute_input": "2024-08-12T10:31:05.042737Z",
+ "iopub.status.busy": "2024-08-12T10:31:05.042378Z",
+ "iopub.status.idle": "2024-08-12T10:31:05.046796Z",
+ "shell.execute_reply": "2024-08-12T10:31:05.046322Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:04.888005Z",
- "iopub.status.busy": "2024-08-08T18:53:04.887662Z",
- "iopub.status.idle": "2024-08-08T18:53:04.894381Z",
- "shell.execute_reply": "2024-08-08T18:53:04.893952Z"
+ "iopub.execute_input": "2024-08-12T10:31:05.048880Z",
+ "iopub.status.busy": "2024-08-12T10:31:05.048557Z",
+ "iopub.status.idle": "2024-08-12T10:31:05.056263Z",
+ "shell.execute_reply": "2024-08-12T10:31:05.055716Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:04.896556Z",
- "iopub.status.busy": "2024-08-08T18:53:04.896226Z",
- "iopub.status.idle": "2024-08-08T18:53:05.009071Z",
- "shell.execute_reply": "2024-08-08T18:53:05.008556Z"
+ "iopub.execute_input": "2024-08-12T10:31:05.058466Z",
+ "iopub.status.busy": "2024-08-12T10:31:05.058117Z",
+ "iopub.status.idle": "2024-08-12T10:31:05.172389Z",
+ "shell.execute_reply": "2024-08-12T10:31:05.171830Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:05.011216Z",
- "iopub.status.busy": "2024-08-08T18:53:05.010890Z",
- "iopub.status.idle": "2024-08-08T18:53:05.013680Z",
- "shell.execute_reply": "2024-08-08T18:53:05.013228Z"
+ "iopub.execute_input": "2024-08-12T10:31:05.174639Z",
+ "iopub.status.busy": "2024-08-12T10:31:05.174253Z",
+ "iopub.status.idle": "2024-08-12T10:31:05.177235Z",
+ "shell.execute_reply": "2024-08-12T10:31:05.176793Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:05.015527Z",
- "iopub.status.busy": "2024-08-08T18:53:05.015354Z",
- "iopub.status.idle": "2024-08-08T18:53:07.128537Z",
- "shell.execute_reply": "2024-08-08T18:53:07.127880Z"
+ "iopub.execute_input": "2024-08-12T10:31:05.179303Z",
+ "iopub.status.busy": "2024-08-12T10:31:05.178961Z",
+ "iopub.status.idle": "2024-08-12T10:31:07.400050Z",
+ "shell.execute_reply": "2024-08-12T10:31:07.399213Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:07.131691Z",
- "iopub.status.busy": "2024-08-08T18:53:07.130868Z",
- "iopub.status.idle": "2024-08-08T18:53:07.142106Z",
- "shell.execute_reply": "2024-08-08T18:53:07.141539Z"
+ "iopub.execute_input": "2024-08-12T10:31:07.403380Z",
+ "iopub.status.busy": "2024-08-12T10:31:07.402738Z",
+ "iopub.status.idle": "2024-08-12T10:31:07.414900Z",
+ "shell.execute_reply": "2024-08-12T10:31:07.414301Z"
}
},
"outputs": [
@@ -786,10 +786,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:07.144209Z",
- "iopub.status.busy": "2024-08-08T18:53:07.143893Z",
- "iopub.status.idle": "2024-08-08T18:53:07.205051Z",
- "shell.execute_reply": "2024-08-08T18:53:07.204457Z"
+ "iopub.execute_input": "2024-08-12T10:31:07.417422Z",
+ "iopub.status.busy": "2024-08-12T10:31:07.417172Z",
+ "iopub.status.idle": "2024-08-12T10:31:07.521745Z",
+ "shell.execute_reply": "2024-08-12T10:31:07.521237Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index cfe4a8783..61cf69bbc 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
This dataset has 10 classes.
-Classes: {'card_payment_fee_charged', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'change_pin', 'card_about_to_expire', 'getting_spare_card', 'supported_cards_and_currencies', 'visa_or_mastercard', 'cancel_transfer'}
+Classes: {'beneficiary_not_allowed', 'visa_or_mastercard', 'getting_spare_card', 'supported_cards_and_currencies', 'change_pin', 'cancel_transfer', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'card_about_to_expire'}
Let’s print the first example in the train set.
@@ -880,43 +880,43 @@ 2. Load and format the text dataset
-
+
-
+
-
+
-
+
-
+
-
+
-
+
@@ -1219,7 +1219,7 @@ Spending too much time on data quality?Cleanlab Studio – an automated platform to find and fix issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. Try it for free!
