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diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle
index e38dd7105..60aea2abe 100644
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diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree
index d64a0dfbf..177d4b2e1 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 35f0d970f..835b9297f 100644
--- a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:00.381660Z",
- "iopub.status.busy": "2024-06-28T15:32:00.381248Z",
- "iopub.status.idle": "2024-06-28T15:32:01.681791Z",
- "shell.execute_reply": "2024-06-28T15:32:01.681244Z"
+ "iopub.execute_input": "2024-07-01T15:01:38.704463Z",
+ "iopub.status.busy": "2024-07-01T15:01:38.704282Z",
+ "iopub.status.idle": "2024-07-01T15:01:39.968773Z",
+ "shell.execute_reply": "2024-07-01T15:01:39.968140Z"
},
"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@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:01.684513Z",
- "iopub.status.busy": "2024-06-28T15:32:01.684044Z",
- "iopub.status.idle": "2024-06-28T15:32:01.703220Z",
- "shell.execute_reply": "2024-06-28T15:32:01.702743Z"
+ "iopub.execute_input": "2024-07-01T15:01:39.971457Z",
+ "iopub.status.busy": "2024-07-01T15:01:39.971069Z",
+ "iopub.status.idle": "2024-07-01T15:01:39.990015Z",
+ "shell.execute_reply": "2024-07-01T15:01:39.989387Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:01.705803Z",
- "iopub.status.busy": "2024-06-28T15:32:01.705424Z",
- "iopub.status.idle": "2024-06-28T15:32:01.873655Z",
- "shell.execute_reply": "2024-06-28T15:32:01.873076Z"
+ "iopub.execute_input": "2024-07-01T15:01:39.992806Z",
+ "iopub.status.busy": "2024-07-01T15:01:39.992402Z",
+ "iopub.status.idle": "2024-07-01T15:01:40.303536Z",
+ "shell.execute_reply": "2024-07-01T15:01:40.302965Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:01.905438Z",
- "iopub.status.busy": "2024-06-28T15:32:01.905010Z",
- "iopub.status.idle": "2024-06-28T15:32:01.908827Z",
- "shell.execute_reply": "2024-06-28T15:32:01.908342Z"
+ "iopub.execute_input": "2024-07-01T15:01:40.336204Z",
+ "iopub.status.busy": "2024-07-01T15:01:40.335666Z",
+ "iopub.status.idle": "2024-07-01T15:01:40.340138Z",
+ "shell.execute_reply": "2024-07-01T15:01:40.339623Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:01.910976Z",
- "iopub.status.busy": "2024-06-28T15:32:01.910625Z",
- "iopub.status.idle": "2024-06-28T15:32:01.919240Z",
- "shell.execute_reply": "2024-06-28T15:32:01.918799Z"
+ "iopub.execute_input": "2024-07-01T15:01:40.342354Z",
+ "iopub.status.busy": "2024-07-01T15:01:40.342145Z",
+ "iopub.status.idle": "2024-07-01T15:01:40.351148Z",
+ "shell.execute_reply": "2024-07-01T15:01:40.350569Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:01.921349Z",
- "iopub.status.busy": "2024-06-28T15:32:01.921161Z",
- "iopub.status.idle": "2024-06-28T15:32:01.923695Z",
- "shell.execute_reply": "2024-06-28T15:32:01.923253Z"
+ "iopub.execute_input": "2024-07-01T15:01:40.353562Z",
+ "iopub.status.busy": "2024-07-01T15:01:40.353231Z",
+ "iopub.status.idle": "2024-07-01T15:01:40.356046Z",
+ "shell.execute_reply": "2024-07-01T15:01:40.355491Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:01.925557Z",
- "iopub.status.busy": "2024-06-28T15:32:01.925387Z",
- "iopub.status.idle": "2024-06-28T15:32:02.457912Z",
- "shell.execute_reply": "2024-06-28T15:32:02.457433Z"
+ "iopub.execute_input": "2024-07-01T15:01:40.358053Z",
+ "iopub.status.busy": "2024-07-01T15:01:40.357874Z",
+ "iopub.status.idle": "2024-07-01T15:01:40.885000Z",
+ "shell.execute_reply": "2024-07-01T15:01:40.884377Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:02.460329Z",
- "iopub.status.busy": "2024-06-28T15:32:02.460138Z",
- "iopub.status.idle": "2024-06-28T15:32:04.490214Z",
- "shell.execute_reply": "2024-06-28T15:32:04.489567Z"
+ "iopub.execute_input": "2024-07-01T15:01:40.887806Z",
+ "iopub.status.busy": "2024-07-01T15:01:40.887346Z",
+ "iopub.status.idle": "2024-07-01T15:01:42.858439Z",
+ "shell.execute_reply": "2024-07-01T15:01:42.857751Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:04.492856Z",
- "iopub.status.busy": "2024-06-28T15:32:04.492241Z",
- "iopub.status.idle": "2024-06-28T15:32:04.502594Z",
- "shell.execute_reply": "2024-06-28T15:32:04.502078Z"
+ "iopub.execute_input": "2024-07-01T15:01:42.861505Z",
+ "iopub.status.busy": "2024-07-01T15:01:42.860685Z",
+ "iopub.status.idle": "2024-07-01T15:01:42.872129Z",
+ "shell.execute_reply": "2024-07-01T15:01:42.871534Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:04.504766Z",
- "iopub.status.busy": "2024-06-28T15:32:04.504436Z",
- "iopub.status.idle": "2024-06-28T15:32:04.508609Z",
- "shell.execute_reply": "2024-06-28T15:32:04.508063Z"
+ "iopub.execute_input": "2024-07-01T15:01:42.874722Z",
+ "iopub.status.busy": "2024-07-01T15:01:42.874312Z",
+ "iopub.status.idle": "2024-07-01T15:01:42.879185Z",
+ "shell.execute_reply": "2024-07-01T15:01:42.878651Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:04.510703Z",
- "iopub.status.busy": "2024-06-28T15:32:04.510397Z",
- "iopub.status.idle": "2024-06-28T15:32:04.517582Z",
- "shell.execute_reply": "2024-06-28T15:32:04.517118Z"
+ "iopub.execute_input": "2024-07-01T15:01:42.881719Z",
+ "iopub.status.busy": "2024-07-01T15:01:42.881293Z",
+ "iopub.status.idle": "2024-07-01T15:01:42.890936Z",
+ "shell.execute_reply": "2024-07-01T15:01:42.890441Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:04.519554Z",
- "iopub.status.busy": "2024-06-28T15:32:04.519252Z",
- "iopub.status.idle": "2024-06-28T15:32:04.632352Z",
- "shell.execute_reply": "2024-06-28T15:32:04.631747Z"
+ "iopub.execute_input": "2024-07-01T15:01:42.893152Z",
+ "iopub.status.busy": "2024-07-01T15:01:42.892940Z",
+ "iopub.status.idle": "2024-07-01T15:01:43.010191Z",
+ "shell.execute_reply": "2024-07-01T15:01:43.009566Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:04.634587Z",
- "iopub.status.busy": "2024-06-28T15:32:04.634256Z",
- "iopub.status.idle": "2024-06-28T15:32:04.637211Z",
- "shell.execute_reply": "2024-06-28T15:32:04.636666Z"
+ "iopub.execute_input": "2024-07-01T15:01:43.012877Z",
+ "iopub.status.busy": "2024-07-01T15:01:43.012678Z",
+ "iopub.status.idle": "2024-07-01T15:01:43.015881Z",
+ "shell.execute_reply": "2024-07-01T15:01:43.015414Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:04.639474Z",
- "iopub.status.busy": "2024-06-28T15:32:04.638904Z",
- "iopub.status.idle": "2024-06-28T15:32:06.709224Z",
- "shell.execute_reply": "2024-06-28T15:32:06.708438Z"
+ "iopub.execute_input": "2024-07-01T15:01:43.017749Z",
+ "iopub.status.busy": "2024-07-01T15:01:43.017574Z",
+ "iopub.status.idle": "2024-07-01T15:01:45.116344Z",
+ "shell.execute_reply": "2024-07-01T15:01:45.115698Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:06.712672Z",
- "iopub.status.busy": "2024-06-28T15:32:06.711755Z",
- "iopub.status.idle": "2024-06-28T15:32:06.724142Z",
- "shell.execute_reply": "2024-06-28T15:32:06.723572Z"
+ "iopub.execute_input": "2024-07-01T15:01:45.119290Z",
+ "iopub.status.busy": "2024-07-01T15:01:45.118731Z",
+ "iopub.status.idle": "2024-07-01T15:01:45.130593Z",
+ "shell.execute_reply": "2024-07-01T15:01:45.130118Z"
}
},
"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:06.726394Z",
- "iopub.status.busy": "2024-06-28T15:32:06.726040Z",
- "iopub.status.idle": "2024-06-28T15:32:06.750576Z",
- "shell.execute_reply": "2024-06-28T15:32:06.750015Z"
+ "iopub.execute_input": "2024-07-01T15:01:45.132594Z",
+ "iopub.status.busy": "2024-07-01T15:01:45.132413Z",
+ "iopub.status.idle": "2024-07-01T15:01:45.200709Z",
+ "shell.execute_reply": "2024-07-01T15:01:45.200202Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
index abdb62899..e5a2ac8fa 100644
--- a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
@@ -115,10 +115,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:10.483142Z",
- "iopub.status.busy": "2024-06-28T15:32:10.482709Z",
- "iopub.status.idle": "2024-06-28T15:32:13.547102Z",
- "shell.execute_reply": "2024-06-28T15:32:13.546477Z"
+ "iopub.execute_input": "2024-07-01T15:01:48.389395Z",
+ "iopub.status.busy": "2024-07-01T15:01:48.389202Z",
+ "iopub.status.idle": "2024-07-01T15:01:51.596566Z",
+ "shell.execute_reply": "2024-07-01T15:01:51.595964Z"
},
"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@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -160,10 +160,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:13.549911Z",
- "iopub.status.busy": "2024-06-28T15:32:13.549439Z",
- "iopub.status.idle": "2024-06-28T15:32:13.552758Z",
- "shell.execute_reply": "2024-06-28T15:32:13.552287Z"
+ "iopub.execute_input": "2024-07-01T15:01:51.599757Z",
+ "iopub.status.busy": "2024-07-01T15:01:51.599136Z",
+ "iopub.status.idle": "2024-07-01T15:01:51.603065Z",
+ "shell.execute_reply": "2024-07-01T15:01:51.602415Z"
}
},
"outputs": [],
@@ -185,10 +185,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:13.554848Z",
- "iopub.status.busy": "2024-06-28T15:32:13.554437Z",
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@@ -342,7 +342,7 @@
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"This dataset has 10 classes.\n",
- "Classes: {'card_about_to_expire', 'cancel_transfer', 'getting_spare_card', 'beneficiary_not_allowed', 'visa_or_mastercard', 'change_pin', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'lost_or_stolen_phone'}\n"
+ "Classes: {'getting_spare_card', 'cancel_transfer', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_about_to_expire', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'change_pin'}\n"
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@@ -3414,33 +3369,46 @@
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- "_view_name": "ProgressView",
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- "layout": "IPY_MODEL_8d71fa197d2545bba0a3256f24725cae",
- "max": 2211.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_2b8cf285d37641e78f819e258a1471d5",
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- "value": 2211.0
+ "value": "README.md: 100%"
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@@ -3493,7 +3461,23 @@
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@@ -3546,7 +3530,49 @@
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+ "IPY_MODEL_3123835078454cf9b22abb8a471265a0"
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@@ -3598,32 +3624,6 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
index ef95879eb..2f2d2a40d 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@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
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@@ -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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@@ -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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@@ -717,10 +717,10 @@
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@@ -807,10 +807,10 @@
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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 fe9f08e9b..1e7141136 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
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"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@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -1247,10 +1247,10 @@
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@@ -1307,10 +1307,10 @@
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@@ -1447,7 +1447,7 @@
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@@ -1500,48 +1500,33 @@
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- "value": "Saving the dataset (1/1 shards): 100%"
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@@ -1594,7 +1579,7 @@
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@@ -1647,49 +1632,30 @@
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- "_view_name": "ProgressView",
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- "layout": "IPY_MODEL_84e1f61107d940b7977e4fff0fb8f2bd",
- "max": 132.0,
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- "orientation": "horizontal",
- "style": "IPY_MODEL_905e6b0f80c7424ca91d5ba60216672e",
+ "layout": "IPY_MODEL_746118eed9d445c9a37e681cbacd9674",
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+ "style": "IPY_MODEL_d57ca1f4799d45229ae2f7c720c262f5",
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- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
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- "description_width": ""
+ "value": "Saving the dataset (1/1 shards): 100%"
}
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@@ -1742,7 +1708,7 @@
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@@ -1760,31 +1726,23 @@
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- "_view_module": "@jupyter-widgets/controls",
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@@ -1799,12 +1757,54 @@
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- "style": "IPY_MODEL_b93d0824edb848df8dfe762bda5a4c34",
+ "style": "IPY_MODEL_bcb7b5d047f845978925e2ef6da3385e",
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"tooltip": null,
- "value": " 132/132 [00:00<00:00, 12955.99 examples/s]"
+ "value": " 132/132 [00:00<00:00, 11804.36 examples/s]"
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+ "model_module": "@jupyter-widgets/controls",
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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 0ab929cc5..e8c4bda9d 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
"metadata": {
"execution": {
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- "iopub.status.busy": "2024-06-28T15:33:02.265601Z",
- "iopub.status.idle": "2024-06-28T15:33:03.540244Z",
- "shell.execute_reply": "2024-06-28T15:33:03.539709Z"
+ "iopub.execute_input": "2024-07-01T15:02:41.409044Z",
+ "iopub.status.busy": "2024-07-01T15:02:41.408875Z",
+ "iopub.status.idle": "2024-07-01T15:02:42.611044Z",
+ "shell.execute_reply": "2024-07-01T15:02:42.610498Z"
},
"nbsphinx": "hidden"
},
@@ -91,7 +91,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\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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- "shell.execute_reply": "2024-06-28T15:33:03.545256Z"
+ "iopub.execute_input": "2024-07-01T15:02:42.613616Z",
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@@ -250,10 +250,10 @@
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- "shell.execute_reply": "2024-06-28T15:33:03.556489Z"
+ "iopub.execute_input": "2024-07-01T15:02:42.618748Z",
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},
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@@ -356,10 +356,10 @@
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- "shell.execute_reply": "2024-06-28T15:33:03.563684Z"
+ "iopub.execute_input": "2024-07-01T15:02:42.629537Z",
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+ "shell.execute_reply": "2024-07-01T15:02:42.633516Z"
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@@ -448,10 +448,10 @@
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+ "iopub.status.busy": "2024-07-01T15:02:42.635851Z",
+ "iopub.status.idle": "2024-07-01T15:02:42.823356Z",
+ "shell.execute_reply": "2024-07-01T15:02:42.822807Z"
},
"nbsphinx": "hidden"
},
@@ -520,10 +520,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:03.757588Z",
- "iopub.status.busy": "2024-06-28T15:33:03.757353Z",
- "iopub.status.idle": "2024-06-28T15:33:04.137542Z",
- "shell.execute_reply": "2024-06-28T15:33:04.136949Z"
+ "iopub.execute_input": "2024-07-01T15:02:42.826055Z",
+ "iopub.status.busy": "2024-07-01T15:02:42.825690Z",
+ "iopub.status.idle": "2024-07-01T15:02:43.206067Z",
+ "shell.execute_reply": "2024-07-01T15:02:43.205474Z"
}
},
"outputs": [
@@ -559,10 +559,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:04.139670Z",
- "iopub.status.busy": "2024-06-28T15:33:04.139477Z",
- "iopub.status.idle": "2024-06-28T15:33:04.142221Z",
- "shell.execute_reply": "2024-06-28T15:33:04.141788Z"
+ "iopub.execute_input": "2024-07-01T15:02:43.208532Z",
+ "iopub.status.busy": "2024-07-01T15:02:43.208145Z",
+ "iopub.status.idle": "2024-07-01T15:02:43.211102Z",
+ "shell.execute_reply": "2024-07-01T15:02:43.210626Z"
}
},
"outputs": [],
@@ -602,10 +602,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:04.144228Z",
- "iopub.status.busy": "2024-06-28T15:33:04.144051Z",
- "iopub.status.idle": "2024-06-28T15:33:04.182999Z",
- "shell.execute_reply": "2024-06-28T15:33:04.182501Z"
+ "iopub.execute_input": "2024-07-01T15:02:43.213282Z",
+ "iopub.status.busy": "2024-07-01T15:02:43.212936Z",
+ "iopub.status.idle": "2024-07-01T15:02:43.248404Z",
+ "shell.execute_reply": "2024-07-01T15:02:43.247768Z"
}
},
"outputs": [],
@@ -638,10 +638,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:04.185443Z",
- "iopub.status.busy": "2024-06-28T15:33:04.185256Z",
- "iopub.status.idle": "2024-06-28T15:33:06.400028Z",
- "shell.execute_reply": "2024-06-28T15:33:06.399335Z"
+ "iopub.execute_input": "2024-07-01T15:02:43.251350Z",
+ "iopub.status.busy": "2024-07-01T15:02:43.250964Z",
+ "iopub.status.idle": "2024-07-01T15:02:45.296650Z",
+ "shell.execute_reply": "2024-07-01T15:02:45.296009Z"
}
},
"outputs": [
@@ -685,10 +685,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:06.402739Z",
- "iopub.status.busy": "2024-06-28T15:33:06.402145Z",
- "iopub.status.idle": "2024-06-28T15:33:06.421740Z",
- "shell.execute_reply": "2024-06-28T15:33:06.421232Z"
+ "iopub.execute_input": "2024-07-01T15:02:45.298975Z",
+ "iopub.status.busy": "2024-07-01T15:02:45.298607Z",
+ "iopub.status.idle": "2024-07-01T15:02:45.317301Z",
+ "shell.execute_reply": "2024-07-01T15:02:45.316762Z"
}
},
"outputs": [
@@ -821,10 +821,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:06.423971Z",
- "iopub.status.busy": "2024-06-28T15:33:06.423624Z",
- "iopub.status.idle": "2024-06-28T15:33:06.430447Z",
- "shell.execute_reply": "2024-06-28T15:33:06.429995Z"
+ "iopub.execute_input": "2024-07-01T15:02:45.319610Z",
+ "iopub.status.busy": "2024-07-01T15:02:45.319291Z",
+ "iopub.status.idle": "2024-07-01T15:02:45.325606Z",
+ "shell.execute_reply": "2024-07-01T15:02:45.325097Z"
}
},
"outputs": [
@@ -935,10 +935,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:06.432560Z",
- "iopub.status.busy": "2024-06-28T15:33:06.432209Z",
- "iopub.status.idle": "2024-06-28T15:33:06.438239Z",
- "shell.execute_reply": "2024-06-28T15:33:06.437699Z"
+ "iopub.execute_input": "2024-07-01T15:02:45.327815Z",
+ "iopub.status.busy": "2024-07-01T15:02:45.327438Z",
+ "iopub.status.idle": "2024-07-01T15:02:45.333038Z",
+ "shell.execute_reply": "2024-07-01T15:02:45.332566Z"
}
},
"outputs": [
@@ -1005,10 +1005,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:06.440265Z",
- "iopub.status.busy": "2024-06-28T15:33:06.439954Z",
- "iopub.status.idle": "2024-06-28T15:33:06.450509Z",
- "shell.execute_reply": "2024-06-28T15:33:06.450061Z"
+ "iopub.execute_input": "2024-07-01T15:02:45.335093Z",
+ "iopub.status.busy": "2024-07-01T15:02:45.334787Z",
+ "iopub.status.idle": "2024-07-01T15:02:45.345460Z",
+ "shell.execute_reply": "2024-07-01T15:02:45.344912Z"
}
},
"outputs": [
@@ -1200,10 +1200,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:06.452688Z",
- "iopub.status.busy": "2024-06-28T15:33:06.452339Z",
- "iopub.status.idle": "2024-06-28T15:33:06.461491Z",
- "shell.execute_reply": "2024-06-28T15:33:06.461029Z"
+ "iopub.execute_input": "2024-07-01T15:02:45.347438Z",
+ "iopub.status.busy": "2024-07-01T15:02:45.347138Z",
+ "iopub.status.idle": "2024-07-01T15:02:45.356126Z",
+ "shell.execute_reply": "2024-07-01T15:02:45.355581Z"
}
},
"outputs": [
@@ -1319,10 +1319,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:06.463689Z",
- "iopub.status.busy": "2024-06-28T15:33:06.463348Z",
- "iopub.status.idle": "2024-06-28T15:33:06.470367Z",
- "shell.execute_reply": "2024-06-28T15:33:06.469791Z"
+ "iopub.execute_input": "2024-07-01T15:02:45.358103Z",
+ "iopub.status.busy": "2024-07-01T15:02:45.357792Z",
+ "iopub.status.idle": "2024-07-01T15:02:45.364571Z",
+ "shell.execute_reply": "2024-07-01T15:02:45.364114Z"
},
"scrolled": true
},
@@ -1447,10 +1447,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:06.472356Z",
- "iopub.status.busy": "2024-06-28T15:33:06.472176Z",