diff --git a/master/tutorials/clean_learning/text.ipynb b/master/tutorials/clean_learning/text.ipynb
index 6911507a3..14bfb32ea 100644
--- a/master/tutorials/clean_learning/text.ipynb
+++ b/master/tutorials/clean_learning/text.ipynb
@@ -115,10 +115,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:10.467009Z",
- "iopub.status.busy": "2024-08-08T18:53:10.466580Z",
- "iopub.status.idle": "2024-08-08T18:53:13.885731Z",
- "shell.execute_reply": "2024-08-08T18:53:13.885078Z"
+ "iopub.execute_input": "2024-08-12T10:31:11.599918Z",
+ "iopub.status.busy": "2024-08-12T10:31:11.599739Z",
+ "iopub.status.idle": "2024-08-12T10:31:14.792390Z",
+ "shell.execute_reply": "2024-08-12T10:31:14.791829Z"
},
"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@ed1943228cd408bbef2343ae07f897ac0f8c96bd\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@399938be1f46b62c047276c21928e3071ce4ba6d\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": "2024-08-08T18:53:13.888448Z",
- "iopub.status.busy": "2024-08-08T18:53:13.887972Z",
- "iopub.status.idle": "2024-08-08T18:53:13.891865Z",
- "shell.execute_reply": "2024-08-08T18:53:13.891431Z"
+ "iopub.execute_input": "2024-08-12T10:31:14.794853Z",
+ "iopub.status.busy": "2024-08-12T10:31:14.794551Z",
+ "iopub.status.idle": "2024-08-12T10:31:14.797789Z",
+ "shell.execute_reply": "2024-08-12T10:31:14.797355Z"
}
},
"outputs": [],
@@ -185,10 +185,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:13.893972Z",
- "iopub.status.busy": "2024-08-08T18:53:13.893556Z",
- "iopub.status.idle": "2024-08-08T18:53:13.896736Z",
- "shell.execute_reply": "2024-08-08T18:53:13.896267Z"
+ "iopub.execute_input": "2024-08-12T10:31:14.799835Z",
+ "iopub.status.busy": "2024-08-12T10:31:14.799654Z",
+ "iopub.status.idle": "2024-08-12T10:31:14.803159Z",
+ "shell.execute_reply": "2024-08-12T10:31:14.802724Z"
},
"nbsphinx": "hidden"
},
@@ -219,10 +219,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:13.898679Z",
- "iopub.status.busy": "2024-08-08T18:53:13.898480Z",
- "iopub.status.idle": "2024-08-08T18:53:13.961823Z",
- "shell.execute_reply": "2024-08-08T18:53:13.961397Z"
+ "iopub.execute_input": "2024-08-12T10:31:14.805171Z",
+ "iopub.status.busy": "2024-08-12T10:31:14.804782Z",
+ "iopub.status.idle": "2024-08-12T10:31:15.011942Z",
+ "shell.execute_reply": "2024-08-12T10:31:15.011370Z"
}
},
"outputs": [
@@ -312,10 +312,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:13.963939Z",
- "iopub.status.busy": "2024-08-08T18:53:13.963591Z",
- "iopub.status.idle": "2024-08-08T18:53:13.967158Z",
- "shell.execute_reply": "2024-08-08T18:53:13.966607Z"
+ "iopub.execute_input": "2024-08-12T10:31:15.014170Z",
+ "iopub.status.busy": "2024-08-12T10:31:15.013748Z",
+ "iopub.status.idle": "2024-08-12T10:31:15.017471Z",
+ "shell.execute_reply": "2024-08-12T10:31:15.016937Z"
}
},
"outputs": [],
@@ -330,10 +330,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:13.969281Z",
- "iopub.status.busy": "2024-08-08T18:53:13.968818Z",
- "iopub.status.idle": "2024-08-08T18:53:13.972078Z",
- "shell.execute_reply": "2024-08-08T18:53:13.971631Z"
+ "iopub.execute_input": "2024-08-12T10:31:15.019665Z",
+ "iopub.status.busy": "2024-08-12T10:31:15.019223Z",
+ "iopub.status.idle": "2024-08-12T10:31:15.022434Z",
+ "shell.execute_reply": "2024-08-12T10:31:15.021954Z"
}
},
"outputs": [
@@ -342,7 +342,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'card_payment_fee_charged', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'change_pin', 'card_about_to_expire', 'getting_spare_card', 'supported_cards_and_currencies', 'visa_or_mastercard', 'cancel_transfer'}\n"
+ "Classes: {'beneficiary_not_allowed', 'visa_or_mastercard', 'getting_spare_card', 'supported_cards_and_currencies', 'change_pin', 'cancel_transfer', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'card_about_to_expire'}\n"
]
}
],
@@ -365,10 +365,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:13.974171Z",
- "iopub.status.busy": "2024-08-08T18:53:13.973769Z",
- "iopub.status.idle": "2024-08-08T18:53:13.976927Z",
- "shell.execute_reply": "2024-08-08T18:53:13.976392Z"
+ "iopub.execute_input": "2024-08-12T10:31:15.024295Z",
+ "iopub.status.busy": "2024-08-12T10:31:15.024124Z",
+ "iopub.status.idle": "2024-08-12T10:31:15.027383Z",
+ "shell.execute_reply": "2024-08-12T10:31:15.026923Z"
}
},
"outputs": [
@@ -409,10 +409,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:13.978883Z",
- "iopub.status.busy": "2024-08-08T18:53:13.978703Z",
- "iopub.status.idle": "2024-08-08T18:53:13.982149Z",
- "shell.execute_reply": "2024-08-08T18:53:13.981688Z"
+ "iopub.execute_input": "2024-08-12T10:31:15.029211Z",
+ "iopub.status.busy": "2024-08-12T10:31:15.029037Z",
+ "iopub.status.idle": "2024-08-12T10:31:15.032408Z",
+ "shell.execute_reply": "2024-08-12T10:31:15.031823Z"
}
},
"outputs": [],
@@ -453,17 +453,17 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:13.984246Z",
- "iopub.status.busy": "2024-08-08T18:53:13.983925Z",
- "iopub.status.idle": "2024-08-08T18:53:18.602098Z",
- "shell.execute_reply": "2024-08-08T18:53:18.601532Z"
+ "iopub.execute_input": "2024-08-12T10:31:15.034473Z",
+ "iopub.status.busy": "2024-08-12T10:31:15.034066Z",
+ "iopub.status.idle": "2024-08-12T10:31:20.022718Z",
+ "shell.execute_reply": "2024-08-12T10:31:20.022044Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "a84b83317eb1464a89d98acf732e69f3",
+ "model_id": "b77e95d91f29458c87a8a832d9354217",
"version_major": 2,
"version_minor": 0
},
@@ -477,7 +477,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b69c87c8bab34d739f9dc8d892d1bdb9",
+ "model_id": "08ba8674e30e46fc930e33c52fd19cae",
"version_major": 2,
"version_minor": 0
},
@@ -491,7 +491,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "9ad66530c90644829af3ff3b71de772e",