- "iopub.status.idle": "2024-06-28T15:33:06.482081Z",
- "shell.execute_reply": "2024-06-28T15:33:06.481598Z"
+ "iopub.execute_input": "2024-07-01T15:02:45.366559Z",
+ "iopub.status.busy": "2024-07-01T15:02:45.366255Z",
+ "iopub.status.idle": "2024-07-01T15:02:45.375353Z",
+ "shell.execute_reply": "2024-07-01T15:02:45.374817Z"
}
},
"outputs": [
@@ -1553,10 +1553,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:06.484231Z",
- "iopub.status.busy": "2024-06-28T15:33:06.483891Z",
- "iopub.status.idle": "2024-06-28T15:33:06.495536Z",
- "shell.execute_reply": "2024-06-28T15:33:06.495097Z"
+ "iopub.execute_input": "2024-07-01T15:02:45.377315Z",
+ "iopub.status.busy": "2024-07-01T15:02:45.377010Z",
+ "iopub.status.idle": "2024-07-01T15:02:45.392963Z",
+ "shell.execute_reply": "2024-07-01T15:02:45.392390Z"
},
"nbsphinx": "hidden"
},
@@ -1565,9 +1565,11 @@
"# Note: This cell is only for docs.cleanlab.ai, if running on local Jupyter or Colab, please ignore it.\n",
"from sklearn.metrics import roc_auc_score\n",
"\n",
- "issue_results = lab.get_issues(\"label\")\n",
- "outlier_results = lab.get_issues(\"outlier\")\n",
- "duplicate_results = lab.get_issues(\"near_duplicate\")\n",
+ "def precision_at_k(predicted_indices, true_indices, k):\n",
+ " return len(set(predicted_indices[:k]).intersection(set(true_indices))) / k\n",
+ "\n",
+ "def recall_at_k(predicted_indices, true_indices, k):\n",
+ " return len(set(predicted_indices[:k]).intersection(set(true_indices))) / len(true_indices)\n",
"\n",
"def jaccard_similarity(l1, l2):\n",
" s1 = set(l1)\n",
@@ -1578,26 +1580,40 @@
" return 0\n",
" return len(intersect_set) / len(union_set)\n",
"\n",
- "identified_label_issues_indices = issue_results[issue_results[\"is_label_issue\"] == True].index.tolist()\n",
+ "label_issues = lab.get_issues(\"label\")\n",
+ "predicted_label_issues_indices = (\n",
+ " label_issues.query(\"is_label_issue\").sort_values(\"label_score\").index.to_list()\n",
+ ")\n",
+ "predicted_label_issues_indices_by_score = (\n",
+ " label_issues.sort_values(\"label_score\").index.to_list()\n",
+ ")\n",
"label_issue_indices = np.where(y_train_idx != noisy_labels_idx)[0]\n",
"\n",
- "label_quality_scores = issue_results[\"label_score\"].tolist()\n",
+ "label_quality_scores = label_issues[\"label_score\"].tolist()\n",
"Z = (y_train_idx == noisy_labels_idx).astype(float).tolist()\n",
"\n",
- "identified_outlier_issues_indices = outlier_results[outlier_results[\"is_outlier_issue\"] == True].index.to_list()\n",
+ "predicted_outlier_issues_indices = (\n",
+ " lab.get_issues(\"outlier\").query(\"is_outlier_issue\").index.to_list()\n",
+ ")\n",
"outlier_issue_indices = list(range(125, 130+1))\n",
"exact_duplicate_idx = [index for index, elem in enumerate(X_train) if (elem == X_duplicate).all()][0]\n",
"if exact_duplicate_idx >= 125: # if the random index selected to create a duplicate >= 125, then the last point is also an outlier\n",
" outlier_issue_indices.append(131)\n",
- " \n",
- "identified_duplicate_issues_indices = duplicate_results[duplicate_results[\"is_near_duplicate_issue\"] == True].index.tolist()\n",
- "duplicate_issue_indices = [exact_duplicate_idx, 129, 130, 131]\n",
"\n",
+ "predicted_duplicate_issues_indices = (\n",
+ " lab.get_issues(\"near_duplicate\").query(\"is_near_duplicate_issue\").index.tolist()\n",
+ ")\n",
+ "duplicate_issue_indices = [exact_duplicate_idx, 129, 130, 131]\n",
"\n",
- "assert jaccard_similarity(identified_label_issues_indices, label_issue_indices) > 0.4\n",
+ "k = len(label_issue_indices)\n",
+ "assert precision_at_k(predicted_label_issues_indices, label_issue_indices, k) >= 0.75\n",
+ "assert recall_at_k(predicted_label_issues_indices, label_issue_indices, k) >= 0.75\n",
+ "assert precision_at_k(predicted_label_issues_indices_by_score, label_issue_indices, k) == 1.0\n",
+ "assert recall_at_k(predicted_label_issues_indices_by_score, label_issue_indices, k) == 1.0\n",
"assert roc_auc_score(Z, label_quality_scores) > 0.9\n",
- "assert jaccard_similarity(identified_outlier_issues_indices, outlier_issue_indices) > 0.9\n",
- "assert jaccard_similarity(identified_duplicate_issues_indices, duplicate_issue_indices) > 0.9"
+ "\n",
+ "assert jaccard_similarity(predicted_outlier_issues_indices, outlier_issue_indices) > 0.9\n",
+ "assert jaccard_similarity(predicted_duplicate_issues_indices, duplicate_issue_indices) > 0.9"
]
}
],
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
index 9bd5eb804..03d847503 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": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:09.403235Z",
- "iopub.status.busy": "2024-06-28T15:33:09.402738Z",
- "iopub.status.idle": "2024-06-28T15:33:12.466652Z",
- "shell.execute_reply": "2024-06-28T15:33:12.466079Z"
+ "iopub.execute_input": "2024-07-01T15:02:48.074971Z",
+ "iopub.status.busy": "2024-07-01T15:02:48.074723Z",
+ "iopub.status.idle": "2024-07-01T15:02:51.342353Z",
+ "shell.execute_reply": "2024-07-01T15:02:51.341605Z"
},
"nbsphinx": "hidden"
},
@@ -112,10 +112,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:12.469241Z",
- "iopub.status.busy": "2024-06-28T15:33:12.468934Z",
- "iopub.status.idle": "2024-06-28T15:33:12.472447Z",
- "shell.execute_reply": "2024-06-28T15:33:12.472019Z"
+ "iopub.execute_input": "2024-07-01T15:02:51.345614Z",
+ "iopub.status.busy": "2024-07-01T15:02:51.345060Z",
+ "iopub.status.idle": "2024-07-01T15:02:51.349168Z",
+ "shell.execute_reply": "2024-07-01T15:02:51.348682Z"
}
},
"outputs": [],
@@ -152,17 +152,17 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:12.474380Z",
- "iopub.status.busy": "2024-06-28T15:33:12.474201Z",
- "iopub.status.idle": "2024-06-28T15:33:24.977088Z",
- "shell.execute_reply": "2024-06-28T15:33:24.976474Z"
+ "iopub.execute_input": "2024-07-01T15:02:51.351438Z",
+ "iopub.status.busy": "2024-07-01T15:02:51.351054Z",
+ "iopub.status.idle": "2024-07-01T15:03:02.526470Z",
+ "shell.execute_reply": "2024-07-01T15:03:02.525870Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "dd367ed6b3e145b9ba56c440d63f6948",
+ "model_id": "bd4e5e775e0d4b5d90568b686f8fd56f",
"version_major": 2,
"version_minor": 0
},
@@ -176,7 +176,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "999579ced4a04955b6fe76b06613510d",
+ "model_id": "a9efee99388e4bd987cba82e4c249be5",
"version_major": 2,
"version_minor": 0
},
@@ -190,7 +190,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "9611c5ddf09444219b0832f81930fdd7",
+ "model_id": "b13b21c3b7544706aacfbba4f3504a8b",
"version_major": 2,
"version_minor": 0
},
@@ -204,7 +204,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "2bbd780010f04629a57e9a48d6241a4e",
+ "model_id": "dcfb76cdced842fd810c0329fa0f1c7f",
"version_major": 2,
"version_minor": 0
},
@@ -218,7 +218,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "21ffc7623d52499f96e098345ad1b94d",
+ "model_id": "0febc72cf36d4d939a7991cbb880240e",
"version_major": 2,
"version_minor": 0
},
@@ -232,7 +232,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "8c14226990a04e87aa10a393e2a0203a",
+ "model_id": "6296fc9f1a3947edb989ab3a35afbefe",
"version_major": 2,
"version_minor": 0
},
@@ -246,7 +246,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "519660b9e9fd424bbb188e3f3d9d3b89",
+ "model_id": "bc98754b340343f594559442ba450aa4",
"version_major": 2,
"version_minor": 0
},
@@ -260,7 +260,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "510e84f543554f8d8f0f21ce00483d7d",
+ "model_id": "d60f32b2907d4a288385a30c717ef39d",
"version_major": 2,
"version_minor": 0
},
@@ -302,10 +302,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:24.979342Z",
- "iopub.status.busy": "2024-06-28T15:33:24.979046Z",
- "iopub.status.idle": "2024-06-28T15:33:24.983576Z",
- "shell.execute_reply": "2024-06-28T15:33:24.983103Z"
+ "iopub.execute_input": "2024-07-01T15:03:02.528967Z",
+ "iopub.status.busy": "2024-07-01T15:03:02.528621Z",
+ "iopub.status.idle": "2024-07-01T15:03:02.532647Z",
+ "shell.execute_reply": "2024-07-01T15:03:02.532080Z"
}
},
"outputs": [
@@ -330,17 +330,17 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:24.985815Z",
- "iopub.status.busy": "2024-06-28T15:33:24.985393Z",
- "iopub.status.idle": "2024-06-28T15:33:36.598392Z",
- "shell.execute_reply": "2024-06-28T15:33:36.597792Z"
+ "iopub.execute_input": "2024-07-01T15:03:02.534910Z",
+ "iopub.status.busy": "2024-07-01T15:03:02.534585Z",
+ "iopub.status.idle": "2024-07-01T15:03:13.866603Z",
+ "shell.execute_reply": "2024-07-01T15:03:13.865937Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "748b7bbe2c8345c6a22623d9f52f46cf",
+ "model_id": "70b6c17f51c948158afefdd56830a23f",
"version_major": 2,
"version_minor": 0
},
@@ -378,10 +378,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:36.601110Z",
- "iopub.status.busy": "2024-06-28T15:33:36.600777Z",
- "iopub.status.idle": "2024-06-28T15:33:55.067117Z",
- "shell.execute_reply": "2024-06-28T15:33:55.066477Z"
+ "iopub.execute_input": "2024-07-01T15:03:13.869049Z",
+ "iopub.status.busy": "2024-07-01T15:03:13.868821Z",
+ "iopub.status.idle": "2024-07-01T15:03:31.582919Z",
+ "shell.execute_reply": "2024-07-01T15:03:31.582298Z"
}
},
"outputs": [],
@@ -414,10 +414,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:55.070279Z",
- "iopub.status.busy": "2024-06-28T15:33:55.069803Z",
- "iopub.status.idle": "2024-06-28T15:33:55.075612Z",
- "shell.execute_reply": "2024-06-28T15:33:55.075060Z"
+ "iopub.execute_input": "2024-07-01T15:03:31.585953Z",
+ "iopub.status.busy": "2024-07-01T15:03:31.585389Z",
+ "iopub.status.idle": "2024-07-01T15:03:31.591279Z",
+ "shell.execute_reply": "2024-07-01T15:03:31.590830Z"
}
},
"outputs": [],
@@ -455,10 +455,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:55.078062Z",
- "iopub.status.busy": "2024-06-28T15:33:55.077664Z",
- "iopub.status.idle": "2024-06-28T15:33:55.082536Z",
- "shell.execute_reply": "2024-06-28T15:33:55.081922Z"
+ "iopub.execute_input": "2024-07-01T15:03:31.593306Z",
+ "iopub.status.busy": "2024-07-01T15:03:31.592981Z",
+ "iopub.status.idle": "2024-07-01T15:03:31.596855Z",
+ "shell.execute_reply": "2024-07-01T15:03:31.596450Z"
},
"nbsphinx": "hidden"
},
@@ -595,10 +595,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:55.085223Z",
- "iopub.status.busy": "2024-06-28T15:33:55.084873Z",
- "iopub.status.idle": "2024-06-28T15:33:55.094273Z",
- "shell.execute_reply": "2024-06-28T15:33:55.093706Z"
+ "iopub.execute_input": "2024-07-01T15:03:31.598838Z",
+ "iopub.status.busy": "2024-07-01T15:03:31.598577Z",
+ "iopub.status.idle": "2024-07-01T15:03:31.607398Z",
+ "shell.execute_reply": "2024-07-01T15:03:31.606925Z"
},
"nbsphinx": "hidden"
},
@@ -723,10 +723,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:55.096457Z",
- "iopub.status.busy": "2024-06-28T15:33:55.096269Z",
- "iopub.status.idle": "2024-06-28T15:33:55.123566Z",
- "shell.execute_reply": "2024-06-28T15:33:55.123064Z"
+ "iopub.execute_input": "2024-07-01T15:03:31.609325Z",
+ "iopub.status.busy": "2024-07-01T15:03:31.609007Z",
+ "iopub.status.idle": "2024-07-01T15:03:31.635278Z",
+ "shell.execute_reply": "2024-07-01T15:03:31.634840Z"
}
},
"outputs": [],
@@ -763,10 +763,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:55.126192Z",
- "iopub.status.busy": "2024-06-28T15:33:55.125844Z",
- "iopub.status.idle": "2024-06-28T15:34:29.556539Z",
- "shell.execute_reply": "2024-06-28T15:34:29.555882Z"
+ "iopub.execute_input": "2024-07-01T15:03:31.637322Z",
+ "iopub.status.busy": "2024-07-01T15:03:31.636996Z",
+ "iopub.status.idle": "2024-07-01T15:04:03.652341Z",
+ "shell.execute_reply": "2024-07-01T15:04:03.651742Z"
}
},
"outputs": [
@@ -782,21 +782,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.070\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.749\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.893\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.439\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "01d874da00234ef1aa34287815d37d45",
+ "model_id": "b69aa5fb137444eb962d31f239578d65",
"version_major": 2,
"version_minor": 0
},
@@ -817,7 +817,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "02ee74aebbc04f9a82b5829341e501a6",
+ "model_id": "6ca247bf72f54f03aabdd5d72546025f",
"version_major": 2,
"version_minor": 0
},
@@ -840,21 +840,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.975\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.851\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.917\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.491\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ddb26404cd4644d3a9efa8efd7af9104",
+ "model_id": "d6465626e3264fa58f44ddccd18cfef2",
"version_major": 2,
"version_minor": 0
},
@@ -875,7 +875,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c60874cc394b428786de11432b5ba1ce",
+ "model_id": "3fa46dee97a14f9594eb60312b03e045",
"version_major": 2,
"version_minor": 0
},
@@ -898,21 +898,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.951\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.739\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 5.061\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.490\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ca1506e7d3f846adb2f7487be4ad5f1d",
+ "model_id": "76cd9d157bf74d6e93db6f5727c6f900",
"version_major": 2,
"version_minor": 0
},
@@ -933,7 +933,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ff1c46ea9c474d1bb4645c7cbbf298b0",
+ "model_id": "9717f3b4aaae491d9cb2e07d49a003a5",
"version_major": 2,
"version_minor": 0
},
@@ -1012,10 +1012,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:34:29.558914Z",
- "iopub.status.busy": "2024-06-28T15:34:29.558674Z",
- "iopub.status.idle": "2024-06-28T15:34:29.573241Z",
- "shell.execute_reply": "2024-06-28T15:34:29.572642Z"
+ "iopub.execute_input": "2024-07-01T15:04:03.654962Z",
+ "iopub.status.busy": "2024-07-01T15:04:03.654720Z",
+ "iopub.status.idle": "2024-07-01T15:04:03.668632Z",
+ "shell.execute_reply": "2024-07-01T15:04:03.668209Z"
}
},
"outputs": [],
@@ -1040,10 +1040,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:34:29.575486Z",
- "iopub.status.busy": "2024-06-28T15:34:29.575301Z",
- "iopub.status.idle": "2024-06-28T15:34:30.062525Z",
- "shell.execute_reply": "2024-06-28T15:34:30.061951Z"
+ "iopub.execute_input": "2024-07-01T15:04:03.670732Z",
+ "iopub.status.busy": "2024-07-01T15:04:03.670344Z",
+ "iopub.status.idle": "2024-07-01T15:04:04.150524Z",
+ "shell.execute_reply": "2024-07-01T15:04:04.149791Z"
}
},
"outputs": [],
@@ -1063,10 +1063,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:34:30.065567Z",
- "iopub.status.busy": "2024-06-28T15:34:30.065103Z",
- "iopub.status.idle": "2024-06-28T15:36:09.855028Z",
- "shell.execute_reply": "2024-06-28T15:36:09.854365Z"
+ "iopub.execute_input": "2024-07-01T15:04:04.153028Z",
+ "iopub.status.busy": "2024-07-01T15:04:04.152825Z",
+ "iopub.status.idle": "2024-07-01T15:05:40.110641Z",
+ "shell.execute_reply": "2024-07-01T15:05:40.110011Z"
}
},
"outputs": [
@@ -1105,7 +1105,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f55b4eb540fd4f368229bcf7012adf9f",
+ "model_id": "8b242b3757014ca08c0be26603c856e5",
"version_major": 2,
"version_minor": 0
},
@@ -1144,10 +1144,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:09.857655Z",
- "iopub.status.busy": "2024-06-28T15:36:09.857109Z",
- "iopub.status.idle": "2024-06-28T15:36:10.330566Z",
- "shell.execute_reply": "2024-06-28T15:36:10.330003Z"
+ "iopub.execute_input": "2024-07-01T15:05:40.113143Z",
+ "iopub.status.busy": "2024-07-01T15:05:40.112512Z",
+ "iopub.status.idle": "2024-07-01T15:05:40.560298Z",
+ "shell.execute_reply": "2024-07-01T15:05:40.559714Z"
}
},
"outputs": [
@@ -1293,10 +1293,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.333473Z",
- "iopub.status.busy": "2024-06-28T15:36:10.333080Z",
- "iopub.status.idle": "2024-06-28T15:36:10.397225Z",
- "shell.execute_reply": "2024-06-28T15:36:10.396605Z"
+ "iopub.execute_input": "2024-07-01T15:05:40.563315Z",
+ "iopub.status.busy": "2024-07-01T15:05:40.562801Z",
+ "iopub.status.idle": "2024-07-01T15:05:40.624738Z",
+ "shell.execute_reply": "2024-07-01T15:05:40.624116Z"
}
},
"outputs": [
@@ -1400,10 +1400,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.399332Z",
- "iopub.status.busy": "2024-06-28T15:36:10.399151Z",
- "iopub.status.idle": "2024-06-28T15:36:10.408208Z",
- "shell.execute_reply": "2024-06-28T15:36:10.407701Z"
+ "iopub.execute_input": "2024-07-01T15:05:40.627071Z",
+ "iopub.status.busy": "2024-07-01T15:05:40.626639Z",
+ "iopub.status.idle": "2024-07-01T15:05:40.635299Z",
+ "shell.execute_reply": "2024-07-01T15:05:40.634756Z"
}
},
"outputs": [
@@ -1533,10 +1533,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.410606Z",
- "iopub.status.busy": "2024-06-28T15:36:10.410094Z",
- "iopub.status.idle": "2024-06-28T15:36:10.415260Z",
- "shell.execute_reply": "2024-06-28T15:36:10.414712Z"
+ "iopub.execute_input": "2024-07-01T15:05:40.637389Z",
+ "iopub.status.busy": "2024-07-01T15:05:40.636989Z",
+ "iopub.status.idle": "2024-07-01T15:05:40.641711Z",
+ "shell.execute_reply": "2024-07-01T15:05:40.641175Z"
},
"nbsphinx": "hidden"
},
@@ -1582,10 +1582,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.417475Z",
- "iopub.status.busy": "2024-06-28T15:36:10.417052Z",
- "iopub.status.idle": "2024-06-28T15:36:10.958110Z",
- "shell.execute_reply": "2024-06-28T15:36:10.957499Z"
+ "iopub.execute_input": "2024-07-01T15:05:40.643683Z",
+ "iopub.status.busy": "2024-07-01T15:05:40.643498Z",
+ "iopub.status.idle": "2024-07-01T15:05:41.152016Z",
+ "shell.execute_reply": "2024-07-01T15:05:41.151428Z"
}
},
"outputs": [
@@ -1620,10 +1620,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.960727Z",
- "iopub.status.busy": "2024-06-28T15:36:10.960231Z",
- "iopub.status.idle": "2024-06-28T15:36:10.969176Z",
- "shell.execute_reply": "2024-06-28T15:36:10.968687Z"
+ "iopub.execute_input": "2024-07-01T15:05:41.154341Z",
+ "iopub.status.busy": "2024-07-01T15:05:41.154029Z",
+ "iopub.status.idle": "2024-07-01T15:05:41.162706Z",
+ "shell.execute_reply": "2024-07-01T15:05:41.162252Z"
}
},
"outputs": [
@@ -1790,10 +1790,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.971544Z",
- "iopub.status.busy": "2024-06-28T15:36:10.971137Z",
- "iopub.status.idle": "2024-06-28T15:36:10.978595Z",
- "shell.execute_reply": "2024-06-28T15:36:10.978143Z"
+ "iopub.execute_input": "2024-07-01T15:05:41.164766Z",
+ "iopub.status.busy": "2024-07-01T15:05:41.164446Z",
+ "iopub.status.idle": "2024-07-01T15:05:41.171486Z",
+ "shell.execute_reply": "2024-07-01T15:05:41.171059Z"
},
"nbsphinx": "hidden"
},
@@ -1869,10 +1869,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.980809Z",
- "iopub.status.busy": "2024-06-28T15:36:10.980353Z",
- "iopub.status.idle": "2024-06-28T15:36:11.774826Z",
- "shell.execute_reply": "2024-06-28T15:36:11.774218Z"
+ "iopub.execute_input": "2024-07-01T15:05:41.173399Z",
+ "iopub.status.busy": "2024-07-01T15:05:41.173075Z",
+ "iopub.status.idle": "2024-07-01T15:05:41.934946Z",
+ "shell.execute_reply": "2024-07-01T15:05:41.934291Z"
}
},
"outputs": [
@@ -1909,10 +1909,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:11.777464Z",
- "iopub.status.busy": "2024-06-28T15:36:11.777117Z",
- "iopub.status.idle": "2024-06-28T15:36:11.793779Z",
- "shell.execute_reply": "2024-06-28T15:36:11.793195Z"
+ "iopub.execute_input": "2024-07-01T15:05:41.937509Z",
+ "iopub.status.busy": "2024-07-01T15:05:41.937076Z",
+ "iopub.status.idle": "2024-07-01T15:05:41.952809Z",
+ "shell.execute_reply": "2024-07-01T15:05:41.952240Z"
}
},
"outputs": [
@@ -2069,10 +2069,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:11.796111Z",
- "iopub.status.busy": "2024-06-28T15:36:11.795755Z",
- "iopub.status.idle": "2024-06-28T15:36:11.801625Z",
- "shell.execute_reply": "2024-06-28T15:36:11.801150Z"
+ "iopub.execute_input": "2024-07-01T15:05:41.954986Z",
+ "iopub.status.busy": "2024-07-01T15:05:41.954646Z",
+ "iopub.status.idle": "2024-07-01T15:05:41.960097Z",
+ "shell.execute_reply": "2024-07-01T15:05:41.959674Z"
},
"nbsphinx": "hidden"
},
@@ -2117,10 +2117,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:11.803713Z",
- "iopub.status.busy": "2024-06-28T15:36:11.803391Z",
- "iopub.status.idle": "2024-06-28T15:36:12.281855Z",