+ "model_id": "bcd74fc84ce94b119d8e8d4b6070122a",
"version_major": 2,
"version_minor": 0
},
@@ -505,7 +505,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "6ec8b78979d74d00bc134e4f89931257",
+ "model_id": "cf55d71b710845d8890451acc33799c0",
"version_major": 2,
"version_minor": 0
},
@@ -519,7 +519,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b0107289a3c74150b6a63b0639f52163",
+ "model_id": "4ff5b7e108a64120b255afa2e1ff6f7d",
"version_major": 2,
"version_minor": 0
},
@@ -533,7 +533,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "4b4e83c9680e4f2d86bb439951fa33a5",
+ "model_id": "f096b4fd6872467eb521cc3425e4ad77",
"version_major": 2,
"version_minor": 0
},
@@ -547,7 +547,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3b469d399c444b75afe0939d5aff9af1",
+ "model_id": "6757467eb2d347bdbfc65c8a3b0b752c",
"version_major": 2,
"version_minor": 0
},
@@ -601,10 +601,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:18.604936Z",
- "iopub.status.busy": "2024-08-08T18:53:18.604518Z",
- "iopub.status.idle": "2024-08-08T18:53:18.607571Z",
- "shell.execute_reply": "2024-08-08T18:53:18.607079Z"
+ "iopub.execute_input": "2024-08-12T10:31:20.025603Z",
+ "iopub.status.busy": "2024-08-12T10:31:20.025214Z",
+ "iopub.status.idle": "2024-08-12T10:31:20.028239Z",
+ "shell.execute_reply": "2024-08-12T10:31:20.027686Z"
}
},
"outputs": [],
@@ -626,10 +626,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:18.609598Z",
- "iopub.status.busy": "2024-08-08T18:53:18.609262Z",
- "iopub.status.idle": "2024-08-08T18:53:18.611809Z",
- "shell.execute_reply": "2024-08-08T18:53:18.611372Z"
+ "iopub.execute_input": "2024-08-12T10:31:20.030306Z",
+ "iopub.status.busy": "2024-08-12T10:31:20.029981Z",
+ "iopub.status.idle": "2024-08-12T10:31:20.033107Z",
+ "shell.execute_reply": "2024-08-12T10:31:20.032678Z"
}
},
"outputs": [],
@@ -644,10 +644,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:18.613708Z",
- "iopub.status.busy": "2024-08-08T18:53:18.613439Z",
- "iopub.status.idle": "2024-08-08T18:53:21.359090Z",
- "shell.execute_reply": "2024-08-08T18:53:21.358391Z"
+ "iopub.execute_input": "2024-08-12T10:31:20.035082Z",
+ "iopub.status.busy": "2024-08-12T10:31:20.034747Z",
+ "iopub.status.idle": "2024-08-12T10:31:22.925207Z",
+ "shell.execute_reply": "2024-08-12T10:31:22.924551Z"
},
"scrolled": true
},
@@ -670,10 +670,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:21.362432Z",
- "iopub.status.busy": "2024-08-08T18:53:21.361471Z",
- "iopub.status.idle": "2024-08-08T18:53:21.369580Z",
- "shell.execute_reply": "2024-08-08T18:53:21.369105Z"
+ "iopub.execute_input": "2024-08-12T10:31:22.928473Z",
+ "iopub.status.busy": "2024-08-12T10:31:22.927617Z",
+ "iopub.status.idle": "2024-08-12T10:31:22.935577Z",
+ "shell.execute_reply": "2024-08-12T10:31:22.935118Z"
}
},
"outputs": [
@@ -774,10 +774,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:21.371955Z",
- "iopub.status.busy": "2024-08-08T18:53:21.371614Z",
- "iopub.status.idle": "2024-08-08T18:53:21.375479Z",
- "shell.execute_reply": "2024-08-08T18:53:21.375018Z"
+ "iopub.execute_input": "2024-08-12T10:31:22.937626Z",
+ "iopub.status.busy": "2024-08-12T10:31:22.937285Z",
+ "iopub.status.idle": "2024-08-12T10:31:22.941357Z",
+ "shell.execute_reply": "2024-08-12T10:31:22.940756Z"
}
},
"outputs": [],
@@ -791,10 +791,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:21.377414Z",
- "iopub.status.busy": "2024-08-08T18:53:21.377233Z",
- "iopub.status.idle": "2024-08-08T18:53:21.380294Z",
- "shell.execute_reply": "2024-08-08T18:53:21.379747Z"
+ "iopub.execute_input": "2024-08-12T10:31:22.943707Z",
+ "iopub.status.busy": "2024-08-12T10:31:22.943304Z",
+ "iopub.status.idle": "2024-08-12T10:31:22.946672Z",
+ "shell.execute_reply": "2024-08-12T10:31:22.946082Z"
}
},
"outputs": [
@@ -829,10 +829,10 @@
"execution_count": 16,
"metadata": {
"execution": {
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diff --git a/master/tutorials/datalab/audio.ipynb b/master/tutorials/datalab/audio.ipynb
index 65c0346d8..be2c44c4c 100644
--- a/master/tutorials/datalab/audio.ipynb
+++ b/master/tutorials/datalab/audio.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
"metadata": {
"execution": {
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- "iopub.status.busy": "2024-08-08T18:53:25.242777Z",
- "iopub.status.idle": "2024-08-08T18:53:30.964482Z",
- "shell.execute_reply": "2024-08-08T18:53:30.963973Z"
+ "iopub.execute_input": "2024-08-12T10:31:27.559918Z",
+ "iopub.status.busy": "2024-08-12T10:31:27.559732Z",
+ "iopub.status.idle": "2024-08-12T10:31:33.493498Z",
+ "shell.execute_reply": "2024-08-12T10:31:33.492957Z"
},
"nbsphinx": "hidden"
},
@@ -97,7 +97,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@ed1943228cd408bbef2343ae07f897ac0f8c96bd\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@399938be1f46b62c047276c21928e3071ce4ba6d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -131,10 +131,10 @@
"execution_count": 2,
"metadata": {
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+ "shell.execute_reply": "2024-08-12T10:31:33.498441Z"
},
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},
@@ -157,10 +157,10 @@
"execution_count": 3,
"metadata": {
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+ "shell.execute_reply": "2024-08-12T10:31:33.505157Z"
},
"nbsphinx": "hidden"
},
@@ -208,10 +208,10 @@
"base_uri": "https://localhost:8080/"
},
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- "iopub.execute_input": "2024-08-08T18:53:30.978729Z",
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- "shell.execute_reply": "2024-08-08T18:53:32.730757Z"
+ "iopub.execute_input": "2024-08-12T10:31:33.507649Z",
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+ "shell.execute_reply": "2024-08-12T10:31:35.406669Z"
},
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"outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6"
@@ -242,10 +242,10 @@
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- "shell.execute_reply": "2024-08-08T18:53:32.744840Z"
+ "iopub.execute_input": "2024-08-12T10:31:35.410188Z",
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},
"id": "FDA5sGZwUSur",
"outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895"
@@ -329,10 +329,10 @@
"execution_count": 6,
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@@ -380,10 +380,10 @@
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},
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"outputId": "c6a4917f-4a82-4a89-9193-415072e45550"
@@ -435,10 +435,10 @@
"execution_count": 8,
"metadata": {
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- "iopub.execute_input": "2024-08-08T18:53:33.204900Z",
- "iopub.status.busy": "2024-08-08T18:53:33.204458Z",
- "iopub.status.idle": "2024-08-08T18:53:34.314129Z",
- "shell.execute_reply": "2024-08-08T18:53:34.313503Z"
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@@ -474,10 +474,10 @@
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+ "iopub.execute_input": "2024-08-12T10:31:39.363821Z",
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+ "shell.execute_reply": "2024-08-12T10:31:39.381166Z"
},
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"outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4"
@@ -557,10 +557,10 @@
"execution_count": 10,
"metadata": {
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- "shell.execute_reply": "2024-08-08T18:53:34.339110Z"
+ "iopub.execute_input": "2024-08-12T10:31:39.383708Z",
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+ "shell.execute_reply": "2024-08-12T10:31:39.386056Z"
},
"id": "I8JqhOZgi94g"
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@@ -582,10 +582,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:53:34.341609Z",
- "iopub.status.busy": "2024-08-08T18:53:34.341314Z",
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+ "iopub.execute_input": "2024-08-12T10:31:39.388474Z",
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+ "shell.execute_reply": "2024-08-12T10:31:53.911735Z"
},
"id": "2FSQ2GR9R_YA"
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@@ -617,10 +617,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
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- "shell.execute_reply": "2024-08-08T18:53:48.575422Z"
+ "iopub.execute_input": "2024-08-12T10:31:53.914987Z",
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"outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632"
@@ -680,10 +680,10 @@
"execution_count": 13,
"metadata": {
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- "iopub.status.busy": "2024-08-08T18:53:48.577718Z",
- "iopub.status.idle": "2024-08-08T18:53:49.277921Z",
- "shell.execute_reply": "2024-08-08T18:53:49.277311Z"
+ "iopub.execute_input": "2024-08-12T10:31:53.920671Z",
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@@ -717,10 +717,10 @@
"base_uri": "https://localhost:8080/"
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- "iopub.execute_input": "2024-08-08T18:53:49.280920Z",
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+ "iopub.execute_input": "2024-08-12T10:31:54.653500Z",
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"id": "_b-AQeoXOc7q",
"outputId": "15ae534a-f517-4906-b177-ca91931a8954"
@@ -767,10 +767,10 @@
"execution_count": 15,
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}
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@@ -807,10 +807,10 @@
"execution_count": 16,
"metadata": {
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@@ -870,10 +870,10 @@
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@@ -977,10 +977,10 @@
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@@ -1018,10 +1018,10 @@
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@@ -1148,10 +1148,10 @@
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@@ -1205,10 +1205,10 @@
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@@ -1253,10 +1253,10 @@
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@@ -1297,10 +1297,10 @@
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diff --git a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb
index 323ce22c5..3b981fe19 100644
--- a/master/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/tutorials/datalab/datalab_advanced.ipynb
@@ -80,10 +80,10 @@
"execution_count": 1,
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-08-08T18:53:55.081486Z",
- "shell.execute_reply": "2024-08-08T18:53:55.080922Z"
+ "iopub.execute_input": "2024-08-12T10:31:59.748865Z",
+ "iopub.status.busy": "2024-08-12T10:31:59.748689Z",
+ "iopub.status.idle": "2024-08-12T10:32:01.178381Z",
+ "shell.execute_reply": "2024-08-12T10:32:01.177678Z"
},
"nbsphinx": "hidden"
},
@@ -93,7 +93,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@ed1943228cd408bbef2343ae07f897ac0f8c96bd\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@399938be1f46b62c047276c21928e3071ce4ba6d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -118,10 +118,10 @@
"execution_count": 2,
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+ "shell.execute_reply": "2024-08-12T10:32:01.183554Z"
}
},
"outputs": [],
@@ -252,10 +252,10 @@
"execution_count": 3,
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- "shell.execute_reply": "2024-08-08T18:53:55.096595Z"
+ "iopub.execute_input": "2024-08-12T10:32:01.186467Z",
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+ "shell.execute_reply": "2024-08-12T10:32:01.194325Z"
},
"nbsphinx": "hidden"
},
@@ -353,10 +353,10 @@
"execution_count": 4,
"metadata": {
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- "iopub.execute_input": "2024-08-08T18:53:55.099353Z",