- "shell.execute_reply": "2024-06-28T15:36:12.281284Z"
+ "iopub.execute_input": "2024-07-01T15:05:41.961941Z",
+ "iopub.status.busy": "2024-07-01T15:05:41.961770Z",
+ "iopub.status.idle": "2024-07-01T15:05:42.348365Z",
+ "shell.execute_reply": "2024-07-01T15:05:42.347794Z"
}
},
"outputs": [
@@ -2202,10 +2202,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:12.285071Z",
- "iopub.status.busy": "2024-06-28T15:36:12.284635Z",
- "iopub.status.idle": "2024-06-28T15:36:12.295272Z",
- "shell.execute_reply": "2024-06-28T15:36:12.294767Z"
+ "iopub.execute_input": "2024-07-01T15:05:42.350754Z",
+ "iopub.status.busy": "2024-07-01T15:05:42.350573Z",
+ "iopub.status.idle": "2024-07-01T15:05:42.359462Z",
+ "shell.execute_reply": "2024-07-01T15:05:42.358866Z"
}
},
"outputs": [
@@ -2333,10 +2333,10 @@
"execution_count": 27,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:12.297976Z",
- "iopub.status.busy": "2024-06-28T15:36:12.297776Z",
- "iopub.status.idle": "2024-06-28T15:36:12.304804Z",
- "shell.execute_reply": "2024-06-28T15:36:12.304249Z"
+ "iopub.execute_input": "2024-07-01T15:05:42.361756Z",
+ "iopub.status.busy": "2024-07-01T15:05:42.361579Z",
+ "iopub.status.idle": "2024-07-01T15:05:42.366530Z",
+ "shell.execute_reply": "2024-07-01T15:05:42.365855Z"
},
"nbsphinx": "hidden"
},
@@ -2373,10 +2373,10 @@
"execution_count": 28,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:12.307029Z",
- "iopub.status.busy": "2024-06-28T15:36:12.306836Z",
- "iopub.status.idle": "2024-06-28T15:36:12.512134Z",
- "shell.execute_reply": "2024-06-28T15:36:12.511544Z"
+ "iopub.execute_input": "2024-07-01T15:05:42.368583Z",
+ "iopub.status.busy": "2024-07-01T15:05:42.368393Z",
+ "iopub.status.idle": "2024-07-01T15:05:42.547146Z",
+ "shell.execute_reply": "2024-07-01T15:05:42.546558Z"
}
},
"outputs": [
@@ -2418,10 +2418,10 @@
"execution_count": 29,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:12.514678Z",
- "iopub.status.busy": "2024-06-28T15:36:12.514470Z",
- "iopub.status.idle": "2024-06-28T15:36:12.523352Z",
- "shell.execute_reply": "2024-06-28T15:36:12.522769Z"
+ "iopub.execute_input": "2024-07-01T15:05:42.549711Z",
+ "iopub.status.busy": "2024-07-01T15:05:42.549286Z",
+ "iopub.status.idle": "2024-07-01T15:05:42.557834Z",
+ "shell.execute_reply": "2024-07-01T15:05:42.557185Z"
}
},
"outputs": [
@@ -2446,47 +2446,47 @@
" \n",
" \n",
" | \n",
- " low_information_score | \n",
" is_low_information_issue | \n",
+ " low_information_score | \n",
"
\n",
" \n",
"
\n",
" \n",
" 53050 | \n",
- " 0.067975 | \n",
" True | \n",
+ " 0.067975 | \n",
"
\n",
" \n",
" 40875 | \n",
- " 0.089929 | \n",
" True | \n",
+ " 0.089929 | \n",
"
\n",
" \n",
" 9594 | \n",
- " 0.092601 | \n",
" True | \n",
+ " 0.092601 | \n",
"
\n",
" \n",
" 34825 | \n",
- " 0.107744 | \n",
" True | \n",
+ " 0.107744 | \n",
"
\n",
" \n",
" 37530 | \n",
- " 0.108516 | \n",
" True | \n",
+ " 0.108516 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " low_information_score is_low_information_issue\n",
- "53050 0.067975 True\n",
- "40875 0.089929 True\n",
- "9594 0.092601 True\n",
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
index f67c6a971..452755a26 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
@@ -73,10 +73,10 @@
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@@ -86,7 +86,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -111,10 +111,10 @@
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@@ -264,10 +264,10 @@
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@@ -336,10 +336,10 @@
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@@ -476,10 +476,10 @@
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@@ -609,10 +609,10 @@
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@@ -716,10 +716,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
index 475827b74..94ec2b5de 100644
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@@ -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@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -121,10 +121,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:29.106890Z",
- "iopub.status.busy": "2024-06-28T15:36:29.106333Z",
- "iopub.status.idle": "2024-06-28T15:36:29.109810Z",
- "shell.execute_reply": "2024-06-28T15:36:29.109260Z"
+ "iopub.execute_input": "2024-07-01T15:05:57.758704Z",
+ "iopub.status.busy": "2024-07-01T15:05:57.758362Z",
+ "iopub.status.idle": "2024-07-01T15:05:57.761689Z",
+ "shell.execute_reply": "2024-07-01T15:05:57.761157Z"
}
},
"outputs": [],
@@ -145,10 +145,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:29.112003Z",
- "iopub.status.busy": "2024-06-28T15:36:29.111592Z",
- "iopub.status.idle": "2024-06-28T15:36:29.114674Z",
- "shell.execute_reply": "2024-06-28T15:36:29.114249Z"
+ "iopub.execute_input": "2024-07-01T15:05:57.763881Z",
+ "iopub.status.busy": "2024-07-01T15:05:57.763378Z",
+ "iopub.status.idle": "2024-07-01T15:05:57.766675Z",
+ "shell.execute_reply": "2024-07-01T15:05:57.766123Z"
},
"nbsphinx": "hidden"
},
@@ -178,10 +178,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:29.116836Z",
- "iopub.status.busy": "2024-06-28T15:36:29.116485Z",
- "iopub.status.idle": "2024-06-28T15:36:29.142651Z",
- "shell.execute_reply": "2024-06-28T15:36:29.142099Z"
+ "iopub.execute_input": "2024-07-01T15:05:57.768614Z",
+ "iopub.status.busy": "2024-07-01T15:05:57.768315Z",
+ "iopub.status.idle": "2024-07-01T15:05:57.808437Z",
+ "shell.execute_reply": "2024-07-01T15:05:57.807887Z"
}
},
"outputs": [
@@ -271,10 +271,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:29.144877Z",
- "iopub.status.busy": "2024-06-28T15:36:29.144489Z",
- "iopub.status.idle": "2024-06-28T15:36:29.148574Z",
- "shell.execute_reply": "2024-06-28T15:36:29.148059Z"
+ "iopub.execute_input": "2024-07-01T15:05:57.810722Z",
+ "iopub.status.busy": "2024-07-01T15:05:57.810309Z",
+ "iopub.status.idle": "2024-07-01T15:05:57.814281Z",
+ "shell.execute_reply": "2024-07-01T15:05:57.813706Z"
}
},
"outputs": [
@@ -283,7 +283,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'visa_or_mastercard', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'getting_spare_card', 'lost_or_stolen_phone', 'change_pin', 'beneficiary_not_allowed', 'cancel_transfer'}\n"
+ "Classes: {'cancel_transfer', 'getting_spare_card', 'change_pin', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'card_about_to_expire'}\n"
]
}
],
@@ -307,10 +307,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:29.150742Z",
- "iopub.status.busy": "2024-06-28T15:36:29.150368Z",
- "iopub.status.idle": "2024-06-28T15:36:29.153564Z",
- "shell.execute_reply": "2024-06-28T15:36:29.152998Z"
+ "iopub.execute_input": "2024-07-01T15:05:57.816292Z",
+ "iopub.status.busy": "2024-07-01T15:05:57.816001Z",
+ "iopub.status.idle": "2024-07-01T15:05:57.819153Z",
+ "shell.execute_reply": "2024-07-01T15:05:57.818607Z"
}
},
"outputs": [
@@ -365,10 +365,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:29.155686Z",
- "iopub.status.busy": "2024-06-28T15:36:29.155353Z",
- "iopub.status.idle": "2024-06-28T15:36:33.062605Z",
- "shell.execute_reply": "2024-06-28T15:36:33.062024Z"
+ "iopub.execute_input": "2024-07-01T15:05:57.821168Z",
+ "iopub.status.busy": "2024-07-01T15:05:57.820783Z",
+ "iopub.status.idle": "2024-07-01T15:06:01.454864Z",
+ "shell.execute_reply": "2024-07-01T15:06:01.454218Z"
}
},
"outputs": [
@@ -416,10 +416,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:33.065550Z",
- "iopub.status.busy": "2024-06-28T15:36:33.065329Z",
- "iopub.status.idle": "2024-06-28T15:36:33.965085Z",
- "shell.execute_reply": "2024-06-28T15:36:33.964485Z"
+ "iopub.execute_input": "2024-07-01T15:06:01.457576Z",
+ "iopub.status.busy": "2024-07-01T15:06:01.457191Z",
+ "iopub.status.idle": "2024-07-01T15:06:02.359759Z",
+ "shell.execute_reply": "2024-07-01T15:06:02.359194Z"
},
"scrolled": true
},
@@ -451,10 +451,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:33.968066Z",
- "iopub.status.busy": "2024-06-28T15:36:33.967676Z",
- "iopub.status.idle": "2024-06-28T15:36:33.970602Z",
- "shell.execute_reply": "2024-06-28T15:36:33.970111Z"
+ "iopub.execute_input": "2024-07-01T15:06:02.362504Z",
+ "iopub.status.busy": "2024-07-01T15:06:02.362099Z",
+ "iopub.status.idle": "2024-07-01T15:06:02.365173Z",
+ "shell.execute_reply": "2024-07-01T15:06:02.364692Z"
}
},
"outputs": [],
@@ -474,10 +474,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:33.972948Z",
- "iopub.status.busy": "2024-06-28T15:36:33.972596Z",
- "iopub.status.idle": "2024-06-28T15:36:36.086496Z",
- "shell.execute_reply": "2024-06-28T15:36:36.085881Z"
+ "iopub.execute_input": "2024-07-01T15:06:02.368303Z",
+ "iopub.status.busy": "2024-07-01T15:06:02.367393Z",
+ "iopub.status.idle": "2024-07-01T15:06:04.354878Z",
+ "shell.execute_reply": "2024-07-01T15:06:04.354255Z"
},
"scrolled": true
},
@@ -521,10 +521,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:36.089588Z",
- "iopub.status.busy": "2024-06-28T15:36:36.088943Z",
- "iopub.status.idle": "2024-06-28T15:36:36.113521Z",
- "shell.execute_reply": "2024-06-28T15:36:36.112998Z"
+ "iopub.execute_input": "2024-07-01T15:06:04.359326Z",
+ "iopub.status.busy": "2024-07-01T15:06:04.358175Z",
+ "iopub.status.idle": "2024-07-01T15:06:04.383863Z",
+ "shell.execute_reply": "2024-07-01T15:06:04.383356Z"
},
"scrolled": true
},
@@ -654,10 +654,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:36.116126Z",
- "iopub.status.busy": "2024-06-28T15:36:36.115727Z",
- "iopub.status.idle": "2024-06-28T15:36:36.125325Z",
- "shell.execute_reply": "2024-06-28T15:36:36.124905Z"
+ "iopub.execute_input": "2024-07-01T15:06:04.387331Z",
+ "iopub.status.busy": "2024-07-01T15:06:04.386438Z",
+ "iopub.status.idle": "2024-07-01T15:06:04.396138Z",
+ "shell.execute_reply": "2024-07-01T15:06:04.395755Z"
},
"scrolled": true
},
@@ -767,10 +767,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:36.127442Z",
- "iopub.status.busy": "2024-06-28T15:36:36.127118Z",
- "iopub.status.idle": "2024-06-28T15:36:36.131240Z",
- "shell.execute_reply": "2024-06-28T15:36:36.130840Z"
+ "iopub.execute_input": "2024-07-01T15:06:04.398058Z",
+ "iopub.status.busy": "2024-07-01T15:06:04.397776Z",
+ "iopub.status.idle": "2024-07-01T15:06:04.401475Z",
+ "shell.execute_reply": "2024-07-01T15:06:04.401092Z"
}
},
"outputs": [
@@ -808,10 +808,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:36.133167Z",
- "iopub.status.busy": "2024-06-28T15:36:36.132866Z",
- "iopub.status.idle": "2024-06-28T15:36:36.138900Z",
- "shell.execute_reply": "2024-06-28T15:36:36.138496Z"
+ "iopub.execute_input": "2024-07-01T15:06:04.403322Z",
+ "iopub.status.busy": "2024-07-01T15:06:04.403036Z",
+ "iopub.status.idle": "2024-07-01T15:06:04.408720Z",
+ "shell.execute_reply": "2024-07-01T15:06:04.408332Z"
}
},
"outputs": [
@@ -928,10 +928,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:36.140862Z",
- "iopub.status.busy": "2024-06-28T15:36:36.140553Z",
- "iopub.status.idle": "2024-06-28T15:36:36.146597Z",
- "shell.execute_reply": "2024-06-28T15:36:36.146207Z"
+ "iopub.execute_input": "2024-07-01T15:06:04.410591Z",
+ "iopub.status.busy": "2024-07-01T15:06:04.410423Z",
+ "iopub.status.idle": "2024-07-01T15:06:04.416683Z",
+ "shell.execute_reply": "2024-07-01T15:06:04.416154Z"
}
},
"outputs": [
@@ -1014,10 +1014,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:36.148465Z",
- "iopub.status.busy": "2024-06-28T15:36:36.148170Z",
- "iopub.status.idle": "2024-06-28T15:36:36.153742Z",
- "shell.execute_reply": "2024-06-28T15:36:36.153299Z"
+ "iopub.execute_input": "2024-07-01T15:06:04.418724Z",
+ "iopub.status.busy": "2024-07-01T15:06:04.418385Z",
+ "iopub.status.idle": "2024-07-01T15:06:04.424043Z",
+ "shell.execute_reply": "2024-07-01T15:06:04.423521Z"
}
},
"outputs": [
@@ -1125,10 +1125,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:36.155782Z",
- "iopub.status.busy": "2024-06-28T15:36:36.155479Z",
- "iopub.status.idle": "2024-06-28T15:36:36.164116Z",
- "shell.execute_reply": "2024-06-28T15:36:36.163656Z"
+ "iopub.execute_input": "2024-07-01T15:06:04.426089Z",
+ "iopub.status.busy": "2024-07-01T15:06:04.425788Z",
+ "iopub.status.idle": "2024-07-01T15:06:04.434068Z",
+ "shell.execute_reply": "2024-07-01T15:06:04.433526Z"
}
},
"outputs": [
@@ -1239,10 +1239,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:36.166281Z",
- "iopub.status.busy": "2024-06-28T15:36:36.165967Z",
- "iopub.status.idle": "2024-06-28T15:36:36.171549Z",
- "shell.execute_reply": "2024-06-28T15:36:36.171073Z"
+ "iopub.execute_input": "2024-07-01T15:06:04.435974Z",
+ "iopub.status.busy": "2024-07-01T15:06:04.435800Z",
+ "iopub.status.idle": "2024-07-01T15:06:04.441070Z",
+ "shell.execute_reply": "2024-07-01T15:06:04.440586Z"
}
},
"outputs": [
@@ -1310,10 +1310,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:36.173603Z",
- "iopub.status.busy": "2024-06-28T15:36:36.173270Z",
- "iopub.status.idle": "2024-06-28T15:36:36.179096Z",
- "shell.execute_reply": "2024-06-28T15:36:36.178548Z"
+ "iopub.execute_input": "2024-07-01T15:06:04.443100Z",
+ "iopub.status.busy": "2024-07-01T15:06:04.442719Z",
+ "iopub.status.idle": "2024-07-01T15:06:04.447928Z",
+ "shell.execute_reply": "2024-07-01T15:06:04.447468Z"
}
},
"outputs": [
@@ -1392,10 +1392,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:36.181331Z",
- "iopub.status.busy": "2024-06-28T15:36:36.181023Z",
- "iopub.status.idle": "2024-06-28T15:36:36.184720Z",
- "shell.execute_reply": "2024-06-28T15:36:36.184156Z"
+ "iopub.execute_input": "2024-07-01T15:06:04.450005Z",
+ "iopub.status.busy": "2024-07-01T15:06:04.449608Z",
+ "iopub.status.idle": "2024-07-01T15:06:04.453217Z",
+ "shell.execute_reply": "2024-07-01T15:06:04.452674Z"
}
},
"outputs": [
@@ -1443,10 +1443,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:36.186917Z",
- "iopub.status.busy": "2024-06-28T15:36:36.186527Z",
- "iopub.status.idle": "2024-06-28T15:36:36.192238Z",
- "shell.execute_reply": "2024-06-28T15:36:36.191670Z"
+ "iopub.execute_input": "2024-07-01T15:06:04.455383Z",
+ "iopub.status.busy": "2024-07-01T15:06:04.455056Z",
+ "iopub.status.idle": "2024-07-01T15:06:04.460142Z",
+ "shell.execute_reply": "2024-07-01T15:06:04.459596Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
index a40cddd8f..71ffac131 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
@@ -38,10 +38,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:40.361660Z",
- "iopub.status.busy": "2024-06-28T15:36:40.361112Z",
- "iopub.status.idle": "2024-06-28T15:36:40.809139Z",
- "shell.execute_reply": "2024-06-28T15:36:40.808612Z"
+ "iopub.execute_input": "2024-07-01T15:06:07.601006Z",
+ "iopub.status.busy": "2024-07-01T15:06:07.600505Z",
+ "iopub.status.idle": "2024-07-01T15:06:08.023065Z",
+ "shell.execute_reply": "2024-07-01T15:06:08.022566Z"
}
},
"outputs": [],
@@ -87,10 +87,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:40.811963Z",
- "iopub.status.busy": "2024-06-28T15:36:40.811488Z",
- "iopub.status.idle": "2024-06-28T15:36:40.944953Z",
- "shell.execute_reply": "2024-06-28T15:36:40.944353Z"
+ "iopub.execute_input": "2024-07-01T15:06:08.025689Z",
+ "iopub.status.busy": "2024-07-01T15:06:08.025283Z",
+ "iopub.status.idle": "2024-07-01T15:06:08.152849Z",
+ "shell.execute_reply": "2024-07-01T15:06:08.152350Z"
}
},
"outputs": [
@@ -181,10 +181,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:40.947272Z",
- "iopub.status.busy": "2024-06-28T15:36:40.946935Z",
- "iopub.status.idle": "2024-06-28T15:36:40.971280Z",
- "shell.execute_reply": "2024-06-28T15:36:40.970561Z"
+ "iopub.execute_input": "2024-07-01T15:06:08.155131Z",
+ "iopub.status.busy": "2024-07-01T15:06:08.154741Z",
+ "iopub.status.idle": "2024-07-01T15:06:08.177601Z",
+ "shell.execute_reply": "2024-07-01T15:06:08.177069Z"
}
},
"outputs": [],
@@ -210,10 +210,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:40.974379Z",
- "iopub.status.busy": "2024-06-28T15:36:40.974119Z",
- "iopub.status.idle": "2024-06-28T15:36:43.938511Z",
- "shell.execute_reply": "2024-06-28T15:36:43.937823Z"
+ "iopub.execute_input": "2024-07-01T15:06:08.180286Z",
+ "iopub.status.busy": "2024-07-01T15:06:08.179872Z",
+ "iopub.status.idle": "2024-07-01T15:06:10.839277Z",
+ "shell.execute_reply": "2024-07-01T15:06:10.838727Z"
}
},
"outputs": [
@@ -280,7 +280,7 @@
" \n",
" 2 | \n",
" outlier | \n",
- " 0.356959 | \n",
+ " 0.356958 | \n",
" 362 | \n",
"
\n",
" \n",
@@ -315,7 +315,7 @@
" issue_type score num_issues\n",
"0 null 1.000000 0\n",
"1 label 0.991400 52\n",
- "2 outlier 0.356959 362\n",
+ "2 outlier 0.356958 362\n",
"3 near_duplicate 0.619565 108\n",
"4 non_iid 0.000000 1\n",
"5 class_imbalance 0.500000 0\n",
@@ -700,10 +700,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:43.941207Z",
- "iopub.status.busy": "2024-06-28T15:36:43.940593Z",
- "iopub.status.idle": "2024-06-28T15:36:52.261673Z",
- "shell.execute_reply": "2024-06-28T15:36:52.261071Z"
+ "iopub.execute_input": "2024-07-01T15:06:10.841884Z",
+ "iopub.status.busy": "2024-07-01T15:06:10.841353Z",
+ "iopub.status.idle": "2024-07-01T15:06:18.620342Z",
+ "shell.execute_reply": "2024-07-01T15:06:18.619784Z"
}
},
"outputs": [
@@ -804,10 +804,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:52.264189Z",
- "iopub.status.busy": "2024-06-28T15:36:52.263727Z",
- "iopub.status.idle": "2024-06-28T15:36:52.413280Z",
- "shell.execute_reply": "2024-06-28T15:36:52.412618Z"
+ "iopub.execute_input": "2024-07-01T15:06:18.622535Z",
+ "iopub.status.busy": "2024-07-01T15:06:18.622344Z",
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@@ -838,10 +838,10 @@
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@@ -1390,10 +1390,10 @@
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@@ -1460,10 +1460,10 @@
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@@ -1729,10 +1729,10 @@
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@@ -1956,10 +1956,10 @@
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@@ -1981,10 +1981,10 @@
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@@ -2142,10 +2142,10 @@
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@@ -2327,10 +2327,10 @@
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@@ -2397,10 +2397,10 @@
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@@ -2451,10 +2451,10 @@
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@@ -3206,10 +3206,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
index 3194f04c5..2c0bb8a06 100644
--- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
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+ "iopub.execute_input": "2024-07-01T15:06:34.327040Z",
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@@ -85,7 +85,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -285,10 +285,10 @@
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},
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diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
index cc0a526c8..86b92fc2a 100644
--- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
@@ -18,10 +18,10 @@
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@@ -137,10 +137,10 @@
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@@ -176,10 +176,10 @@
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@@ -202,10 +202,10 @@
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@@ -228,10 +228,10 @@
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@@ -253,10 +253,10 @@
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@@ -278,10 +278,10 @@
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@@ -363,10 +363,10 @@
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@@ -380,7 +380,7 @@
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@@ -565,10 +565,10 @@
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@@ -585,10 +585,10 @@
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@@ -667,10 +667,10 @@
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@@ -737,10 +737,10 @@
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@@ -826,10 +826,10 @@
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@@ -1285,10 +1285,10 @@
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@@ -1319,7 +1319,7 @@
},
{
"cell_type": "markdown",
- "id": "08a301fa",
+ "id": "6cb95977",
"metadata": {},
"source": [
"### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?"