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- "iopub.status.idle": "2024-08-08T18:53:55.103585Z",
- "shell.execute_reply": "2024-08-08T18:53:55.103129Z"
+ "iopub.execute_input": "2024-08-12T10:32:01.196739Z",
+ "iopub.status.busy": "2024-08-12T10:32:01.196579Z",
+ "iopub.status.idle": "2024-08-12T10:32:01.201598Z",
+ "shell.execute_reply": "2024-08-12T10:32:01.201169Z"
}
},
"outputs": [],
@@ -445,10 +445,10 @@
"execution_count": 5,
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- "shell.execute_reply": "2024-08-08T18:53:55.112684Z"
+ "iopub.execute_input": "2024-08-12T10:32:01.203716Z",
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+ "shell.execute_reply": "2024-08-12T10:32:01.210770Z"
},
"nbsphinx": "hidden"
},
@@ -517,10 +517,10 @@
"execution_count": 6,
"metadata": {
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- "iopub.execute_input": "2024-08-08T18:53:55.115230Z",
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- "shell.execute_reply": "2024-08-08T18:53:55.433771Z"
+ "iopub.execute_input": "2024-08-12T10:32:01.213218Z",
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+ "shell.execute_reply": "2024-08-12T10:32:01.589735Z"
}
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"outputs": [
@@ -569,10 +569,10 @@
"execution_count": 7,
"metadata": {
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+ "iopub.execute_input": "2024-08-12T10:32:01.592629Z",
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@@ -608,10 +608,10 @@
"execution_count": 8,
"metadata": {
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- "iopub.status.idle": "2024-08-08T18:53:55.543506Z",
- "shell.execute_reply": "2024-08-08T18:53:55.543045Z"
+ "iopub.execute_input": "2024-08-12T10:32:01.617888Z",
+ "iopub.status.busy": "2024-08-12T10:32:01.617542Z",
+ "iopub.status.idle": "2024-08-12T10:32:01.706974Z",
+ "shell.execute_reply": "2024-08-12T10:32:01.706312Z"
}
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"outputs": [],
@@ -642,10 +642,10 @@
"execution_count": 9,
"metadata": {
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- "iopub.execute_input": "2024-08-08T18:53:55.545976Z",
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- "iopub.status.idle": "2024-08-08T18:53:57.517193Z",
- "shell.execute_reply": "2024-08-08T18:53:57.516574Z"
+ "iopub.execute_input": "2024-08-12T10:32:01.709728Z",
+ "iopub.status.busy": "2024-08-12T10:32:01.709322Z",
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@@ -714,10 +714,10 @@
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- "iopub.status.idle": "2024-08-08T18:53:57.541098Z",
- "shell.execute_reply": "2024-08-08T18:53:57.540654Z"
+ "iopub.execute_input": "2024-08-12T10:32:03.793485Z",
+ "iopub.status.busy": "2024-08-12T10:32:03.793002Z",
+ "iopub.status.idle": "2024-08-12T10:32:03.815741Z",
+ "shell.execute_reply": "2024-08-12T10:32:03.815264Z"
}
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"outputs": [
@@ -830,10 +830,10 @@
"execution_count": 11,
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- "iopub.execute_input": "2024-08-08T18:53:57.543256Z",
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- "iopub.status.idle": "2024-08-08T18:53:57.560318Z",
- "shell.execute_reply": "2024-08-08T18:53:57.559875Z"
+ "iopub.execute_input": "2024-08-12T10:32:03.817911Z",
+ "iopub.status.busy": "2024-08-12T10:32:03.817557Z",
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@@ -937,10 +937,10 @@
"execution_count": 12,
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- "shell.execute_reply": "2024-08-08T18:53:57.574916Z"
+ "iopub.execute_input": "2024-08-12T10:32:03.837879Z",
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@@ -1075,17 +1075,17 @@
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@@ -1554,15 +1498,15 @@
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- "layout": "IPY_MODEL_f0fa1263d8404f888e39f5c9ed9134ea",
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- "value": " 132/132 [00:00<00:00, 11354.09 examples/s]"
+ "value": " 132/132 [00:00<00:00, 12983.33 examples/s]"
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@@ -1578,17 +1522,17 @@
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@@ -1641,7 +1585,7 @@
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@@ -1694,25 +1638,7 @@
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@@ -1765,7 +1691,30 @@
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diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb
index 0b3e3aaf0..5a8714bfc 100644
--- a/master/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
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- "shell.execute_reply": "2024-08-08T18:54:01.882096Z"
+ "iopub.execute_input": "2024-08-12T10:32:06.983365Z",
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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@ed1943228cd408bbef2343ae07f897ac0f8c96bd\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@399938be1f46b62c047276c21928e3071ce4ba6d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -116,10 +116,10 @@
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@@ -250,10 +250,10 @@
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@@ -356,10 +356,10 @@
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- "shell.execute_reply": "2024-08-08T18:54:01.905235Z"
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@@ -448,10 +448,10 @@
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@@ -520,10 +520,10 @@
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@@ -559,10 +559,10 @@
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@@ -602,10 +602,10 @@
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@@ -638,10 +638,10 @@
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@@ -685,10 +685,10 @@