@@ -1327,7 +1327,7 @@
},
{
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- "id": "d17b4a6d",
+ "id": "7616eae0",
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"The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n",
@@ -1338,7 +1338,7 @@
},
{
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+ "id": "c8e20eef",
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"source": [
"### How to handle near-duplicate data identified by Datalab?\n",
@@ -1349,13 +1349,13 @@
{
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@@ -1457,7 +1457,7 @@
},
{
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"The functions above collect sets of near-duplicate examples. Within each\n",
@@ -1472,13 +1472,13 @@
{
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@@ -1521,13 +1521,13 @@
{
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- "value": "number of examples processed for estimating thresholds: "
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diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
index da76b3390..4c0344124 100644
--- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
@@ -53,10 +53,10 @@
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"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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- "shell.execute_reply": "2024-06-28T15:37:29.390996Z"
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+ "iopub.status.busy": "2024-07-01T15:06:54.028276Z",
+ "iopub.status.idle": "2024-07-01T15:06:54.258453Z",
+ "shell.execute_reply": "2024-07-01T15:06:54.257871Z"
}
},
"outputs": [
@@ -393,10 +393,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:29.393993Z",
- "iopub.status.busy": "2024-06-28T15:37:29.393620Z",
- "iopub.status.idle": "2024-06-28T15:37:29.420116Z",
- "shell.execute_reply": "2024-06-28T15:37:29.419615Z"
+ "iopub.execute_input": "2024-07-01T15:06:54.260736Z",
+ "iopub.status.busy": "2024-07-01T15:06:54.260424Z",
+ "iopub.status.idle": "2024-07-01T15:06:54.286726Z",
+ "shell.execute_reply": "2024-07-01T15:06:54.286174Z"
}
},
"outputs": [],
@@ -428,10 +428,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:29.422535Z",
- "iopub.status.busy": "2024-06-28T15:37:29.422200Z",
- "iopub.status.idle": "2024-06-28T15:37:31.677275Z",
- "shell.execute_reply": "2024-06-28T15:37:31.676561Z"
+ "iopub.execute_input": "2024-07-01T15:06:54.288988Z",
+ "iopub.status.busy": "2024-07-01T15:06:54.288686Z",
+ "iopub.status.idle": "2024-07-01T15:06:56.272596Z",
+ "shell.execute_reply": "2024-07-01T15:06:56.271981Z"
}
},
"outputs": [
@@ -474,10 +474,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:31.679930Z",
- "iopub.status.busy": "2024-06-28T15:37:31.679401Z",
- "iopub.status.idle": "2024-06-28T15:37:31.700730Z",
- "shell.execute_reply": "2024-06-28T15:37:31.700165Z"
+ "iopub.execute_input": "2024-07-01T15:06:56.275147Z",
+ "iopub.status.busy": "2024-07-01T15:06:56.274650Z",
+ "iopub.status.idle": "2024-07-01T15:06:56.292694Z",
+ "shell.execute_reply": "2024-07-01T15:06:56.292260Z"
},
"scrolled": true
},
@@ -607,10 +607,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:31.702898Z",
- "iopub.status.busy": "2024-06-28T15:37:31.702688Z",
- "iopub.status.idle": "2024-06-28T15:37:33.227201Z",
- "shell.execute_reply": "2024-06-28T15:37:33.226576Z"
+ "iopub.execute_input": "2024-07-01T15:06:56.294630Z",
+ "iopub.status.busy": "2024-07-01T15:06:56.294450Z",
+ "iopub.status.idle": "2024-07-01T15:06:57.712997Z",
+ "shell.execute_reply": "2024-07-01T15:06:57.712387Z"
},
"id": "AaHC5MRKjruT"
},
@@ -729,10 +729,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:33.230165Z",
- "iopub.status.busy": "2024-06-28T15:37:33.229351Z",
- "iopub.status.idle": "2024-06-28T15:37:33.243878Z",
- "shell.execute_reply": "2024-06-28T15:37:33.243283Z"
+ "iopub.execute_input": "2024-07-01T15:06:57.715656Z",
+ "iopub.status.busy": "2024-07-01T15:06:57.715048Z",
+ "iopub.status.idle": "2024-07-01T15:06:57.728518Z",
+ "shell.execute_reply": "2024-07-01T15:06:57.728061Z"
},
"id": "Wy27rvyhjruU"
},
@@ -781,10 +781,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:33.246084Z",
- "iopub.status.busy": "2024-06-28T15:37:33.245762Z",
- "iopub.status.idle": "2024-06-28T15:37:33.325997Z",
- "shell.execute_reply": "2024-06-28T15:37:33.325374Z"
+ "iopub.execute_input": "2024-07-01T15:06:57.730397Z",
+ "iopub.status.busy": "2024-07-01T15:06:57.730225Z",
+ "iopub.status.idle": "2024-07-01T15:06:57.801036Z",
+ "shell.execute_reply": "2024-07-01T15:06:57.800476Z"
},
"id": "Db8YHnyVjruU"
},
@@ -891,10 +891,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:33.328410Z",
- "iopub.status.busy": "2024-06-28T15:37:33.328174Z",
- "iopub.status.idle": "2024-06-28T15:37:33.543623Z",
- "shell.execute_reply": "2024-06-28T15:37:33.543031Z"
+ "iopub.execute_input": "2024-07-01T15:06:57.803338Z",
+ "iopub.status.busy": "2024-07-01T15:06:57.802990Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.016553Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.016010Z"
},
"id": "iJqAHuS2jruV"
},
@@ -931,10 +931,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:33.545784Z",
- "iopub.status.busy": "2024-06-28T15:37:33.545592Z",
- "iopub.status.idle": "2024-06-28T15:37:33.562693Z",
- "shell.execute_reply": "2024-06-28T15:37:33.562099Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.018803Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.018381Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.034965Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.034432Z"
},
"id": "PcPTZ_JJG3Cx"
},
@@ -1400,10 +1400,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:33.564886Z",
- "iopub.status.busy": "2024-06-28T15:37:33.564699Z",
- "iopub.status.idle": "2024-06-28T15:37:33.574692Z",
- "shell.execute_reply": "2024-06-28T15:37:33.574212Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.037248Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.036814Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.046245Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.045792Z"
},
"id": "0lonvOYvjruV"
},
@@ -1550,10 +1550,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:33.576695Z",
- "iopub.status.busy": "2024-06-28T15:37:33.576495Z",
- "iopub.status.idle": "2024-06-28T15:37:33.663696Z",
- "shell.execute_reply": "2024-06-28T15:37:33.663087Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.048367Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.048051Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.130967Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.130377Z"
},
"id": "MfqTCa3kjruV"
},
@@ -1634,10 +1634,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:33.666354Z",
- "iopub.status.busy": "2024-06-28T15:37:33.665907Z",
- "iopub.status.idle": "2024-06-28T15:37:33.801045Z",
- "shell.execute_reply": "2024-06-28T15:37:33.800289Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.133536Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.133071Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.249759Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.249159Z"
},
"id": "9ZtWAYXqMAPL"
},
@@ -1697,10 +1697,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:33.803432Z",
- "iopub.status.busy": "2024-06-28T15:37:33.803187Z",
- "iopub.status.idle": "2024-06-28T15:37:33.806941Z",
- "shell.execute_reply": "2024-06-28T15:37:33.806446Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.252274Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.251852Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.255729Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.255184Z"
},
"id": "0rXP3ZPWjruW"
},
@@ -1738,10 +1738,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:33.808893Z",
- "iopub.status.busy": "2024-06-28T15:37:33.808719Z",
- "iopub.status.idle": "2024-06-28T15:37:33.812775Z",
- "shell.execute_reply": "2024-06-28T15:37:33.812256Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.257572Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.257399Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.261023Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.260496Z"
},
"id": "-iRPe8KXjruW"
},
@@ -1796,10 +1796,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:33.814864Z",
- "iopub.status.busy": "2024-06-28T15:37:33.814431Z",
- "iopub.status.idle": "2024-06-28T15:37:33.851921Z",
- "shell.execute_reply": "2024-06-28T15:37:33.851323Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.263055Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.262734Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.298627Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.298174Z"
},
"id": "ZpipUliyjruW"
},
@@ -1850,10 +1850,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:33.854343Z",
- "iopub.status.busy": "2024-06-28T15:37:33.853885Z",
- "iopub.status.idle": "2024-06-28T15:37:33.900346Z",
- "shell.execute_reply": "2024-06-28T15:37:33.899841Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.300556Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.300384Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.341152Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.340674Z"
},
"id": "SLq-3q4xjruX"
},
@@ -1922,10 +1922,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:33.902629Z",
- "iopub.status.busy": "2024-06-28T15:37:33.902277Z",
- "iopub.status.idle": "2024-06-28T15:37:34.010055Z",
- "shell.execute_reply": "2024-06-28T15:37:34.009418Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.343232Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.343056Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.437535Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.436855Z"
},
"id": "g5LHhhuqFbXK"
},
@@ -1957,10 +1957,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:34.012994Z",
- "iopub.status.busy": "2024-06-28T15:37:34.012514Z",
- "iopub.status.idle": "2024-06-28T15:37:34.121092Z",
- "shell.execute_reply": "2024-06-28T15:37:34.120466Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.440127Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.439842Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.527589Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.526960Z"
},
"id": "p7w8F8ezBcet"
},
@@ -2017,10 +2017,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:34.123432Z",
- "iopub.status.busy": "2024-06-28T15:37:34.123168Z",
- "iopub.status.idle": "2024-06-28T15:37:34.337858Z",
- "shell.execute_reply": "2024-06-28T15:37:34.337368Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.529972Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.529737Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.741167Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.740717Z"
},
"id": "WETRL74tE_sU"
},
@@ -2055,10 +2055,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:34.340126Z",
- "iopub.status.busy": "2024-06-28T15:37:34.339793Z",
- "iopub.status.idle": "2024-06-28T15:37:34.547731Z",
- "shell.execute_reply": "2024-06-28T15:37:34.547093Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.743495Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.743153Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.920954Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.920411Z"
},
"id": "kCfdx2gOLmXS"
},
@@ -2220,10 +2220,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:34.550178Z",
- "iopub.status.busy": "2024-06-28T15:37:34.549981Z",
- "iopub.status.idle": "2024-06-28T15:37:34.556248Z",
- "shell.execute_reply": "2024-06-28T15:37:34.555797Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.923453Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.923009Z",
+ "iopub.status.idle": "2024-07-01T15:06:58.928872Z",
+ "shell.execute_reply": "2024-07-01T15:06:58.928426Z"
},
"id": "-uogYRWFYnuu"
},
@@ -2277,10 +2277,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:34.558418Z",
- "iopub.status.busy": "2024-06-28T15:37:34.558088Z",
- "iopub.status.idle": "2024-06-28T15:37:34.773972Z",
- "shell.execute_reply": "2024-06-28T15:37:34.773468Z"
+ "iopub.execute_input": "2024-07-01T15:06:58.930892Z",
+ "iopub.status.busy": "2024-07-01T15:06:58.930502Z",
+ "iopub.status.idle": "2024-07-01T15:06:59.148406Z",
+ "shell.execute_reply": "2024-07-01T15:06:59.147826Z"
},
"id": "pG-ljrmcYp9Q"
},
@@ -2327,10 +2327,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:34.776118Z",
- "iopub.status.busy": "2024-06-28T15:37:34.775925Z",
- "iopub.status.idle": "2024-06-28T15:37:35.877501Z",
- "shell.execute_reply": "2024-06-28T15:37:35.877002Z"
+ "iopub.execute_input": "2024-07-01T15:06:59.150754Z",
+ "iopub.status.busy": "2024-07-01T15:06:59.150391Z",
+ "iopub.status.idle": "2024-07-01T15:07:00.213417Z",
+ "shell.execute_reply": "2024-07-01T15:07:00.212813Z"
},
"id": "wL3ngCnuLEWd"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
index 8af61e177..b426f5b7a 100644
--- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
@@ -88,10 +88,10 @@
"id": "a3ddc95f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:39.503284Z",
- "iopub.status.busy": "2024-06-28T15:37:39.503099Z",
- "iopub.status.idle": "2024-06-28T15:37:40.687920Z",
- "shell.execute_reply": "2024-06-28T15:37:40.687390Z"
+ "iopub.execute_input": "2024-07-01T15:07:03.695403Z",
+ "iopub.status.busy": "2024-07-01T15:07:03.695236Z",
+ "iopub.status.idle": "2024-07-01T15:07:04.786480Z",
+ "shell.execute_reply": "2024-07-01T15:07:04.785971Z"
},
"nbsphinx": "hidden"
},
@@ -101,7 +101,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -135,10 +135,10 @@
"id": "c4efd119",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:40.690877Z",
- "iopub.status.busy": "2024-06-28T15:37:40.690286Z",
- "iopub.status.idle": "2024-06-28T15:37:40.693591Z",
- "shell.execute_reply": "2024-06-28T15:37:40.693053Z"
+ "iopub.execute_input": "2024-07-01T15:07:04.789244Z",
+ "iopub.status.busy": "2024-07-01T15:07:04.788665Z",
+ "iopub.status.idle": "2024-07-01T15:07:04.791892Z",
+ "shell.execute_reply": "2024-07-01T15:07:04.791444Z"
}
},
"outputs": [],
@@ -263,10 +263,10 @@
"id": "c37c0a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:40.695825Z",
- "iopub.status.busy": "2024-06-28T15:37:40.695507Z",
- "iopub.status.idle": "2024-06-28T15:37:40.703252Z",
- "shell.execute_reply": "2024-06-28T15:37:40.702722Z"
+ "iopub.execute_input": "2024-07-01T15:07:04.793920Z",
+ "iopub.status.busy": "2024-07-01T15:07:04.793593Z",
+ "iopub.status.idle": "2024-07-01T15:07:04.801265Z",
+ "shell.execute_reply": "2024-07-01T15:07:04.800810Z"
},
"nbsphinx": "hidden"
},
@@ -350,10 +350,10 @@
"id": "99f69523",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:40.705494Z",
- "iopub.status.busy": "2024-06-28T15:37:40.705178Z",
- "iopub.status.idle": "2024-06-28T15:37:40.752328Z",
- "shell.execute_reply": "2024-06-28T15:37:40.751695Z"
+ "iopub.execute_input": "2024-07-01T15:07:04.803286Z",
+ "iopub.status.busy": "2024-07-01T15:07:04.802900Z",
+ "iopub.status.idle": "2024-07-01T15:07:04.850047Z",
+ "shell.execute_reply": "2024-07-01T15:07:04.849570Z"
}
},
"outputs": [],
@@ -379,10 +379,10 @@
"id": "8f241c16",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:40.755037Z",
- "iopub.status.busy": "2024-06-28T15:37:40.754588Z",
- "iopub.status.idle": "2024-06-28T15:37:40.771776Z",
- "shell.execute_reply": "2024-06-28T15:37:40.771317Z"
+ "iopub.execute_input": "2024-07-01T15:07:04.852247Z",
+ "iopub.status.busy": "2024-07-01T15:07:04.852061Z",
+ "iopub.status.idle": "2024-07-01T15:07:04.869485Z",
+ "shell.execute_reply": "2024-07-01T15:07:04.869018Z"
}
},
"outputs": [
@@ -597,10 +597,10 @@
"id": "4f0819ba",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:40.774143Z",
- "iopub.status.busy": "2024-06-28T15:37:40.773746Z",
- "iopub.status.idle": "2024-06-28T15:37:40.777708Z",
- "shell.execute_reply": "2024-06-28T15:37:40.777230Z"
+ "iopub.execute_input": "2024-07-01T15:07:04.871391Z",
+ "iopub.status.busy": "2024-07-01T15:07:04.871213Z",
+ "iopub.status.idle": "2024-07-01T15:07:04.875222Z",
+ "shell.execute_reply": "2024-07-01T15:07:04.874787Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "d009f347",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:40.779858Z",
- "iopub.status.busy": "2024-06-28T15:37:40.779536Z",
- "iopub.status.idle": "2024-06-28T15:37:40.792878Z",
- "shell.execute_reply": "2024-06-28T15:37:40.792415Z"
+ "iopub.execute_input": "2024-07-01T15:07:04.877103Z",
+ "iopub.status.busy": "2024-07-01T15:07:04.876935Z",
+ "iopub.status.idle": "2024-07-01T15:07:04.890567Z",
+ "shell.execute_reply": "2024-07-01T15:07:04.890109Z"
}
},
"outputs": [],
@@ -698,10 +698,10 @@
"id": "cbd1e415",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:40.794938Z",
- "iopub.status.busy": "2024-06-28T15:37:40.794619Z",
- "iopub.status.idle": "2024-06-28T15:37:40.821318Z",
- "shell.execute_reply": "2024-06-28T15:37:40.820726Z"
+ "iopub.execute_input": "2024-07-01T15:07:04.892340Z",
+ "iopub.status.busy": "2024-07-01T15:07:04.892165Z",
+ "iopub.status.idle": "2024-07-01T15:07:04.917921Z",
+ "shell.execute_reply": "2024-07-01T15:07:04.917510Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"id": "6ca92617",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:40.823690Z",
- "iopub.status.busy": "2024-06-28T15:37:40.823369Z",
- "iopub.status.idle": "2024-06-28T15:37:42.858786Z",
- "shell.execute_reply": "2024-06-28T15:37:42.858257Z"
+ "iopub.execute_input": "2024-07-01T15:07:04.919909Z",
+ "iopub.status.busy": "2024-07-01T15:07:04.919740Z",
+ "iopub.status.idle": "2024-07-01T15:07:06.770405Z",
+ "shell.execute_reply": "2024-07-01T15:07:06.769771Z"
}
},
"outputs": [],
@@ -771,10 +771,10 @@
"id": "bf945113",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:42.861902Z",
- "iopub.status.busy": "2024-06-28T15:37:42.861153Z",
- "iopub.status.idle": "2024-06-28T15:37:42.868329Z",
- "shell.execute_reply": "2024-06-28T15:37:42.867757Z"
+ "iopub.execute_input": "2024-07-01T15:07:06.773094Z",
+ "iopub.status.busy": "2024-07-01T15:07:06.772569Z",
+ "iopub.status.idle": "2024-07-01T15:07:06.779189Z",
+ "shell.execute_reply": "2024-07-01T15:07:06.778660Z"
},
"scrolled": true
},
@@ -885,10 +885,10 @@
"id": "14251ee0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:42.870554Z",
- "iopub.status.busy": "2024-06-28T15:37:42.870224Z",
- "iopub.status.idle": "2024-06-28T15:37:42.882956Z",
- "shell.execute_reply": "2024-06-28T15:37:42.882441Z"
+ "iopub.execute_input": "2024-07-01T15:07:06.781060Z",
+ "iopub.status.busy": "2024-07-01T15:07:06.780797Z",
+ "iopub.status.idle": "2024-07-01T15:07:06.793132Z",
+ "shell.execute_reply": "2024-07-01T15:07:06.792613Z"
}
},
"outputs": [
@@ -1138,10 +1138,10 @@
"id": "efe16638",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:42.885139Z",
- "iopub.status.busy": "2024-06-28T15:37:42.884799Z",
- "iopub.status.idle": "2024-06-28T15:37:42.891477Z",
- "shell.execute_reply": "2024-06-28T15:37:42.890929Z"
+ "iopub.execute_input": "2024-07-01T15:07:06.795311Z",
+ "iopub.status.busy": "2024-07-01T15:07:06.794896Z",
+ "iopub.status.idle": "2024-07-01T15:07:06.801219Z",
+ "shell.execute_reply": "2024-07-01T15:07:06.800801Z"
},
"scrolled": true
},
@@ -1315,10 +1315,10 @@
"id": "abd0fb0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:42.893662Z",
- "iopub.status.busy": "2024-06-28T15:37:42.893329Z",
- "iopub.status.idle": "2024-06-28T15:37:42.895941Z",
- "shell.execute_reply": "2024-06-28T15:37:42.895509Z"
+ "iopub.execute_input": "2024-07-01T15:07:06.803175Z",
+ "iopub.status.busy": "2024-07-01T15:07:06.802994Z",
+ "iopub.status.idle": "2024-07-01T15:07:06.805670Z",
+ "shell.execute_reply": "2024-07-01T15:07:06.805234Z"
}
},
"outputs": [],
@@ -1340,10 +1340,10 @@
"id": "cdf061df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:42.898071Z",