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"execution": {
- "iopub.execute_input": "2024-08-08T18:54:04.480787Z",
- "iopub.status.busy": "2024-08-08T18:54:04.480397Z",
- "iopub.status.idle": "2024-08-08T18:54:04.501436Z",
- "shell.execute_reply": "2024-08-08T18:54:04.500864Z"
+ "iopub.execute_input": "2024-08-12T10:32:11.098718Z",
+ "iopub.status.busy": "2024-08-12T10:32:11.098130Z",
+ "iopub.status.idle": "2024-08-12T10:32:11.117905Z",
+ "shell.execute_reply": "2024-08-12T10:32:11.117324Z"
}
},
"outputs": [
@@ -821,10 +821,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:54:04.503661Z",
- "iopub.status.busy": "2024-08-08T18:54:04.503340Z",
- "iopub.status.idle": "2024-08-08T18:54:04.510666Z",
- "shell.execute_reply": "2024-08-08T18:54:04.510198Z"
+ "iopub.execute_input": "2024-08-12T10:32:11.120377Z",
+ "iopub.status.busy": "2024-08-12T10:32:11.119904Z",
+ "iopub.status.idle": "2024-08-12T10:32:11.127046Z",
+ "shell.execute_reply": "2024-08-12T10:32:11.126456Z"
}
},
"outputs": [
@@ -935,10 +935,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:54:04.512703Z",
- "iopub.status.busy": "2024-08-08T18:54:04.512524Z",
- "iopub.status.idle": "2024-08-08T18:54:04.518581Z",
- "shell.execute_reply": "2024-08-08T18:54:04.518110Z"
+ "iopub.execute_input": "2024-08-12T10:32:11.129084Z",
+ "iopub.status.busy": "2024-08-12T10:32:11.128900Z",
+ "iopub.status.idle": "2024-08-12T10:32:11.136478Z",
+ "shell.execute_reply": "2024-08-12T10:32:11.135930Z"
}
},
"outputs": [
@@ -1005,10 +1005,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:54:04.520503Z",
- "iopub.status.busy": "2024-08-08T18:54:04.520326Z",
- "iopub.status.idle": "2024-08-08T18:54:04.532165Z",
- "shell.execute_reply": "2024-08-08T18:54:04.531600Z"
+ "iopub.execute_input": "2024-08-12T10:32:11.138541Z",
+ "iopub.status.busy": "2024-08-12T10:32:11.138204Z",
+ "iopub.status.idle": "2024-08-12T10:32:11.148788Z",
+ "shell.execute_reply": "2024-08-12T10:32:11.148225Z"
}
},
"outputs": [
@@ -1200,10 +1200,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:54:04.534253Z",
- "iopub.status.busy": "2024-08-08T18:54:04.533963Z",
- "iopub.status.idle": "2024-08-08T18:54:04.543383Z",
- "shell.execute_reply": "2024-08-08T18:54:04.542816Z"
+ "iopub.execute_input": "2024-08-12T10:32:11.150984Z",
+ "iopub.status.busy": "2024-08-12T10:32:11.150654Z",
+ "iopub.status.idle": "2024-08-12T10:32:11.160607Z",
+ "shell.execute_reply": "2024-08-12T10:32:11.160022Z"
}
},
"outputs": [
@@ -1319,10 +1319,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:54:04.545549Z",
- "iopub.status.busy": "2024-08-08T18:54:04.545231Z",
- "iopub.status.idle": "2024-08-08T18:54:04.552108Z",
- "shell.execute_reply": "2024-08-08T18:54:04.551568Z"
+ "iopub.execute_input": "2024-08-12T10:32:11.163177Z",
+ "iopub.status.busy": "2024-08-12T10:32:11.162777Z",
+ "iopub.status.idle": "2024-08-12T10:32:11.170325Z",
+ "shell.execute_reply": "2024-08-12T10:32:11.169691Z"
},
"scrolled": true
},
@@ -1447,10 +1447,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:54:04.554444Z",
- "iopub.status.busy": "2024-08-08T18:54:04.554013Z",
- "iopub.status.idle": "2024-08-08T18:54:04.563589Z",
- "shell.execute_reply": "2024-08-08T18:54:04.563132Z"
+ "iopub.execute_input": "2024-08-12T10:32:11.172574Z",
+ "iopub.status.busy": "2024-08-12T10:32:11.172225Z",
+ "iopub.status.idle": "2024-08-12T10:32:11.182760Z",
+ "shell.execute_reply": "2024-08-12T10:32:11.182213Z"
}
},
"outputs": [
@@ -1553,10 +1553,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-08T18:54:04.565603Z",
- "iopub.status.busy": "2024-08-08T18:54:04.565425Z",
- "iopub.status.idle": "2024-08-08T18:54:04.581683Z",
- "shell.execute_reply": "2024-08-08T18:54:04.581197Z"
+ "iopub.execute_input": "2024-08-12T10:32:11.184974Z",
+ "iopub.status.busy": "2024-08-12T10:32:11.184656Z",
+ "iopub.status.idle": "2024-08-12T10:32:11.201470Z",
+ "shell.execute_reply": "2024-08-12T10:32:11.200965Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html
index 7c49d81e7..ab8e80b79 100644
--- a/master/tutorials/datalab/image.html
+++ b/master/tutorials/datalab/image.html
@@ -727,31 +727,31 @@ 2. Fetch and normalize the Fashion-MNIST dataset
-
+
-
+
-
+
-
+
-
+
Convert the transformed dataset to a torch dataset. Torch datasets are more efficient with dataloading in practice.
@@ -1055,8 +1055,8 @@ 5. Compute out-of-sample predicted probabilities and feature embeddings
Training on fold: 1 ...
-epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.909
-epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.600
+epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.112
+epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.743
Computing feature embeddings ...
@@ -1064,7 +1064,7 @@ 5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1087,8 +1087,8 @@ 5. Compute out-of-sample predicted probabilities and feature embeddings
Training on fold: 2 ...
-epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.885
-epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.612
+epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.025
+epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.947
Computing feature embeddings ...
@@ -1096,7 +1096,7 @@ 5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1119,8 +1119,8 @@ 5. Compute out-of-sample predicted probabilities and feature embeddings
Training on fold: 3 ...
-epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.765
-epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.617
+epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.366
+epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.925
Computing feature embeddings ...
@@ -1128,7 +1128,7 @@ 5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
This dataset has 10 classes.
-Classes: {'card_payment_fee_charged', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'cancel_transfer', 'card_about_to_expire', 'getting_spare_card', 'change_pin', 'supported_cards_and_currencies', 'visa_or_mastercard'}
+Classes: {'beneficiary_not_allowed', 'card_payment_fee_charged', 'cancel_transfer', 'visa_or_mastercard', 'change_pin', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'getting_spare_card', 'card_about_to_expire', 'apple_pay_or_google_pay'}
Let’s view the i-th example in the dataset:
diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 666762212..cb47d3b31 100644 --- a/master/tutorials/datalab/text.ipynb +++ b/master/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:23.637597Z", - "iopub.status.busy": "2024-08-08T18:57:23.637174Z", - "iopub.status.idle": "2024-08-08T18:57:26.772389Z", - "shell.execute_reply": "2024-08-08T18:57:26.771759Z" + "iopub.execute_input": "2024-08-12T10:35:37.558577Z", + "iopub.status.busy": "2024-08-12T10:35:37.558339Z", + "iopub.status.idle": "2024-08-12T10:35:40.739470Z", + "shell.execute_reply": "2024-08-12T10:35:40.738831Z" }, "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@ed1943228cd408bbef2343ae07f897ac0f8c96bd\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@399938be1f46b62c047276c21928e3071ce4ba6d\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": "2024-08-08T18:57:26.775206Z", - "iopub.status.busy": "2024-08-08T18:57:26.774784Z", - "iopub.status.idle": "2024-08-08T18:57:26.778003Z", - "shell.execute_reply": "2024-08-08T18:57:26.777555Z" + "iopub.execute_input": "2024-08-12T10:35:40.742086Z", + "iopub.status.busy": "2024-08-12T10:35:40.741793Z", + "iopub.status.idle": "2024-08-12T10:35:40.745137Z", + "shell.execute_reply": "2024-08-12T10:35:40.744684Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:26.780141Z", - "iopub.status.busy": "2024-08-08T18:57:26.779785Z", - "iopub.status.idle": "2024-08-08T18:57:26.782943Z", - "shell.execute_reply": "2024-08-08T18:57:26.782441Z" + "iopub.execute_input": "2024-08-12T10:35:40.747148Z", + "iopub.status.busy": "2024-08-12T10:35:40.746882Z", + "iopub.status.idle": "2024-08-12T10:35:40.749760Z", + "shell.execute_reply": "2024-08-12T10:35:40.749330Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:26.784993Z", - "iopub.status.busy": "2024-08-08T18:57:26.784660Z", - "iopub.status.idle": "2024-08-08T18:57:26.810720Z", - "shell.execute_reply": "2024-08-08T18:57:26.810171Z" + "iopub.execute_input": "2024-08-12T10:35:40.751845Z", + "iopub.status.busy": "2024-08-12T10:35:40.751461Z", + "iopub.status.idle": "2024-08-12T10:35:40.775689Z", + "shell.execute_reply": "2024-08-12T10:35:40.775149Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:26.812844Z", - "iopub.status.busy": "2024-08-08T18:57:26.812508Z", - "iopub.status.idle": "2024-08-08T18:57:26.816017Z", - "shell.execute_reply": "2024-08-08T18:57:26.815460Z" + "iopub.execute_input": "2024-08-12T10:35:40.777762Z", + "iopub.status.busy": "2024-08-12T10:35:40.777401Z", + "iopub.status.idle": "2024-08-12T10:35:40.780895Z", + "shell.execute_reply": "2024-08-12T10:35:40.780349Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_payment_fee_charged', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'cancel_transfer', 'card_about_to_expire', 'getting_spare_card', 'change_pin', 'supported_cards_and_currencies', 'visa_or_mastercard'}\n" + "Classes: {'beneficiary_not_allowed', 'card_payment_fee_charged', 'cancel_transfer', 'visa_or_mastercard', 'change_pin', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'getting_spare_card', 'card_about_to_expire', 'apple_pay_or_google_pay'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:26.818127Z", - "iopub.status.busy": "2024-08-08T18:57:26.817789Z", - "iopub.status.idle": "2024-08-08T18:57:26.820784Z", - "shell.execute_reply": "2024-08-08T18:57:26.820238Z" + "iopub.execute_input": "2024-08-12T10:35:40.782945Z", + "iopub.status.busy": "2024-08-12T10:35:40.782612Z", + "iopub.status.idle": "2024-08-12T10:35:40.785832Z", + "shell.execute_reply": "2024-08-12T10:35:40.785363Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:26.823035Z", - "iopub.status.busy": "2024-08-08T18:57:26.822565Z", - "iopub.status.idle": "2024-08-08T18:57:30.521343Z", - "shell.execute_reply": "2024-08-08T18:57:30.520787Z" + "iopub.execute_input": "2024-08-12T10:35:40.787889Z", + "iopub.status.busy": "2024-08-12T10:35:40.787554Z", + "iopub.status.idle": "2024-08-12T10:35:44.773402Z", + "shell.execute_reply": "2024-08-12T10:35:44.772834Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:30.524114Z", - "iopub.status.busy": "2024-08-08T18:57:30.523692Z", - "iopub.status.idle": "2024-08-08T18:57:31.422700Z", - "shell.execute_reply": "2024-08-08T18:57:31.422089Z" + "iopub.execute_input": "2024-08-12T10:35:44.776274Z", + "iopub.status.busy": "2024-08-12T10:35:44.775914Z", + "iopub.status.idle": "2024-08-12T10:35:45.665358Z", + "shell.execute_reply": "2024-08-12T10:35:45.664759Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:31.425649Z", - "iopub.status.busy": "2024-08-08T18:57:31.425265Z", - "iopub.status.idle": "2024-08-08T18:57:31.428198Z", - "shell.execute_reply": "2024-08-08T18:57:31.427696Z" + "iopub.execute_input": "2024-08-12T10:35:45.668546Z", + "iopub.status.busy": "2024-08-12T10:35:45.668154Z", + "iopub.status.idle": "2024-08-12T10:35:45.671068Z", + "shell.execute_reply": "2024-08-12T10:35:45.670578Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:31.430626Z", - "iopub.status.busy": "2024-08-08T18