- "iopub.status.busy": "2024-06-28T15:37:42.897574Z",
- "iopub.status.idle": "2024-06-28T15:37:42.901102Z",
- "shell.execute_reply": "2024-06-28T15:37:42.900664Z"
+ "iopub.execute_input": "2024-07-01T15:07:06.807492Z",
+ "iopub.status.busy": "2024-07-01T15:07:06.807328Z",
+ "iopub.status.idle": "2024-07-01T15:07:06.810895Z",
+ "shell.execute_reply": "2024-07-01T15:07:06.810453Z"
},
"scrolled": true
},
@@ -1395,10 +1395,10 @@
"id": "08949890",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:42.903184Z",
- "iopub.status.busy": "2024-06-28T15:37:42.902860Z",
- "iopub.status.idle": "2024-06-28T15:37:42.905499Z",
- "shell.execute_reply": "2024-06-28T15:37:42.905046Z"
+ "iopub.execute_input": "2024-07-01T15:07:06.812899Z",
+ "iopub.status.busy": "2024-07-01T15:07:06.812510Z",
+ "iopub.status.idle": "2024-07-01T15:07:06.815130Z",
+ "shell.execute_reply": "2024-07-01T15:07:06.814702Z"
}
},
"outputs": [],
@@ -1422,10 +1422,10 @@
"id": "6948b073",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:42.907596Z",
- "iopub.status.busy": "2024-06-28T15:37:42.907270Z",
- "iopub.status.idle": "2024-06-28T15:37:42.911435Z",
- "shell.execute_reply": "2024-06-28T15:37:42.910901Z"
+ "iopub.execute_input": "2024-07-01T15:07:06.817057Z",
+ "iopub.status.busy": "2024-07-01T15:07:06.816734Z",
+ "iopub.status.idle": "2024-07-01T15:07:06.820893Z",
+ "shell.execute_reply": "2024-07-01T15:07:06.820448Z"
}
},
"outputs": [
@@ -1480,10 +1480,10 @@
"id": "6f8e6914",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:42.913391Z",
- "iopub.status.busy": "2024-06-28T15:37:42.913217Z",
- "iopub.status.idle": "2024-06-28T15:37:42.941833Z",
- "shell.execute_reply": "2024-06-28T15:37:42.941368Z"
+ "iopub.execute_input": "2024-07-01T15:07:06.822875Z",
+ "iopub.status.busy": "2024-07-01T15:07:06.822704Z",
+ "iopub.status.idle": "2024-07-01T15:07:06.851357Z",
+ "shell.execute_reply": "2024-07-01T15:07:06.850916Z"
}
},
"outputs": [],
@@ -1526,10 +1526,10 @@
"id": "b806d2ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:42.944033Z",
- "iopub.status.busy": "2024-06-28T15:37:42.943856Z",
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- "shell.execute_reply": "2024-06-28T15:37:42.948007Z"
+ "iopub.execute_input": "2024-07-01T15:07:06.853186Z",
+ "iopub.status.busy": "2024-07-01T15:07:06.853017Z",
+ "iopub.status.idle": "2024-07-01T15:07:06.857526Z",
+ "shell.execute_reply": "2024-07-01T15:07:06.857095Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
index fa739c4ac..f9fccade5 100644
--- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
@@ -64,10 +64,10 @@
"id": "7383d024-8273-4039-bccd-aab3020d331f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:46.062655Z",
- "iopub.status.busy": "2024-06-28T15:37:46.062439Z",
- "iopub.status.idle": "2024-06-28T15:37:47.278130Z",
- "shell.execute_reply": "2024-06-28T15:37:47.277493Z"
+ "iopub.execute_input": "2024-07-01T15:07:09.805934Z",
+ "iopub.status.busy": "2024-07-01T15:07:09.805760Z",
+ "iopub.status.idle": "2024-07-01T15:07:10.951874Z",
+ "shell.execute_reply": "2024-07-01T15:07:10.951332Z"
},
"nbsphinx": "hidden"
},
@@ -79,7 +79,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -105,10 +105,10 @@
"id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:47.281006Z",
- "iopub.status.busy": "2024-06-28T15:37:47.280461Z",
- "iopub.status.idle": "2024-06-28T15:37:47.487135Z",
- "shell.execute_reply": "2024-06-28T15:37:47.486629Z"
+ "iopub.execute_input": "2024-07-01T15:07:10.954229Z",
+ "iopub.status.busy": "2024-07-01T15:07:10.953974Z",
+ "iopub.status.idle": "2024-07-01T15:07:11.145898Z",
+ "shell.execute_reply": "2024-07-01T15:07:11.145328Z"
}
},
"outputs": [],
@@ -268,10 +268,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:47.490066Z",
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- "shell.execute_reply": "2024-06-28T15:37:47.502810Z"
+ "iopub.execute_input": "2024-07-01T15:07:11.148952Z",
+ "iopub.status.busy": "2024-07-01T15:07:11.148446Z",
+ "iopub.status.idle": "2024-07-01T15:07:11.162195Z",
+ "shell.execute_reply": "2024-07-01T15:07:11.161701Z"
},
"nbsphinx": "hidden"
},
@@ -407,10 +407,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:47.505906Z",
- "iopub.status.busy": "2024-06-28T15:37:47.505486Z",
- "iopub.status.idle": "2024-06-28T15:37:50.206513Z",
- "shell.execute_reply": "2024-06-28T15:37:50.205911Z"
+ "iopub.execute_input": "2024-07-01T15:07:11.164347Z",
+ "iopub.status.busy": "2024-07-01T15:07:11.163932Z",
+ "iopub.status.idle": "2024-07-01T15:07:13.785011Z",
+ "shell.execute_reply": "2024-07-01T15:07:13.784429Z"
}
},
"outputs": [
@@ -454,10 +454,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:50.208774Z",
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- "iopub.status.idle": "2024-06-28T15:37:51.579425Z",
- "shell.execute_reply": "2024-06-28T15:37:51.578910Z"
+ "iopub.execute_input": "2024-07-01T15:07:13.787263Z",
+ "iopub.status.busy": "2024-07-01T15:07:13.787077Z",
+ "iopub.status.idle": "2024-07-01T15:07:15.129078Z",
+ "shell.execute_reply": "2024-07-01T15:07:15.128523Z"
}
},
"outputs": [],
@@ -499,10 +499,10 @@
"id": "ac1a60df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:51.581852Z",
- "iopub.status.busy": "2024-06-28T15:37:51.581666Z",
- "iopub.status.idle": "2024-06-28T15:37:51.586007Z",
- "shell.execute_reply": "2024-06-28T15:37:51.585531Z"
+ "iopub.execute_input": "2024-07-01T15:07:15.131446Z",
+ "iopub.status.busy": "2024-07-01T15:07:15.131256Z",
+ "iopub.status.idle": "2024-07-01T15:07:15.135063Z",
+ "shell.execute_reply": "2024-07-01T15:07:15.134547Z"
}
},
"outputs": [
@@ -544,10 +544,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:51.587856Z",
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- "iopub.status.idle": "2024-06-28T15:37:53.735997Z",
- "shell.execute_reply": "2024-06-28T15:37:53.735310Z"
+ "iopub.execute_input": "2024-07-01T15:07:15.137004Z",
+ "iopub.status.busy": "2024-07-01T15:07:15.136825Z",
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+ "shell.execute_reply": "2024-07-01T15:07:17.133735Z"
}
},
"outputs": [
@@ -594,10 +594,10 @@
"id": "c18dd83b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:53.738554Z",
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- "iopub.status.idle": "2024-06-28T15:37:53.746671Z",
- "shell.execute_reply": "2024-06-28T15:37:53.746192Z"
+ "iopub.execute_input": "2024-07-01T15:07:17.136852Z",
+ "iopub.status.busy": "2024-07-01T15:07:17.136398Z",
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+ "shell.execute_reply": "2024-07-01T15:07:17.143851Z"
}
},
"outputs": [
@@ -633,10 +633,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:53.748672Z",
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- "shell.execute_reply": "2024-06-28T15:37:56.383027Z"
+ "iopub.execute_input": "2024-07-01T15:07:17.146377Z",
+ "iopub.status.busy": "2024-07-01T15:07:17.146026Z",
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+ "shell.execute_reply": "2024-07-01T15:07:19.686980Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:56.385815Z",
- "iopub.status.busy": "2024-06-28T15:37:56.385620Z",
- "iopub.status.idle": "2024-06-28T15:37:56.389671Z",
- "shell.execute_reply": "2024-06-28T15:37:56.389205Z"
+ "iopub.execute_input": "2024-07-01T15:07:19.689886Z",
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+ "shell.execute_reply": "2024-07-01T15:07:19.692502Z"
}
},
"outputs": [
@@ -721,10 +721,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:56.391657Z",
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- "shell.execute_reply": "2024-06-28T15:37:56.394761Z"
+ "iopub.execute_input": "2024-07-01T15:07:19.694737Z",
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+ "shell.execute_reply": "2024-07-01T15:07:19.697520Z"
}
},
"outputs": [],
@@ -752,10 +752,10 @@
"id": "d1a2c008",
"metadata": {
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- "iopub.execute_input": "2024-06-28T15:37:56.397415Z",
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- "shell.execute_reply": "2024-06-28T15:37:56.399921Z"
+ "iopub.execute_input": "2024-07-01T15:07:19.700087Z",
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+ "shell.execute_reply": "2024-07-01T15:07:19.702302Z"
},
"nbsphinx": "hidden"
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diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
index 19c4a295c..013f8cdd9 100644
--- a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
@@ -70,10 +70,10 @@
"id": "0ba0dc70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:37:59.018700Z",
- "iopub.status.busy": "2024-06-28T15:37:59.018517Z",
- "iopub.status.idle": "2024-06-28T15:38:00.265105Z",
- "shell.execute_reply": "2024-06-28T15:38:00.264576Z"
+ "iopub.execute_input": "2024-07-01T15:07:22.044165Z",
+ "iopub.status.busy": "2024-07-01T15:07:22.043990Z",
+ "iopub.status.idle": "2024-07-01T15:07:23.190619Z",
+ "shell.execute_reply": "2024-07-01T15:07:23.190110Z"
},
"nbsphinx": "hidden"
},
@@ -83,7 +83,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -109,10 +109,10 @@
"id": "c90449c8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:00.267828Z",
- "iopub.status.busy": "2024-06-28T15:38:00.267338Z",
- "iopub.status.idle": "2024-06-28T15:38:01.587223Z",
- "shell.execute_reply": "2024-06-28T15:38:01.586436Z"
+ "iopub.execute_input": "2024-07-01T15:07:23.192992Z",
+ "iopub.status.busy": "2024-07-01T15:07:23.192742Z",
+ "iopub.status.idle": "2024-07-01T15:07:24.642885Z",
+ "shell.execute_reply": "2024-07-01T15:07:24.642213Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:01.589871Z",
- "iopub.status.busy": "2024-06-28T15:38:01.589663Z",
- "iopub.status.idle": "2024-06-28T15:38:01.592876Z",
- "shell.execute_reply": "2024-06-28T15:38:01.592418Z"
+ "iopub.execute_input": "2024-07-01T15:07:24.645453Z",
+ "iopub.status.busy": "2024-07-01T15:07:24.645208Z",
+ "iopub.status.idle": "2024-07-01T15:07:24.648320Z",
+ "shell.execute_reply": "2024-07-01T15:07:24.647888Z"
}
},
"outputs": [],
@@ -169,10 +169,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:01.594798Z",
- "iopub.status.busy": "2024-06-28T15:38:01.594621Z",
- "iopub.status.idle": "2024-06-28T15:38:01.600889Z",
- "shell.execute_reply": "2024-06-28T15:38:01.600425Z"
+ "iopub.execute_input": "2024-07-01T15:07:24.650220Z",
+ "iopub.status.busy": "2024-07-01T15:07:24.650036Z",
+ "iopub.status.idle": "2024-07-01T15:07:24.656028Z",
+ "shell.execute_reply": "2024-07-01T15:07:24.655606Z"
}
},
"outputs": [],
@@ -198,10 +198,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:01.602887Z",
- "iopub.status.busy": "2024-06-28T15:38:01.602700Z",
- "iopub.status.idle": "2024-06-28T15:38:02.098288Z",
- "shell.execute_reply": "2024-06-28T15:38:02.097701Z"
+ "iopub.execute_input": "2024-07-01T15:07:24.657894Z",
+ "iopub.status.busy": "2024-07-01T15:07:24.657721Z",
+ "iopub.status.idle": "2024-07-01T15:07:25.141231Z",
+ "shell.execute_reply": "2024-07-01T15:07:25.140651Z"
},
"scrolled": true
},
@@ -242,10 +242,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:02.101009Z",
- "iopub.status.busy": "2024-06-28T15:38:02.100809Z",
- "iopub.status.idle": "2024-06-28T15:38:02.106483Z",
- "shell.execute_reply": "2024-06-28T15:38:02.106007Z"
+ "iopub.execute_input": "2024-07-01T15:07:25.144290Z",
+ "iopub.status.busy": "2024-07-01T15:07:25.143822Z",
+ "iopub.status.idle": "2024-07-01T15:07:25.149165Z",
+ "shell.execute_reply": "2024-07-01T15:07:25.148739Z"
}
},
"outputs": [
@@ -497,10 +497,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:02.108382Z",
- "iopub.status.busy": "2024-06-28T15:38:02.108204Z",
- "iopub.status.idle": "2024-06-28T15:38:02.112634Z",
- "shell.execute_reply": "2024-06-28T15:38:02.112164Z"
+ "iopub.execute_input": "2024-07-01T15:07:25.151192Z",
+ "iopub.status.busy": "2024-07-01T15:07:25.150870Z",
+ "iopub.status.idle": "2024-07-01T15:07:25.154548Z",
+ "shell.execute_reply": "2024-07-01T15:07:25.154108Z"
}
},
"outputs": [
@@ -557,10 +557,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:02.114751Z",
- "iopub.status.busy": "2024-06-28T15:38:02.114411Z",
- "iopub.status.idle": "2024-06-28T15:38:03.057670Z",
- "shell.execute_reply": "2024-06-28T15:38:03.056990Z"
+ "iopub.execute_input": "2024-07-01T15:07:25.156514Z",
+ "iopub.status.busy": "2024-07-01T15:07:25.156335Z",
+ "iopub.status.idle": "2024-07-01T15:07:26.038062Z",
+ "shell.execute_reply": "2024-07-01T15:07:26.037425Z"
}
},
"outputs": [
@@ -616,10 +616,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:03.060460Z",
- "iopub.status.busy": "2024-06-28T15:38:03.059841Z",
- "iopub.status.idle": "2024-06-28T15:38:03.286800Z",
- "shell.execute_reply": "2024-06-28T15:38:03.286263Z"
+ "iopub.execute_input": "2024-07-01T15:07:26.040299Z",
+ "iopub.status.busy": "2024-07-01T15:07:26.040059Z",
+ "iopub.status.idle": "2024-07-01T15:07:26.281733Z",
+ "shell.execute_reply": "2024-07-01T15:07:26.281238Z"
}
},
"outputs": [
@@ -660,10 +660,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:03.289126Z",
- "iopub.status.busy": "2024-06-28T15:38:03.288767Z",
- "iopub.status.idle": "2024-06-28T15:38:03.293054Z",
- "shell.execute_reply": "2024-06-28T15:38:03.292569Z"
+ "iopub.execute_input": "2024-07-01T15:07:26.284005Z",
+ "iopub.status.busy": "2024-07-01T15:07:26.283674Z",
+ "iopub.status.idle": "2024-07-01T15:07:26.287739Z",
+ "shell.execute_reply": "2024-07-01T15:07:26.287303Z"
}
},
"outputs": [
@@ -700,10 +700,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:03.295148Z",
- "iopub.status.busy": "2024-06-28T15:38:03.294835Z",
- "iopub.status.idle": "2024-06-28T15:38:03.761009Z",
- "shell.execute_reply": "2024-06-28T15:38:03.760370Z"
+ "iopub.execute_input": "2024-07-01T15:07:26.289717Z",
+ "iopub.status.busy": "2024-07-01T15:07:26.289415Z",
+ "iopub.status.idle": "2024-07-01T15:07:26.747330Z",
+ "shell.execute_reply": "2024-07-01T15:07:26.746844Z"
}
},
"outputs": [
@@ -762,10 +762,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:03.764257Z",
- "iopub.status.busy": "2024-06-28T15:38:03.763873Z",
- "iopub.status.idle": "2024-06-28T15:38:04.100471Z",
- "shell.execute_reply": "2024-06-28T15:38:04.099895Z"
+ "iopub.execute_input": "2024-07-01T15:07:26.749504Z",
+ "iopub.status.busy": "2024-07-01T15:07:26.749157Z",
+ "iopub.status.idle": "2024-07-01T15:07:27.049969Z",
+ "shell.execute_reply": "2024-07-01T15:07:27.049390Z"
}
},
"outputs": [
@@ -812,10 +812,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:04.103987Z",
- "iopub.status.busy": "2024-06-28T15:38:04.103475Z",
- "iopub.status.idle": "2024-06-28T15:38:04.445171Z",
- "shell.execute_reply": "2024-06-28T15:38:04.444553Z"
+ "iopub.execute_input": "2024-07-01T15:07:27.052016Z",
+ "iopub.status.busy": "2024-07-01T15:07:27.051834Z",
+ "iopub.status.idle": "2024-07-01T15:07:27.386953Z",
+ "shell.execute_reply": "2024-07-01T15:07:27.386354Z"
}
},
"outputs": [
@@ -862,10 +862,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:04.448311Z",
- "iopub.status.busy": "2024-06-28T15:38:04.447953Z",
- "iopub.status.idle": "2024-06-28T15:38:04.862739Z",
- "shell.execute_reply": "2024-06-28T15:38:04.862176Z"
+ "iopub.execute_input": "2024-07-01T15:07:27.390094Z",
+ "iopub.status.busy": "2024-07-01T15:07:27.389720Z",
+ "iopub.status.idle": "2024-07-01T15:07:27.826810Z",
+ "shell.execute_reply": "2024-07-01T15:07:27.826201Z"
}
},
"outputs": [
@@ -925,10 +925,10 @@
"id": "7e770d23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:04.867087Z",
- "iopub.status.busy": "2024-06-28T15:38:04.866704Z",
- "iopub.status.idle": "2024-06-28T15:38:05.319876Z",
- "shell.execute_reply": "2024-06-28T15:38:05.319267Z"
+ "iopub.execute_input": "2024-07-01T15:07:27.830888Z",
+ "iopub.status.busy": "2024-07-01T15:07:27.830547Z",
+ "iopub.status.idle": "2024-07-01T15:07:28.275927Z",
+ "shell.execute_reply": "2024-07-01T15:07:28.275306Z"
}
},
"outputs": [
@@ -971,10 +971,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:05.322789Z",
- "iopub.status.busy": "2024-06-28T15:38:05.322420Z",
- "iopub.status.idle": "2024-06-28T15:38:05.539025Z",
- "shell.execute_reply": "2024-06-28T15:38:05.538465Z"
+ "iopub.execute_input": "2024-07-01T15:07:28.278580Z",
+ "iopub.status.busy": "2024-07-01T15:07:28.278386Z",
+ "iopub.status.idle": "2024-07-01T15:07:28.478171Z",
+ "shell.execute_reply": "2024-07-01T15:07:28.477537Z"
}
},
"outputs": [
@@ -1017,10 +1017,10 @@
"id": "0302818a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:05.541213Z",
- "iopub.status.busy": "2024-06-28T15:38:05.541026Z",
- "iopub.status.idle": "2024-06-28T15:38:05.743328Z",
- "shell.execute_reply": "2024-06-28T15:38:05.742791Z"
+ "iopub.execute_input": "2024-07-01T15:07:28.481029Z",
+ "iopub.status.busy": "2024-07-01T15:07:28.480514Z",
+ "iopub.status.idle": "2024-07-01T15:07:28.679630Z",
+ "shell.execute_reply": "2024-07-01T15:07:28.679032Z"
}
},
"outputs": [
@@ -1067,10 +1067,10 @@
"id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:05.745675Z",
- "iopub.status.busy": "2024-06-28T15:38:05.745487Z",
- "iopub.status.idle": "2024-06-28T15:38:05.748456Z",
- "shell.execute_reply": "2024-06-28T15:38:05.748001Z"
+ "iopub.execute_input": "2024-07-01T15:07:28.681815Z",
+ "iopub.status.busy": "2024-07-01T15:07:28.681633Z",
+ "iopub.status.idle": "2024-07-01T15:07:28.684760Z",
+ "shell.execute_reply": "2024-07-01T15:07:28.684215Z"
}
},
"outputs": [],
@@ -1090,10 +1090,10 @@
"id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:05.750297Z",
- "iopub.status.busy": "2024-06-28T15:38:05.750127Z",
- "iopub.status.idle": "2024-06-28T15:38:06.726754Z",
- "shell.execute_reply": "2024-06-28T15:38:06.726181Z"
+ "iopub.execute_input": "2024-07-01T15:07:28.686736Z",
+ "iopub.status.busy": "2024-07-01T15:07:28.686404Z",
+ "iopub.status.idle": "2024-07-01T15:07:29.599883Z",
+ "shell.execute_reply": "2024-07-01T15:07:29.599378Z"
}
},
"outputs": [
@@ -1172,10 +1172,10 @@
"id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:06.729839Z",
- "iopub.status.busy": "2024-06-28T15:38:06.729460Z",
- "iopub.status.idle": "2024-06-28T15:38:06.872733Z",
- "shell.execute_reply": "2024-06-28T15:38:06.872148Z"
+ "iopub.execute_input": "2024-07-01T15:07:29.602491Z",
+ "iopub.status.busy": "2024-07-01T15:07:29.602156Z",
+ "iopub.status.idle": "2024-07-01T15:07:29.724845Z",
+ "shell.execute_reply": "2024-07-01T15:07:29.724400Z"
}
},
"outputs": [
@@ -1214,10 +1214,10 @@
"id": "59d7ee39-3785-434b-8680-9133014851cd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:06.875073Z",
- "iopub.status.busy": "2024-06-28T15:38:06.874723Z",
- "iopub.status.idle": "2024-06-28T15:38:07.017945Z",
- "shell.execute_reply": "2024-06-28T15:38:07.017419Z"
+ "iopub.execute_input": "2024-07-01T15:07:29.727049Z",
+ "iopub.status.busy": "2024-07-01T15:07:29.726723Z",
+ "iopub.status.idle": "2024-07-01T15:07:29.857120Z",
+ "shell.execute_reply": "2024-07-01T15:07:29.856610Z"
}
},
"outputs": [],
@@ -1266,10 +1266,10 @@
"id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:07.020497Z",
- "iopub.status.busy": "2024-06-28T15:38:07.020139Z",
- "iopub.status.idle": "2024-06-28T15:38:07.772705Z",
- "shell.execute_reply": "2024-06-28T15:38:07.772059Z"
+ "iopub.execute_input": "2024-07-01T15:07:29.859645Z",
+ "iopub.status.busy": "2024-07-01T15:07:29.859295Z",
+ "iopub.status.idle": "2024-07-01T15:07:30.599850Z",
+ "shell.execute_reply": "2024-07-01T15:07:30.599307Z"
}
},
"outputs": [
@@ -1351,10 +1351,10 @@
"id": "8ce74938",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:07.775031Z",
- "iopub.status.busy": "2024-06-28T15:38:07.774718Z",
- "iopub.status.idle": "2024-06-28T15:38:07.778431Z",
- "shell.execute_reply": "2024-06-28T15:38:07.777967Z"
+ "iopub.execute_input": "2024-07-01T15:07:30.602022Z",
+ "iopub.status.busy": "2024-07-01T15:07:30.601697Z",
+ "iopub.status.idle": "2024-07-01T15:07:30.605345Z",
+ "shell.execute_reply": "2024-07-01T15:07:30.604899Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
index 6169cfa22..de1ca9206 100644
--- a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
@@ -109,10 +109,10 @@
"id": "2bbebfc8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:10.079179Z",
- "iopub.status.busy": "2024-06-28T15:38:10.078994Z",
- "iopub.status.idle": "2024-06-28T15:38:12.976778Z",
- "shell.execute_reply": "2024-06-28T15:38:12.976174Z"
+ "iopub.execute_input": "2024-07-01T15:07:32.630513Z",
+ "iopub.status.busy": "2024-07-01T15:07:32.630022Z",
+ "iopub.status.idle": "2024-07-01T15:07:35.339624Z",
+ "shell.execute_reply": "2024-07-01T15:07:35.338990Z"
},
"nbsphinx": "hidden"
},
@@ -125,7 +125,7 @@
"dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -159,10 +159,10 @@
"id": "4396f544",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:12.979625Z",
- "iopub.status.busy": "2024-06-28T15:38:12.979083Z",
- "iopub.status.idle": "2024-06-28T15:38:13.326543Z",
- "shell.execute_reply": "2024-06-28T15:38:13.326026Z"
+ "iopub.execute_input": "2024-07-01T15:07:35.342314Z",
+ "iopub.status.busy": "2024-07-01T15:07:35.341942Z",
+ "iopub.status.idle": "2024-07-01T15:07:35.677074Z",
+ "shell.execute_reply": "2024-07-01T15:07:35.676543Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "3792f82e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:38:13.329227Z",
- "iopub.status.busy": "2024-06-28T15:38:13.328804Z",
- "iopub.status.idle": "2024-06-28T15:38:13.332930Z",
- "shell.execute_reply": "2024-06-28T15:38:13.332475Z"
+ "iopub.execute_input": "2024-07-01T15:07:35.679709Z",
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@@ -225,10 +225,10 @@
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}
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@@ -252,7 +252,7 @@
"output_type": "stream",
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"\r",
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+ " 0%| | 786432/170498071 [00:00<00:21, 7820176.68it/s]"
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+ " 3%|▎ | 4980736/170498071 [00:00<00:05, 27792797.45it/s]"
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+ " 6%|▋ | 10944512/170498071 [00:00<00:03, 42298222.53it/s]"
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+ " 10%|▉ | 16449536/170498071 [00:00<00:03, 47151386.97it/s]"
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+ " 12%|█▏ | 21168128/170498071 [00:00<00:03, 41939697.62it/s]"
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@@ -292,7 +292,7 @@
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@@ -308,7 +308,7 @@
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@@ -316,7 +316,7 @@
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@@ -324,7 +324,7 @@
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+ " 23%|██▎ | 38600704/170498071 [00:01<00:04, 27686377.59it/s]"
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@@ -332,7 +332,7 @@
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@@ -340,7 +340,7 @@
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@@ -348,7 +348,7 @@
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@@ -356,7 +356,7 @@
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@@ -364,7 +364,7 @@
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@@ -372,7 +372,7 @@
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@@ -388,7 +388,183 @@
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{
@@ -506,10 +682,10 @@
"id": "9b64e0aa",
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@@ -560,10 +736,10 @@
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@@ -596,10 +772,10 @@
"id": "41e5cb6b",
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@@ -637,10 +813,10 @@
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@@ -663,17 +839,17 @@
"id": "85a58d41",
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+ "shell.execute_reply": "2024-07-01T15:07:56.035038Z"
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{
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+ "model_id": "6e0af51d1d7c41f6b28e94e107a2e2dd",
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@@ -732,10 +908,10 @@
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@@ -779,10 +955,10 @@
"id": "089d5860",
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@@ -818,10 +994,10 @@
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@@ -871,10 +1047,10 @@
"id": "e9dff81b",
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@@ -922,10 +1098,10 @@
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@@ -981,10 +1157,10 @@
"id": "40fed4ef",
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@@ -1005,10 +1181,10 @@
"id": "89f9db72",
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@@ -1045,10 +1221,10 @@
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@@ -1079,10 +1255,10 @@
"id": "e110fc4b",
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- "shell.execute_reply": "2024-06-28T15:38:48.818936Z"
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}
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@@ -1096,10 +1272,10 @@
"id": "85b60cbf",
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- "shell.execute_reply": "2024-06-28T15:38:48.824452Z"
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+ "shell.execute_reply": "2024-07-01T15:08:12.597815Z"
}
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"outputs": [],
@@ -1121,10 +1297,10 @@
"id": "17f96fa6",
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- "shell.execute_reply": "2024-06-28T15:38:48.834539Z"
+ "iopub.execute_input": "2024-07-01T15:08:12.600173Z",
+ "iopub.status.busy": "2024-07-01T15:08:12.599856Z",
+ "iopub.status.idle": "2024-07-01T15:08:12.608170Z",
+ "shell.execute_reply": "2024-07-01T15:08:12.607771Z"
},
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},
@@ -1169,31 +1345,30 @@
"widgets": {
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"model_module_version": "2.0.0",
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+ "model_name": "HTMLModel",
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- "_model_name": "HBoxModel",
+ "_model_name": "HTMLModel",
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- "children": [
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- "layout": "IPY_MODEL_3c8bf8f7c6de41b08aca8334534c44d5",
+ "_view_name": "HTMLView",
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+ "description_allow_html": false,
+ "layout": "IPY_MODEL_c995655b1f814ffb9d408cda2ffbc566",
+ "placeholder": "",
+ "style": "IPY_MODEL_f100e8e508354e3a998f09e41481fe4a",
"tabbable": null,
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+ "value": " 102M/102M [00:00<00:00, 311MB/s]"
}
},
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@@ -1209,7 +1384,31 @@
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}
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+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
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+ "box_style": "",
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+ "IPY_MODEL_beaa49c5ac66403896b3c555d2a06c91",
+ "IPY_MODEL_2727be2db0d2497f9fb52dc8697095a2"
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@@ -1262,30 +1461,7 @@
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- "layout": "IPY_MODEL_5b4e0dce4be640c58c64a1d670de2e81",
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- "tabbable": null,
- "tooltip": null,
- "value": " 102M/102M [00:00<00:00, 143MB/s]"
- }
- },
- "3c8bf8f7c6de41b08aca8334534c44d5": {
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@@ -1338,7 +1514,56 @@
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}
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+ "tabbable": null,
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+ "value": "model.safetensors: 100%"
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+ "model_name": "FloatProgressModel",
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+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_ce770c3a1d5d42a98a4d28d04bc1c7d7",
+ "max": 102469840.0,
+ "min": 0.0,
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+ "c995655b1f814ffb9d408cda2ffbc566": {
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@@ -1391,51 +1616,7 @@
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- "layout": "IPY_MODEL_2d2e5be95459464d9398638bc99b5ac9",
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- "bf29183e5e5f4369a7a8bb198f9346b3": {
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@@ -1488,30 +1669,25 @@
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+ "_view_name": "StyleView",
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- "f5a57583e54c48e280d535fd0d90277f": {
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diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
index 8d011fee3..46926446f 100644
--- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
@@ -102,10 +102,10 @@
"id": "2e1af7d8",
"metadata": {
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- "shell.execute_reply": "2024-06-28T15:38:54.203645Z"
+ "iopub.execute_input": "2024-07-01T15:08:16.815662Z",
+ "iopub.status.busy": "2024-07-01T15:08:16.815212Z",
+ "iopub.status.idle": "2024-07-01T15:08:18.061264Z",
+ "shell.execute_reply": "2024-07-01T15:08:18.060688Z"
},
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},
@@ -116,7 +116,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -142,10 +142,10 @@
"id": "4fb10b8f",
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- "shell.execute_reply": "2024-06-28T15:38:54.224435Z"
+ "iopub.execute_input": "2024-07-01T15:08:18.064012Z",
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+ "shell.execute_reply": "2024-07-01T15:08:18.080744Z"
}
},
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@@ -164,10 +164,10 @@
"id": "284dc264",
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- "shell.execute_reply": "2024-06-28T15:38:54.230003Z"
+ "iopub.execute_input": "2024-07-01T15:08:18.083492Z",
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},
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},
@@ -198,10 +198,10 @@
"id": "0f7450db",
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- "iopub.status.idle": "2024-06-28T15:38:54.268547Z",
- "shell.execute_reply": "2024-06-28T15:38:54.268041Z"
+ "iopub.execute_input": "2024-07-01T15:08:18.088481Z",
+ "iopub.status.busy": "2024-07-01T15:08:18.088156Z",
+ "iopub.status.idle": "2024-07-01T15:08:18.174903Z",
+ "shell.execute_reply": "2024-07-01T15:08:18.174413Z"
}
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@@ -374,10 +374,10 @@
"id": "55513fed",
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- "shell.execute_reply": "2024-06-28T15:38:54.453953Z"
+ "iopub.execute_input": "2024-07-01T15:08:18.177216Z",
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+ "shell.execute_reply": "2024-07-01T15:08:18.362759Z"
},
"nbsphinx": "hidden"
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@@ -417,10 +417,10 @@
"id": "df5a0f59",
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- "shell.execute_reply": "2024-06-28T15:38:54.702725Z"
+ "iopub.execute_input": "2024-07-01T15:08:18.366181Z",
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@@ -456,10 +456,10 @@
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- "shell.execute_reply": "2024-06-28T15:38:54.709113Z"
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@@ -477,10 +477,10 @@
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- "shell.execute_reply": "2024-06-28T15:38:54.716684Z"
+ "iopub.execute_input": "2024-07-01T15:08:18.621922Z",
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@@ -527,10 +527,10 @@
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- "shell.execute_reply": "2024-06-28T15:38:54.720825Z"
+ "iopub.execute_input": "2024-07-01T15:08:18.631920Z",
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@@ -545,10 +545,10 @@
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- "shell.execute_reply": "2024-06-28T15:39:03.561823Z"
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@@ -572,10 +572,10 @@
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- "iopub.status.idle": "2024-06-28T15:39:03.572338Z",
- "shell.execute_reply": "2024-06-28T15:39:03.571880Z"
+ "iopub.execute_input": "2024-07-01T15:08:27.683915Z",
+ "iopub.status.busy": "2024-07-01T15:08:27.683447Z",
+ "iopub.status.idle": "2024-07-01T15:08:27.691061Z",
+ "shell.execute_reply": "2024-07-01T15:08:27.690544Z"
}
},
"outputs": [
@@ -678,10 +678,10 @@
"id": "f7385336",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:03.574497Z",
- "iopub.status.busy": "2024-06-28T15:39:03.574149Z",
- "iopub.status.idle": "2024-06-28T15:39:03.577840Z",
- "shell.execute_reply": "2024-06-28T15:39:03.577381Z"
+ "iopub.execute_input": "2024-07-01T15:08:27.693260Z",
+ "iopub.status.busy": "2024-07-01T15:08:27.692915Z",
+ "iopub.status.idle": "2024-07-01T15:08:27.696508Z",
+ "shell.execute_reply": "2024-07-01T15:08:27.696074Z"
}
},
"outputs": [],
@@ -696,10 +696,10 @@
"id": "59fc3091",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:03.579710Z",
- "iopub.status.busy": "2024-06-28T15:39:03.579448Z",
- "iopub.status.idle": "2024-06-28T15:39:03.582873Z",
- "shell.execute_reply": "2024-06-28T15:39:03.582423Z"
+ "iopub.execute_input": "2024-07-01T15:08:27.698591Z",
+ "iopub.status.busy": "2024-07-01T15:08:27.698265Z",
+ "iopub.status.idle": "2024-07-01T15:08:27.701394Z",
+ "shell.execute_reply": "2024-07-01T15:08:27.700844Z"
}
},
"outputs": [
@@ -734,10 +734,10 @@
"id": "00949977",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:03.584887Z",
- "iopub.status.busy": "2024-06-28T15:39:03.584561Z",
- "iopub.status.idle": "2024-06-28T15:39:03.587419Z",
- "shell.execute_reply": "2024-06-28T15:39:03.586979Z"
+ "iopub.execute_input": "2024-07-01T15:08:27.703468Z",
+ "iopub.status.busy": "2024-07-01T15:08:27.703137Z",
+ "iopub.status.idle": "2024-07-01T15:08:27.706217Z",
+ "shell.execute_reply": "2024-07-01T15:08:27.705750Z"
}
},
"outputs": [],
@@ -756,10 +756,10 @@
"id": "b6c1ae3a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:03.589440Z",
- "iopub.status.busy": "2024-06-28T15:39:03.589127Z",
- "iopub.status.idle": "2024-06-28T15:39:03.597072Z",
- "shell.execute_reply": "2024-06-28T15:39:03.596559Z"
+ "iopub.execute_input": "2024-07-01T15:08:27.708232Z",
+ "iopub.status.busy": "2024-07-01T15:08:27.707899Z",
+ "iopub.status.idle": "2024-07-01T15:08:27.715999Z",
+ "shell.execute_reply": "2024-07-01T15:08:27.715525Z"
}
},
"outputs": [
@@ -883,10 +883,10 @@
"id": "9131d82d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:03.599177Z",
- "iopub.status.busy": "2024-06-28T15:39:03.598840Z",
- "iopub.status.idle": "2024-06-28T15:39:03.601388Z",
- "shell.execute_reply": "2024-06-28T15:39:03.600963Z"
+ "iopub.execute_input": "2024-07-01T15:08:27.718018Z",
+ "iopub.status.busy": "2024-07-01T15:08:27.717680Z",
+ "iopub.status.idle": "2024-07-01T15:08:27.720242Z",
+ "shell.execute_reply": "2024-07-01T15:08:27.719798Z"
},
"nbsphinx": "hidden"
},
@@ -921,10 +921,10 @@
"id": "31c704e7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:03.603502Z",
- "iopub.status.busy": "2024-06-28T15:39:03.603179Z",
- "iopub.status.idle": "2024-06-28T15:39:03.723060Z",
- "shell.execute_reply": "2024-06-28T15:39:03.722461Z"
+ "iopub.execute_input": "2024-07-01T15:08:27.722277Z",
+ "iopub.status.busy": "2024-07-01T15:08:27.721939Z",
+ "iopub.status.idle": "2024-07-01T15:08:27.850295Z",
+ "shell.execute_reply": "2024-07-01T15:08:27.849694Z"
}
},
"outputs": [
@@ -963,10 +963,10 @@
"id": "0bcc43db",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:03.725504Z",
- "iopub.status.busy": "2024-06-28T15:39:03.725028Z",
- "iopub.status.idle": "2024-06-28T15:39:03.827464Z",
- "shell.execute_reply": "2024-06-28T15:39:03.826856Z"
+ "iopub.execute_input": "2024-07-01T15:08:27.852484Z",
+ "iopub.status.busy": "2024-07-01T15:08:27.852300Z",
+ "iopub.status.idle": "2024-07-01T15:08:27.955847Z",
+ "shell.execute_reply": "2024-07-01T15:08:27.955257Z"
}
},
"outputs": [
@@ -1022,10 +1022,10 @@
"id": "7021bd68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:03.829919Z",
- "iopub.status.busy": "2024-06-28T15:39:03.829592Z",
- "iopub.status.idle": "2024-06-28T15:39:04.313676Z",
- "shell.execute_reply": "2024-06-28T15:39:04.313059Z"
+ "iopub.execute_input": "2024-07-01T15:08:27.958252Z",
+ "iopub.status.busy": "2024-07-01T15:08:27.957880Z",
+ "iopub.status.idle": "2024-07-01T15:08:28.451750Z",
+ "shell.execute_reply": "2024-07-01T15:08:28.451203Z"
}
},
"outputs": [],
@@ -1041,10 +1041,10 @@
"id": "d49c990b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:04.316205Z",
- "iopub.status.busy": "2024-06-28T15:39:04.316016Z",
- "iopub.status.idle": "2024-06-28T15:39:04.389000Z",
- "shell.execute_reply": "2024-06-28T15:39:04.388436Z"
+ "iopub.execute_input": "2024-07-01T15:08:28.454335Z",
+ "iopub.status.busy": "2024-07-01T15:08:28.454151Z",
+ "iopub.status.idle": "2024-07-01T15:08:28.527356Z",
+ "shell.execute_reply": "2024-07-01T15:08:28.526736Z"
}
},
"outputs": [
@@ -1079,10 +1079,10 @@
"id": "dbab6fb3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:04.391365Z",
- "iopub.status.busy": "2024-06-28T15:39:04.390985Z",
- "iopub.status.idle": "2024-06-28T15:39:04.399547Z",
- "shell.execute_reply": "2024-06-28T15:39:04.399087Z"
+ "iopub.execute_input": "2024-07-01T15:08:28.529697Z",
+ "iopub.status.busy": "2024-07-01T15:08:28.529341Z",
+ "iopub.status.idle": "2024-07-01T15:08:28.538428Z",
+ "shell.execute_reply": "2024-07-01T15:08:28.537958Z"
}
},
"outputs": [
@@ -1189,10 +1189,10 @@
"id": "5b39b8b5",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:04.401759Z",
- "iopub.status.busy": "2024-06-28T15:39:04.401238Z",
- "iopub.status.idle": "2024-06-28T15:39:04.404123Z",
- "shell.execute_reply": "2024-06-28T15:39:04.403592Z"
+ "iopub.execute_input": "2024-07-01T15:08:28.540454Z",
+ "iopub.status.busy": "2024-07-01T15:08:28.540269Z",
+ "iopub.status.idle": "2024-07-01T15:08:28.542883Z",
+ "shell.execute_reply": "2024-07-01T15:08:28.542447Z"
},
"nbsphinx": "hidden"
},
@@ -1217,10 +1217,10 @@
"id": "df06525b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:04.406104Z",
- "iopub.status.busy": "2024-06-28T15:39:04.405929Z",
- "iopub.status.idle": "2024-06-28T15:39:10.119031Z",
- "shell.execute_reply": "2024-06-28T15:39:10.118402Z"
+ "iopub.execute_input": "2024-07-01T15:08:28.544877Z",
+ "iopub.status.busy": "2024-07-01T15:08:28.544701Z",
+ "iopub.status.idle": "2024-07-01T15:08:33.972038Z",
+ "shell.execute_reply": "2024-07-01T15:08:33.971430Z"
}
},
"outputs": [
@@ -1264,10 +1264,10 @@
"id": "05282559",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:10.121458Z",
- "iopub.status.busy": "2024-06-28T15:39:10.121040Z",
- "iopub.status.idle": "2024-06-28T15:39:10.130156Z",
- "shell.execute_reply": "2024-06-28T15:39:10.129695Z"
+ "iopub.execute_input": "2024-07-01T15:08:33.974153Z",
+ "iopub.status.busy": "2024-07-01T15:08:33.973956Z",
+ "iopub.status.idle": "2024-07-01T15:08:33.982773Z",
+ "shell.execute_reply": "2024-07-01T15:08:33.982320Z"
}
},
"outputs": [
@@ -1376,10 +1376,10 @@
"id": "95531cda",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:10.132289Z",
- "iopub.status.busy": "2024-06-28T15:39:10.131950Z",
- "iopub.status.idle": "2024-06-28T15:39:10.201214Z",
- "shell.execute_reply": "2024-06-28T15:39:10.200720Z"
+ "iopub.execute_input": "2024-07-01T15:08:33.984722Z",
+ "iopub.status.busy": "2024-07-01T15:08:33.984546Z",
+ "iopub.status.idle": "2024-07-01T15:08:34.049986Z",
+ "shell.execute_reply": "2024-07-01T15:08:34.049478Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