:57:31.430243Z", - "iopub.status.idle": "2024-08-08T18:57:33.428306Z", - "shell.execute_reply": "2024-08-08T18:57:33.427582Z" + "iopub.execute_input": "2024-08-12T10:35:45.673470Z", + "iopub.status.busy": "2024-08-12T10:35:45.673092Z", + "iopub.status.idle": "2024-08-12T10:35:47.702436Z", + "shell.execute_reply": "2024-08-12T10:35:47.701696Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:33.431267Z", - "iopub.status.busy": "2024-08-08T18:57:33.430823Z", - "iopub.status.idle": "2024-08-08T18:57:33.454383Z", - "shell.execute_reply": "2024-08-08T18:57:33.453871Z" + "iopub.execute_input": "2024-08-12T10:35:47.705440Z", + "iopub.status.busy": "2024-08-12T10:35:47.704975Z", + "iopub.status.idle": "2024-08-12T10:35:47.729437Z", + "shell.execute_reply": "2024-08-12T10:35:47.728904Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:33.456573Z", - "iopub.status.busy": "2024-08-08T18:57:33.456110Z", - "iopub.status.idle": "2024-08-08T18:57:33.464348Z", - "shell.execute_reply": "2024-08-08T18:57:33.463794Z" + "iopub.execute_input": "2024-08-12T10:35:47.732155Z", + "iopub.status.busy": "2024-08-12T10:35:47.731793Z", + "iopub.status.idle": "2024-08-12T10:35:47.741250Z", + "shell.execute_reply": "2024-08-12T10:35:47.740687Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:33.466360Z", - "iopub.status.busy": "2024-08-08T18:57:33.466177Z", - "iopub.status.idle": "2024-08-08T18:57:33.470526Z", - "shell.execute_reply": "2024-08-08T18:57:33.470050Z" + "iopub.execute_input": "2024-08-12T10:35:47.743419Z", + "iopub.status.busy": "2024-08-12T10:35:47.743136Z", + "iopub.status.idle": "2024-08-12T10:35:47.747544Z", + "shell.execute_reply": "2024-08-12T10:35:47.747077Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:33.472647Z", - "iopub.status.busy": "2024-08-08T18:57:33.472321Z", - "iopub.status.idle": "2024-08-08T18:57:33.478952Z", - "shell.execute_reply": "2024-08-08T18:57:33.478375Z" + "iopub.execute_input": "2024-08-12T10:35:47.749487Z", + "iopub.status.busy": "2024-08-12T10:35:47.749326Z", + "iopub.status.idle": "2024-08-12T10:35:47.755601Z", + "shell.execute_reply": "2024-08-12T10:35:47.755155Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:33.481016Z", - "iopub.status.busy": "2024-08-08T18:57:33.480700Z", - "iopub.status.idle": "2024-08-08T18:57:33.487544Z", - "shell.execute_reply": "2024-08-08T18:57:33.487081Z" + "iopub.execute_input": "2024-08-12T10:35:47.757475Z", + "iopub.status.busy": "2024-08-12T10:35:47.757320Z", + "iopub.status.idle": "2024-08-12T10:35:47.763213Z", + "shell.execute_reply": "2024-08-12T10:35:47.762764Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:33.489500Z", - "iopub.status.busy": "2024-08-08T18:57:33.489189Z", - "iopub.status.idle": "2024-08-08T18:57:33.495048Z", - "shell.execute_reply": "2024-08-08T18:57:33.494498Z" + "iopub.execute_input": "2024-08-12T10:35:47.765090Z", + "iopub.status.busy": "2024-08-12T10:35:47.764937Z", + "iopub.status.idle": "2024-08-12T10:35:47.770514Z", + "shell.execute_reply": "2024-08-12T10:35:47.770034Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:33.497134Z", - "iopub.status.busy": "2024-08-08T18:57:33.496821Z", - "iopub.status.idle": "2024-08-08T18:57:33.506094Z", - "shell.execute_reply": "2024-08-08T18:57:33.505538Z" + "iopub.execute_input": "2024-08-12T10:35:47.772510Z", + "iopub.status.busy": "2024-08-12T10:35:47.772173Z", + "iopub.status.idle": "2024-08-12T10:35:47.780532Z", + "shell.execute_reply": "2024-08-12T10:35:47.780093Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:33.508300Z", - "iopub.status.busy": "2024-08-08T18:57:33.507977Z", - "iopub.status.idle": "2024-08-08T18:57:33.513316Z", - "shell.execute_reply": "2024-08-08T18:57:33.512767Z" + "iopub.execute_input": "2024-08-12T10:35:47.782646Z", + "iopub.status.busy": "2024-08-12T10:35:47.782223Z", + "iopub.status.idle": "2024-08-12T10:35:47.787705Z", + "shell.execute_reply": "2024-08-12T10:35:47.787156Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:33.515460Z", - "iopub.status.busy": "2024-08-08T18:57:33.515141Z", - "iopub.status.idle": "2024-08-08T18:57:33.520436Z", - "shell.execute_reply": "2024-08-08T18:57:33.519893Z" + "iopub.execute_input": "2024-08-12T10:35:47.789807Z", + "iopub.status.busy": "2024-08-12T10:35:47.789489Z", + "iopub.status.idle": "2024-08-12T10:35:47.794831Z", + "shell.execute_reply": "2024-08-12T10:35:47.794259Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:33.522522Z", - "iopub.status.busy": "2024-08-08T18:57:33.522213Z", - "iopub.status.idle": "2024-08-08T18:57:33.525898Z", - "shell.execute_reply": "2024-08-08T18:57:33.525345Z" + "iopub.execute_input": "2024-08-12T10:35:47.797007Z", + "iopub.status.busy": "2024-08-12T10:35:47.796666Z", + "iopub.status.idle": "2024-08-12T10:35:47.800304Z", + "shell.execute_reply": "2024-08-12T10:35:47.799739Z" } }, "outputs": [ @@ -1449,10 +1449,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-08-08T18:57:33.528163Z", - "iopub.status.busy": "2024-08-08T18:57:33.527843Z", - "iopub.status.idle": "2024-08-08T18:57:33.533093Z", - "shell.execute_reply": "2024-08-08T18:57:33.532535Z" + "iopub.execute_input": "2024-08-12T10:35:47.802486Z", + "iopub.status.busy": "2024-08-12T10:35:47.802149Z", + "iopub.status.idle": "2024-08-12T10:35:47.807293Z", + "shell.execute_reply": "2024-08-12T10:35:47.806733Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html index 9e2af5fcc..2a6bcd14f 100644 --- a/master/tutorials/datalab/workflows.html +++ b/master/tutorials/datalab/workflows.html @@ -833,7 +833,7 @@