index 1b5222eab..3b1c6435d 100644
--- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
@@ -61,10 +61,10 @@
"id": "ae8a08e0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:13.402150Z",
- "iopub.status.busy": "2024-06-28T15:39:13.401954Z",
- "iopub.status.idle": "2024-06-28T15:39:14.646183Z",
- "shell.execute_reply": "2024-06-28T15:39:14.645441Z"
+ "iopub.execute_input": "2024-07-01T15:08:37.513049Z",
+ "iopub.status.busy": "2024-07-01T15:08:37.512824Z",
+ "iopub.status.idle": "2024-07-01T15:08:39.014630Z",
+ "shell.execute_reply": "2024-07-01T15:08:39.013915Z"
}
},
"outputs": [],
@@ -79,10 +79,10 @@
"id": "58fd4c55",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:14.649149Z",
- "iopub.status.busy": "2024-06-28T15:39:14.648692Z",
- "iopub.status.idle": "2024-06-28T15:39:50.257887Z",
- "shell.execute_reply": "2024-06-28T15:39:50.257232Z"
+ "iopub.execute_input": "2024-07-01T15:08:39.017371Z",
+ "iopub.status.busy": "2024-07-01T15:08:39.017127Z",
+ "iopub.status.idle": "2024-07-01T15:09:39.584116Z",
+ "shell.execute_reply": "2024-07-01T15:09:39.583459Z"
}
},
"outputs": [],
@@ -97,10 +97,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:50.260572Z",
- "iopub.status.busy": "2024-06-28T15:39:50.260205Z",
- "iopub.status.idle": "2024-06-28T15:39:51.486151Z",
- "shell.execute_reply": "2024-06-28T15:39:51.485480Z"
+ "iopub.execute_input": "2024-07-01T15:09:39.586690Z",
+ "iopub.status.busy": "2024-07-01T15:09:39.586340Z",
+ "iopub.status.idle": "2024-07-01T15:09:40.720146Z",
+ "shell.execute_reply": "2024-07-01T15:09:40.719576Z"
},
"nbsphinx": "hidden"
},
@@ -111,7 +111,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -137,10 +137,10 @@
"id": "a1349304",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:51.489186Z",
- "iopub.status.busy": "2024-06-28T15:39:51.488560Z",
- "iopub.status.idle": "2024-06-28T15:39:51.492413Z",
- "shell.execute_reply": "2024-06-28T15:39:51.491891Z"
+ "iopub.execute_input": "2024-07-01T15:09:40.722658Z",
+ "iopub.status.busy": "2024-07-01T15:09:40.722386Z",
+ "iopub.status.idle": "2024-07-01T15:09:40.725657Z",
+ "shell.execute_reply": "2024-07-01T15:09:40.725218Z"
}
},
"outputs": [],
@@ -203,10 +203,10 @@
"id": "07dc5678",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:51.494943Z",
- "iopub.status.busy": "2024-06-28T15:39:51.494455Z",
- "iopub.status.idle": "2024-06-28T15:39:51.498706Z",
- "shell.execute_reply": "2024-06-28T15:39:51.498166Z"
+ "iopub.execute_input": "2024-07-01T15:09:40.727650Z",
+ "iopub.status.busy": "2024-07-01T15:09:40.727470Z",
+ "iopub.status.idle": "2024-07-01T15:09:40.731254Z",
+ "shell.execute_reply": "2024-07-01T15:09:40.730747Z"
}
},
"outputs": [
@@ -247,10 +247,10 @@
"id": "25ebe22a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:51.501145Z",
- "iopub.status.busy": "2024-06-28T15:39:51.500786Z",
- "iopub.status.idle": "2024-06-28T15:39:51.504649Z",
- "shell.execute_reply": "2024-06-28T15:39:51.504101Z"
+ "iopub.execute_input": "2024-07-01T15:09:40.733340Z",
+ "iopub.status.busy": "2024-07-01T15:09:40.733016Z",
+ "iopub.status.idle": "2024-07-01T15:09:40.736638Z",
+ "shell.execute_reply": "2024-07-01T15:09:40.736162Z"
}
},
"outputs": [
@@ -290,10 +290,10 @@
"id": "3faedea9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:51.506798Z",
- "iopub.status.busy": "2024-06-28T15:39:51.506520Z",
- "iopub.status.idle": "2024-06-28T15:39:51.509682Z",
- "shell.execute_reply": "2024-06-28T15:39:51.509112Z"
+ "iopub.execute_input": "2024-07-01T15:09:40.738709Z",
+ "iopub.status.busy": "2024-07-01T15:09:40.738283Z",
+ "iopub.status.idle": "2024-07-01T15:09:40.741139Z",
+ "shell.execute_reply": "2024-07-01T15:09:40.740716Z"
}
},
"outputs": [],
@@ -333,17 +333,17 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:39:51.511926Z",
- "iopub.status.busy": "2024-06-28T15:39:51.511586Z",
- "iopub.status.idle": "2024-06-28T15:40:25.616678Z",
- "shell.execute_reply": "2024-06-28T15:40:25.616008Z"
+ "iopub.execute_input": "2024-07-01T15:09:40.743089Z",
+ "iopub.status.busy": "2024-07-01T15:09:40.742768Z",
+ "iopub.status.idle": "2024-07-01T15:10:14.851046Z",
+ "shell.execute_reply": "2024-07-01T15:10:14.850360Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "0cdc6dee74c040a69b3494b8aab06e7a",
+ "model_id": "e93b88c996c44feeb3673439eaaea41d",
"version_major": 2,
"version_minor": 0
},
@@ -357,7 +357,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b11ab931e8144b04919807741d6347a6",
+ "model_id": "cae016dc953549ce807817682c42dc87",
"version_major": 2,
"version_minor": 0
},
@@ -400,10 +400,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:40:25.619646Z",
- "iopub.status.busy": "2024-06-28T15:40:25.619138Z",
- "iopub.status.idle": "2024-06-28T15:40:26.311249Z",
- "shell.execute_reply": "2024-06-28T15:40:26.310687Z"
+ "iopub.execute_input": "2024-07-01T15:10:14.853695Z",
+ "iopub.status.busy": "2024-07-01T15:10:14.853439Z",
+ "iopub.status.idle": "2024-07-01T15:10:15.523116Z",
+ "shell.execute_reply": "2024-07-01T15:10:15.522614Z"
}
},
"outputs": [
@@ -446,10 +446,10 @@
"id": "57fed473",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:40:26.313705Z",
- "iopub.status.busy": "2024-06-28T15:40:26.313226Z",
- "iopub.status.idle": "2024-06-28T15:40:29.212189Z",
- "shell.execute_reply": "2024-06-28T15:40:29.211650Z"
+ "iopub.execute_input": "2024-07-01T15:10:15.525482Z",
+ "iopub.status.busy": "2024-07-01T15:10:15.525022Z",
+ "iopub.status.idle": "2024-07-01T15:10:18.415066Z",
+ "shell.execute_reply": "2024-07-01T15:10:18.414464Z"
}
},
"outputs": [
@@ -519,17 +519,17 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:40:29.214460Z",
- "iopub.status.busy": "2024-06-28T15:40:29.214101Z",
- "iopub.status.idle": "2024-06-28T15:41:02.133359Z",
- "shell.execute_reply": "2024-06-28T15:41:02.132825Z"
+ "iopub.execute_input": "2024-07-01T15:10:18.417219Z",
+ "iopub.status.busy": "2024-07-01T15:10:18.417035Z",
+ "iopub.status.idle": "2024-07-01T15:10:50.808150Z",
+ "shell.execute_reply": "2024-07-01T15:10:50.807678Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f3c4b5bc26264823bfb0f6b3ace17d05",
+ "model_id": "731093637bac464aa707d2bcbb8b8fa8",
"version_major": 2,
"version_minor": 0
},
@@ -769,10 +769,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:41:02.135440Z",
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diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
index 153b10754..8650ebc00 100644
--- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
@@ -75,10 +75,10 @@
"id": "ae8a08e0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:41:31.234194Z",
- "iopub.status.busy": "2024-06-28T15:41:31.234011Z",
- "iopub.status.idle": "2024-06-28T15:41:32.436580Z",
- "shell.execute_reply": "2024-06-28T15:41:32.435971Z"
+ "iopub.execute_input": "2024-07-01T15:11:18.503218Z",
+ "iopub.status.busy": "2024-07-01T15:11:18.502735Z",
+ "iopub.status.idle": "2024-07-01T15:11:19.975527Z",
+ "shell.execute_reply": "2024-07-01T15:11:19.974839Z"
}
},
"outputs": [
@@ -86,7 +86,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-06-28 15:41:31-- https://data.deepai.org/conll2003.zip\r\n",
+ "--2024-07-01 15:11:18-- https://data.deepai.org/conll2003.zip\r\n",
"Resolving data.deepai.org (data.deepai.org)... "
]
},
@@ -94,9 +94,23 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "185.93.1.246, 2400:52e0:1a00::718:1\r\n",
- "Connecting to data.deepai.org (data.deepai.org)|185.93.1.246|:443... connected.\r\n",
- "HTTP request sent, awaiting response... 200 OK\r\n",
+ "169.150.236.98, 2400:52e0:1a00::871:1\r\n",
+ "Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "connected.\r\n",
+ "HTTP request sent, awaiting response... "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "200 OK\r\n",
"Length: 982975 (960K) [application/zip]\r\n",
"Saving to: ‘conll2003.zip’\r\n",
"\r\n",
@@ -109,9 +123,9 @@
"output_type": "stream",
"text": [
"\r",
- "conll2003.zip 100%[===================>] 959.94K 4.92MB/s in 0.2s \r\n",
+ "conll2003.zip 100%[===================>] 959.94K 5.71MB/s in 0.2s \r\n",
"\r\n",
- "2024-06-28 15:41:31 (4.92 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+ "2024-07-01 15:11:19 (5.71 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
"\r\n",
"mkdir: cannot create directory ‘data’: File exists\r\n"
]
@@ -131,15 +145,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-06-28 15:41:31-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
- "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.117.65, 52.217.130.65, 52.217.92.116, ...\r\n",
- "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.117.65|:443... connected.\r\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "--2024-07-01 15:11:19-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+ "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.28.244, 3.5.24.72, 52.217.13.252, ...\r\n",
+ "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.28.244|:443... connected.\r\n",
"HTTP request sent, awaiting response... "
]
},
@@ -160,7 +168,7 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 48%[========> ] 7.93M 37.9MB/s "
+ "pred_probs.npz 35%[======> ] 5.78M 28.9MB/s "
]
},
{
@@ -168,9 +176,9 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 100%[===================>] 16.26M 60.6MB/s in 0.3s \r\n",
+ "pred_probs.npz 100%[===================>] 16.26M 52.3MB/s in 0.3s \r\n",
"\r\n",
- "2024-06-28 15:41:32 (60.6 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+ "2024-07-01 15:11:19 (52.3 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
"\r\n"
]
}
@@ -187,10 +195,10 @@
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+ "iopub.execute_input": "2024-07-01T15:11:19.978352Z",
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@@ -201,7 +209,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -227,10 +235,10 @@
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- "shell.execute_reply": "2024-06-28T15:41:33.751495Z"
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@@ -280,10 +288,10 @@
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+ "iopub.execute_input": "2024-07-01T15:11:21.223694Z",
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+ "shell.execute_reply": "2024-07-01T15:11:21.225848Z"
},
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@@ -301,10 +309,10 @@
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- "iopub.status.idle": "2024-06-28T15:41:42.901434Z",
- "shell.execute_reply": "2024-06-28T15:41:42.900715Z"
+ "iopub.execute_input": "2024-07-01T15:11:21.228084Z",
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+ "shell.execute_reply": "2024-07-01T15:11:30.310211Z"
}
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@@ -378,10 +386,10 @@
"id": "202f1526",
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- "iopub.execute_input": "2024-06-28T15:41:42.904438Z",
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- "shell.execute_reply": "2024-06-28T15:41:42.909908Z"
+ "iopub.execute_input": "2024-07-01T15:11:30.313310Z",
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+ "iopub.status.idle": "2024-07-01T15:11:30.318459Z",
+ "shell.execute_reply": "2024-07-01T15:11:30.318009Z"
},
"nbsphinx": "hidden"
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@@ -421,10 +429,10 @@
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- "iopub.execute_input": "2024-06-28T15:41:42.912924Z",
- "iopub.status.busy": "2024-06-28T15:41:42.912455Z",
- "iopub.status.idle": "2024-06-28T15:41:43.306159Z",
- "shell.execute_reply": "2024-06-28T15:41:43.305530Z"
+ "iopub.execute_input": "2024-07-01T15:11:30.320517Z",
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+ "shell.execute_reply": "2024-07-01T15:11:30.658770Z"
}
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@@ -461,10 +469,10 @@
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- "shell.execute_reply": "2024-06-28T15:41:43.312627Z"
+ "iopub.execute_input": "2024-07-01T15:11:30.661698Z",
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+ "iopub.status.idle": "2024-07-01T15:11:30.665925Z",
+ "shell.execute_reply": "2024-07-01T15:11:30.665448Z"
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@@ -536,10 +544,10 @@
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- "iopub.status.busy": "2024-06-28T15:41:43.314861Z",
- "iopub.status.idle": "2024-06-28T15:41:45.999753Z",
- "shell.execute_reply": "2024-06-28T15:41:45.998935Z"
+ "iopub.execute_input": "2024-07-01T15:11:30.667958Z",
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@@ -561,10 +569,10 @@
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- "shell.execute_reply": "2024-06-28T15:41:46.006164Z"
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@@ -600,10 +608,10 @@
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- "shell.execute_reply": "2024-06-28T15:41:46.013387Z"
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@@ -781,10 +789,10 @@
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- "shell.execute_reply": "2024-06-28T15:41:46.043105Z"
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@@ -886,10 +894,10 @@
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- "iopub.status.idle": "2024-06-28T15:41:46.050758Z",
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@@ -963,10 +971,10 @@
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- "shell.execute_reply": "2024-06-28T15:41:47.505719Z"
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@@ -1138,10 +1146,10 @@
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Binary files a/master/.doctrees/tutorials/clean_learning/text.doctree and b/master/.doctrees/tutorials/clean_learning/text.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/audio.doctree b/master/.doctrees/tutorials/datalab/audio.doctree
index a709b1c36..fd49abec4 100644
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diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree
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diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 90833cddf..6bd0ac215 100644
--- a/master/_sources/tutorials/clean_learning/tabular.ipynb
+++ b/master/_sources/tutorials/clean_learning/tabular.ipynb
@@ -120,7 +120,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/clean_learning/text.ipynb b/master/_sources/tutorials/clean_learning/text.ipynb
index 2276da1cf..d6b47ffef 100644
--- a/master/_sources/tutorials/clean_learning/text.ipynb
+++ b/master/_sources/tutorials/clean_learning/text.ipynb
@@ -129,7 +129,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/audio.ipynb b/master/_sources/tutorials/datalab/audio.ipynb
index ab6bd1460..d52a1f814 100644
--- a/master/_sources/tutorials/datalab/audio.ipynb
+++ b/master/_sources/tutorials/datalab/audio.ipynb
@@ -91,7 +91,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_advanced.ipynb b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
index 15e24ff27..744175c39 100644
--- a/master/_sources/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
@@ -87,7 +87,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
index e929f62b7..4b8205657 100644
--- a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
@@ -85,7 +85,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -758,9 +758,11 @@
"# Note: This cell is only for docs.cleanlab.ai, if running on local Jupyter or Colab, please ignore it.\n",
"from sklearn.metrics import roc_auc_score\n",
"\n",
- "issue_results = lab.get_issues(\"label\")\n",
- "outlier_results = lab.get_issues(\"outlier\")\n",
- "duplicate_results = lab.get_issues(\"near_duplicate\")\n",
+ "def precision_at_k(predicted_indices, true_indices, k):\n",
+ " return len(set(predicted_indices[:k]).intersection(set(true_indices))) / k\n",
+ "\n",
+ "def recall_at_k(predicted_indices, true_indices, k):\n",
+ " return len(set(predicted_indices[:k]).intersection(set(true_indices))) / len(true_indices)\n",
"\n",
"def jaccard_similarity(l1, l2):\n",
" s1 = set(l1)\n",
@@ -771,26 +773,40 @@
" return 0\n",
" return len(intersect_set) / len(union_set)\n",
"\n",
- "identified_label_issues_indices = issue_results[issue_results[\"is_label_issue\"] == True].index.tolist()\n",
+ "label_issues = lab.get_issues(\"label\")\n",
+ "predicted_label_issues_indices = (\n",
+ " label_issues.query(\"is_label_issue\").sort_values(\"label_score\").index.to_list()\n",
+ ")\n",
+ "predicted_label_issues_indices_by_score = (\n",
+ " label_issues.sort_values(\"label_score\").index.to_list()\n",
+ ")\n",
"label_issue_indices = np.where(y_train_idx != noisy_labels_idx)[0]\n",
"\n",
- "label_quality_scores = issue_results[\"label_score\"].tolist()\n",
+ "label_quality_scores = label_issues[\"label_score\"].tolist()\n",
"Z = (y_train_idx == noisy_labels_idx).astype(float).tolist()\n",
"\n",
- "identified_outlier_issues_indices = outlier_results[outlier_results[\"is_outlier_issue\"] == True].index.to_list()\n",
+ "predicted_outlier_issues_indices = (\n",
+ " lab.get_issues(\"outlier\").query(\"is_outlier_issue\").index.to_list()\n",
+ ")\n",
"outlier_issue_indices = list(range(125, 130+1))\n",
"exact_duplicate_idx = [index for index, elem in enumerate(X_train) if (elem == X_duplicate).all()][0]\n",
"if exact_duplicate_idx >= 125: # if the random index selected to create a duplicate >= 125, then the last point is also an outlier\n",
" outlier_issue_indices.append(131)\n",
- " \n",
- "identified_duplicate_issues_indices = duplicate_results[duplicate_results[\"is_near_duplicate_issue\"] == True].index.tolist()\n",
- "duplicate_issue_indices = [exact_duplicate_idx, 129, 130, 131]\n",
"\n",
+ "predicted_duplicate_issues_indices = (\n",
+ " lab.get_issues(\"near_duplicate\").query(\"is_near_duplicate_issue\").index.tolist()\n",
+ ")\n",
+ "duplicate_issue_indices = [exact_duplicate_idx, 129, 130, 131]\n",
"\n",
- "assert jaccard_similarity(identified_label_issues_indices, label_issue_indices) > 0.4\n",
+ "k = len(label_issue_indices)\n",
+ "assert precision_at_k(predicted_label_issues_indices, label_issue_indices, k) >= 0.75\n",
+ "assert recall_at_k(predicted_label_issues_indices, label_issue_indices, k) >= 0.75\n",
+ "assert precision_at_k(predicted_label_issues_indices_by_score, label_issue_indices, k) == 1.0\n",
+ "assert recall_at_k(predicted_label_issues_indices_by_score, label_issue_indices, k) == 1.0\n",
"assert roc_auc_score(Z, label_quality_scores) > 0.9\n",
- "assert jaccard_similarity(identified_outlier_issues_indices, outlier_issue_indices) > 0.9\n",
- "assert jaccard_similarity(identified_duplicate_issues_indices, duplicate_issue_indices) > 0.9"
+ "\n",
+ "assert jaccard_similarity(predicted_outlier_issues_indices, outlier_issue_indices) > 0.9\n",
+ "assert jaccard_similarity(predicted_duplicate_issues_indices, duplicate_issue_indices) > 0.9"
]
}
],
diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb
index b2fa92ade..2ba045367 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -80,7 +80,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb
index 3599732a0..51da2d9a6 100644
--- a/master/_sources/tutorials/datalab/text.ipynb
+++ b/master/_sources/tutorials/datalab/text.ipynb
@@ -90,7 +90,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb
index 0d8105fd1..ce8be3372 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -79,7 +79,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/indepth_overview.ipynb b/master/_sources/tutorials/indepth_overview.ipynb
index 8c1af8a11..7d89efb39 100644
--- a/master/_sources/tutorials/indepth_overview.ipynb
+++ b/master/_sources/tutorials/indepth_overview.ipynb
@@ -62,7 +62,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb
index dd83004f8..ddf90b86b 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -95,7 +95,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/multilabel_classification.ipynb b/master/_sources/tutorials/multilabel_classification.ipynb
index aab5b8442..542b9cfd4 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -73,7 +73,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb
index 337283453..79a87033f 100644
--- a/master/_sources/tutorials/object_detection.ipynb
+++ b/master/_sources/tutorials/object_detection.ipynb
@@ -77,7 +77,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/outliers.ipynb b/master/_sources/tutorials/outliers.ipynb
index d5d8c9d0c..4f7e0427a 100644
--- a/master/_sources/tutorials/outliers.ipynb
+++ b/master/_sources/tutorials/outliers.ipynb
@@ -119,7 +119,7 @@
"dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb
index 0976d0919..c0d2c1d07 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -110,7 +110,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb
index e058ebb4f..e65d991e6 100644
--- a/master/_sources/tutorials/segmentation.ipynb
+++ b/master/_sources/tutorials/segmentation.ipynb
@@ -91,7 +91,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/token_classification.ipynb b/master/_sources/tutorials/token_classification.ipynb
index 2ed9014c8..2777086e9 100644
--- a/master/_sources/tutorials/token_classification.ipynb
+++ b/master/_sources/tutorials/token_classification.ipynb
@@ -95,7 +95,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/searchindex.js b/master/searchindex.js
index 6c2df3a90..2c6f7b4df 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
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[[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Find Spurious Correlation between Vision Dataset features and class labels": [[96, "Find-Spurious-Correlation-between-Vision-Dataset-features-and-class-labels"]], "1. Load the dataset": [[96, "1.-Load-the-dataset"]], "2. Creating Dataset object to be passed to the Datalab object to find vision-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-vision-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Vision-specific property scores in the original dataset": [[96, "Vision-specific-property-scores-in-the-original-dataset"]], "Vision-specific property scores in the transformed dataset": [[96, "Vision-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. 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Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Find Spurious Correlation between Vision Dataset features and class labels": [[96, "Find-Spurious-Correlation-between-Vision-Dataset-features-and-class-labels"]], "1. Load the dataset": [[96, "1.-Load-the-dataset"]], "2. Creating Dataset object to be passed to the Datalab object to find vision-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-vision-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Vision-specific property scores in the original dataset": [[96, "Vision-specific-property-scores-in-the-original-dataset"]], "Vision-specific property scores in the transformed dataset": [[96, "Vision-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module 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"correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "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 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"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": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, 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"cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, 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method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb
index 35f0d970f..835b9297f 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": "2024-06-28T15:32:00.381660Z",
- "iopub.status.busy": "2024-06-28T15:32:00.381248Z",
- "iopub.status.idle": "2024-06-28T15:32:01.681791Z",
- "shell.execute_reply": "2024-06-28T15:32:01.681244Z"
+ "iopub.execute_input": "2024-07-01T15:01:38.704463Z",
+ "iopub.status.busy": "2024-07-01T15:01:38.704282Z",
+ "iopub.status.idle": "2024-07-01T15:01:39.968773Z",
+ "shell.execute_reply": "2024-07-01T15:01:39.968140Z"
},
"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@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:01.684513Z",
- "iopub.status.busy": "2024-06-28T15:32:01.684044Z",
- "iopub.status.idle": "2024-06-28T15:32:01.703220Z",
- "shell.execute_reply": "2024-06-28T15:32:01.702743Z"
+ "iopub.execute_input": "2024-07-01T15:01:39.971457Z",
+ "iopub.status.busy": "2024-07-01T15:01:39.971069Z",
+ "iopub.status.idle": "2024-07-01T15:01:39.990015Z",
+ "shell.execute_reply": "2024-07-01T15:01:39.989387Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:01.705803Z",
- "iopub.status.busy": "2024-06-28T15:32:01.705424Z",
- "iopub.status.idle": "2024-06-28T15:32:01.873655Z",
- "shell.execute_reply": "2024-06-28T15:32:01.873076Z"
+ "iopub.execute_input": "2024-07-01T15:01:39.992806Z",
+ "iopub.status.busy": "2024-07-01T15:01:39.992402Z",
+ "iopub.status.idle": "2024-07-01T15:01:40.303536Z",
+ "shell.execute_reply": "2024-07-01T15:01:40.302965Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:01.905438Z",
- "iopub.status.busy": "2024-06-28T15:32:01.905010Z",
- "iopub.status.idle": "2024-06-28T15:32:01.908827Z",
- "shell.execute_reply": "2024-06-28T15:32:01.908342Z"
+ "iopub.execute_input": "2024-07-01T15:01:40.336204Z",
+ "iopub.status.busy": "2024-07-01T15:01:40.335666Z",
+ "iopub.status.idle": "2024-07-01T15:01:40.340138Z",
+ "shell.execute_reply": "2024-07-01T15:01:40.339623Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:01.910976Z",
- "iopub.status.busy": "2024-06-28T15:32:01.910625Z",
- "iopub.status.idle": "2024-06-28T15:32:01.919240Z",
- "shell.execute_reply": "2024-06-28T15:32:01.918799Z"
+ "iopub.execute_input": "2024-07-01T15:01:40.342354Z",
+ "iopub.status.busy": "2024-07-01T15:01:40.342145Z",
+ "iopub.status.idle": "2024-07-01T15:01:40.351148Z",
+ "shell.execute_reply": "2024-07-01T15:01:40.350569Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:01.921349Z",
- "iopub.status.busy": "2024-06-28T15:32:01.921161Z",
- "iopub.status.idle": "2024-06-28T15:32:01.923695Z",
- "shell.execute_reply": "2024-06-28T15:32:01.923253Z"
+ "iopub.execute_input": "2024-07-01T15:01:40.353562Z",
+ "iopub.status.busy": "2024-07-01T15:01:40.353231Z",
+ "iopub.status.idle": "2024-07-01T15:01:40.356046Z",
+ "shell.execute_reply": "2024-07-01T15:01:40.355491Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:01.925557Z",
- "iopub.status.busy": "2024-06-28T15:32:01.925387Z",
- "iopub.status.idle": "2024-06-28T15:32:02.457912Z",
- "shell.execute_reply": "2024-06-28T15:32:02.457433Z"
+ "iopub.execute_input": "2024-07-01T15:01:40.358053Z",
+ "iopub.status.busy": "2024-07-01T15:01:40.357874Z",
+ "iopub.status.idle": "2024-07-01T15:01:40.885000Z",
+ "shell.execute_reply": "2024-07-01T15:01:40.884377Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:02.460329Z",
- "iopub.status.busy": "2024-06-28T15:32:02.460138Z",
- "iopub.status.idle": "2024-06-28T15:32:04.490214Z",
- "shell.execute_reply": "2024-06-28T15:32:04.489567Z"
+ "iopub.execute_input": "2024-07-01T15:01:40.887806Z",
+ "iopub.status.busy": "2024-07-01T15:01:40.887346Z",
+ "iopub.status.idle": "2024-07-01T15:01:42.858439Z",
+ "shell.execute_reply": "2024-07-01T15:01:42.857751Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:04.492856Z",
- "iopub.status.busy": "2024-06-28T15:32:04.492241Z",
- "iopub.status.idle": "2024-06-28T15:32:04.502594Z",
- "shell.execute_reply": "2024-06-28T15:32:04.502078Z"
+ "iopub.execute_input": "2024-07-01T15:01:42.861505Z",
+ "iopub.status.busy": "2024-07-01T15:01:42.860685Z",
+ "iopub.status.idle": "2024-07-01T15:01:42.872129Z",
+ "shell.execute_reply": "2024-07-01T15:01:42.871534Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:04.504766Z",
- "iopub.status.busy": "2024-06-28T15:32:04.504436Z",
- "iopub.status.idle": "2024-06-28T15:32:04.508609Z",
- "shell.execute_reply": "2024-06-28T15:32:04.508063Z"
+ "iopub.execute_input": "2024-07-01T15:01:42.874722Z",
+ "iopub.status.busy": "2024-07-01T15:01:42.874312Z",
+ "iopub.status.idle": "2024-07-01T15:01:42.879185Z",
+ "shell.execute_reply": "2024-07-01T15:01:42.878651Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:04.510703Z",
- "iopub.status.busy": "2024-06-28T15:32:04.510397Z",
- "iopub.status.idle": "2024-06-28T15:32:04.517582Z",
- "shell.execute_reply": "2024-06-28T15:32:04.517118Z"
+ "iopub.execute_input": "2024-07-01T15:01:42.881719Z",
+ "iopub.status.busy": "2024-07-01T15:01:42.881293Z",
+ "iopub.status.idle": "2024-07-01T15:01:42.890936Z",
+ "shell.execute_reply": "2024-07-01T15:01:42.890441Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:04.519554Z",
- "iopub.status.busy": "2024-06-28T15:32:04.519252Z",
- "iopub.status.idle": "2024-06-28T15:32:04.632352Z",
- "shell.execute_reply": "2024-06-28T15:32:04.631747Z"
+ "iopub.execute_input": "2024-07-01T15:01:42.893152Z",
+ "iopub.status.busy": "2024-07-01T15:01:42.892940Z",
+ "iopub.status.idle": "2024-07-01T15:01:43.010191Z",
+ "shell.execute_reply": "2024-07-01T15:01:43.009566Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:04.634587Z",
- "iopub.status.busy": "2024-06-28T15:32:04.634256Z",
- "iopub.status.idle": "2024-06-28T15:32:04.637211Z",
- "shell.execute_reply": "2024-06-28T15:32:04.636666Z"
+ "iopub.execute_input": "2024-07-01T15:01:43.012877Z",
+ "iopub.status.busy": "2024-07-01T15:01:43.012678Z",
+ "iopub.status.idle": "2024-07-01T15:01:43.015881Z",
+ "shell.execute_reply": "2024-07-01T15:01:43.015414Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:04.639474Z",
- "iopub.status.busy": "2024-06-28T15:32:04.638904Z",
- "iopub.status.idle": "2024-06-28T15:32:06.709224Z",
- "shell.execute_reply": "2024-06-28T15:32:06.708438Z"
+ "iopub.execute_input": "2024-07-01T15:01:43.017749Z",
+ "iopub.status.busy": "2024-07-01T15:01:43.017574Z",
+ "iopub.status.idle": "2024-07-01T15:01:45.116344Z",
+ "shell.execute_reply": "2024-07-01T15:01:45.115698Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:06.712672Z",
- "iopub.status.busy": "2024-06-28T15:32:06.711755Z",
- "iopub.status.idle": "2024-06-28T15:32:06.724142Z",
- "shell.execute_reply": "2024-06-28T15:32:06.723572Z"
+ "iopub.execute_input": "2024-07-01T15:01:45.119290Z",
+ "iopub.status.busy": "2024-07-01T15:01:45.118731Z",
+ "iopub.status.idle": "2024-07-01T15:01:45.130593Z",
+ "shell.execute_reply": "2024-07-01T15:01:45.130118Z"
}
},
"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:32:06.726394Z",
- "iopub.status.busy": "2024-06-28T15:32:06.726040Z",
- "iopub.status.idle": "2024-06-28T15:32:06.750576Z",
- "shell.execute_reply": "2024-06-28T15:32:06.750015Z"
+ "iopub.execute_input": "2024-07-01T15:01:45.132594Z",
+ "iopub.status.busy": "2024-07-01T15:01:45.132413Z",
+ "iopub.status.idle": "2024-07-01T15:01:45.200709Z",
+ "shell.execute_reply": "2024-07-01T15:01:45.200202Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index fd2594923..87f58e815 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
@@ -2115,7 +2115,7 @@ Easy Mode which will automatically produce one for you. Super easy to use, Cleanlab Studio is no-code platform for data-centric AI that automatically: detects data
issues (more types of issues than this cleanlab package), helps you quickly correct these data issues, confidently labels large subsets of an unlabeled dataset, and provides other smart metadata about each of your data points – all powered by a system that automatically trains/deploys the best ML model for your data. Try it for free!
diff --git a/master/tutorials/datalab/image.ipynb b/master/tutorials/datalab/image.ipynb
index 9bd5eb804..03d847503 100644
--- a/master/tutorials/datalab/image.ipynb
+++ b/master/tutorials/datalab/image.ipynb
@@ -71,10 +71,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:09.403235Z",
- "iopub.status.busy": "2024-06-28T15:33:09.402738Z",
- "iopub.status.idle": "2024-06-28T15:33:12.466652Z",
- "shell.execute_reply": "2024-06-28T15:33:12.466079Z"
+ "iopub.execute_input": "2024-07-01T15:02:48.074971Z",
+ "iopub.status.busy": "2024-07-01T15:02:48.074723Z",
+ "iopub.status.idle": "2024-07-01T15:02:51.342353Z",
+ "shell.execute_reply": "2024-07-01T15:02:51.341605Z"
},
"nbsphinx": "hidden"
},
@@ -112,10 +112,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:12.469241Z",
- "iopub.status.busy": "2024-06-28T15:33:12.468934Z",
- "iopub.status.idle": "2024-06-28T15:33:12.472447Z",
- "shell.execute_reply": "2024-06-28T15:33:12.472019Z"
+ "iopub.execute_input": "2024-07-01T15:02:51.345614Z",
+ "iopub.status.busy": "2024-07-01T15:02:51.345060Z",
+ "iopub.status.idle": "2024-07-01T15:02:51.349168Z",
+ "shell.execute_reply": "2024-07-01T15:02:51.348682Z"
}
},
"outputs": [],
@@ -152,17 +152,17 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:12.474380Z",
- "iopub.status.busy": "2024-06-28T15:33:12.474201Z",
- "iopub.status.idle": "2024-06-28T15:33:24.977088Z",
- "shell.execute_reply": "2024-06-28T15:33:24.976474Z"
+ "iopub.execute_input": "2024-07-01T15:02:51.351438Z",
+ "iopub.status.busy": "2024-07-01T15:02:51.351054Z",
+ "iopub.status.idle": "2024-07-01T15:03:02.526470Z",
+ "shell.execute_reply": "2024-07-01T15:03:02.525870Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "dd367ed6b3e145b9ba56c440d63f6948",
+ "model_id": "bd4e5e775e0d4b5d90568b686f8fd56f",
"version_major": 2,
"version_minor": 0
},
@@ -176,7 +176,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "999579ced4a04955b6fe76b06613510d",
+ "model_id": "a9efee99388e4bd987cba82e4c249be5",
"version_major": 2,
"version_minor": 0
},
@@ -190,7 +190,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "9611c5ddf09444219b0832f81930fdd7",
+ "model_id": "b13b21c3b7544706aacfbba4f3504a8b",
"version_major": 2,
"version_minor": 0
},
@@ -204,7 +204,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "2bbd780010f04629a57e9a48d6241a4e",
+ "model_id": "dcfb76cdced842fd810c0329fa0f1c7f",
"version_major": 2,
"version_minor": 0
},
@@ -218,7 +218,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "21ffc7623d52499f96e098345ad1b94d",
+ "model_id": "0febc72cf36d4d939a7991cbb880240e",
"version_major": 2,
"version_minor": 0
},
@@ -232,7 +232,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "8c14226990a04e87aa10a393e2a0203a",
+ "model_id": "6296fc9f1a3947edb989ab3a35afbefe",
"version_major": 2,
"version_minor": 0
},
@@ -246,7 +246,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "519660b9e9fd424bbb188e3f3d9d3b89",
+ "model_id": "bc98754b340343f594559442ba450aa4",
"version_major": 2,
"version_minor": 0
},
@@ -260,7 +260,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "510e84f543554f8d8f0f21ce00483d7d",
+ "model_id": "d60f32b2907d4a288385a30c717ef39d",
"version_major": 2,
"version_minor": 0
},
@@ -302,10 +302,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:24.979342Z",
- "iopub.status.busy": "2024-06-28T15:33:24.979046Z",
- "iopub.status.idle": "2024-06-28T15:33:24.983576Z",
- "shell.execute_reply": "2024-06-28T15:33:24.983103Z"
+ "iopub.execute_input": "2024-07-01T15:03:02.528967Z",
+ "iopub.status.busy": "2024-07-01T15:03:02.528621Z",
+ "iopub.status.idle": "2024-07-01T15:03:02.532647Z",
+ "shell.execute_reply": "2024-07-01T15:03:02.532080Z"
}
},
"outputs": [
@@ -330,17 +330,17 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:24.985815Z",
- "iopub.status.busy": "2024-06-28T15:33:24.985393Z",
- "iopub.status.idle": "2024-06-28T15:33:36.598392Z",
- "shell.execute_reply": "2024-06-28T15:33:36.597792Z"
+ "iopub.execute_input": "2024-07-01T15:03:02.534910Z",
+ "iopub.status.busy": "2024-07-01T15:03:02.534585Z",
+ "iopub.status.idle": "2024-07-01T15:03:13.866603Z",
+ "shell.execute_reply": "2024-07-01T15:03:13.865937Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "748b7bbe2c8345c6a22623d9f52f46cf",
+ "model_id": "70b6c17f51c948158afefdd56830a23f",
"version_major": 2,
"version_minor": 0
},
@@ -378,10 +378,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:36.601110Z",
- "iopub.status.busy": "2024-06-28T15:33:36.600777Z",
- "iopub.status.idle": "2024-06-28T15:33:55.067117Z",
- "shell.execute_reply": "2024-06-28T15:33:55.066477Z"
+ "iopub.execute_input": "2024-07-01T15:03:13.869049Z",
+ "iopub.status.busy": "2024-07-01T15:03:13.868821Z",
+ "iopub.status.idle": "2024-07-01T15:03:31.582919Z",
+ "shell.execute_reply": "2024-07-01T15:03:31.582298Z"
}
},
"outputs": [],
@@ -414,10 +414,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:55.070279Z",
- "iopub.status.busy": "2024-06-28T15:33:55.069803Z",
- "iopub.status.idle": "2024-06-28T15:33:55.075612Z",
- "shell.execute_reply": "2024-06-28T15:33:55.075060Z"
+ "iopub.execute_input": "2024-07-01T15:03:31.585953Z",
+ "iopub.status.busy": "2024-07-01T15:03:31.585389Z",
+ "iopub.status.idle": "2024-07-01T15:03:31.591279Z",
+ "shell.execute_reply": "2024-07-01T15:03:31.590830Z"
}
},
"outputs": [],
@@ -455,10 +455,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:55.078062Z",
- "iopub.status.busy": "2024-06-28T15:33:55.077664Z",
- "iopub.status.idle": "2024-06-28T15:33:55.082536Z",
- "shell.execute_reply": "2024-06-28T15:33:55.081922Z"
+ "iopub.execute_input": "2024-07-01T15:03:31.593306Z",
+ "iopub.status.busy": "2024-07-01T15:03:31.592981Z",
+ "iopub.status.idle": "2024-07-01T15:03:31.596855Z",
+ "shell.execute_reply": "2024-07-01T15:03:31.596450Z"
},
"nbsphinx": "hidden"
},
@@ -595,10 +595,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:55.085223Z",
- "iopub.status.busy": "2024-06-28T15:33:55.084873Z",
- "iopub.status.idle": "2024-06-28T15:33:55.094273Z",
- "shell.execute_reply": "2024-06-28T15:33:55.093706Z"
+ "iopub.execute_input": "2024-07-01T15:03:31.598838Z",
+ "iopub.status.busy": "2024-07-01T15:03:31.598577Z",
+ "iopub.status.idle": "2024-07-01T15:03:31.607398Z",
+ "shell.execute_reply": "2024-07-01T15:03:31.606925Z"
},
"nbsphinx": "hidden"
},
@@ -723,10 +723,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:55.096457Z",
- "iopub.status.busy": "2024-06-28T15:33:55.096269Z",
- "iopub.status.idle": "2024-06-28T15:33:55.123566Z",
- "shell.execute_reply": "2024-06-28T15:33:55.123064Z"
+ "iopub.execute_input": "2024-07-01T15:03:31.609325Z",
+ "iopub.status.busy": "2024-07-01T15:03:31.609007Z",
+ "iopub.status.idle": "2024-07-01T15:03:31.635278Z",
+ "shell.execute_reply": "2024-07-01T15:03:31.634840Z"
}
},
"outputs": [],
@@ -763,10 +763,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:33:55.126192Z",
- "iopub.status.busy": "2024-06-28T15:33:55.125844Z",
- "iopub.status.idle": "2024-06-28T15:34:29.556539Z",
- "shell.execute_reply": "2024-06-28T15:34:29.555882Z"
+ "iopub.execute_input": "2024-07-01T15:03:31.637322Z",
+ "iopub.status.busy": "2024-07-01T15:03:31.636996Z",
+ "iopub.status.idle": "2024-07-01T15:04:03.652341Z",
+ "shell.execute_reply": "2024-07-01T15:04:03.651742Z"
}
},
"outputs": [
@@ -782,21 +782,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.070\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.749\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.893\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.439\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "01d874da00234ef1aa34287815d37d45",
+ "model_id": "b69aa5fb137444eb962d31f239578d65",
"version_major": 2,
"version_minor": 0
},
@@ -817,7 +817,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "02ee74aebbc04f9a82b5829341e501a6",
+ "model_id": "6ca247bf72f54f03aabdd5d72546025f",
"version_major": 2,
"version_minor": 0
},
@@ -840,21 +840,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.975\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.851\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.917\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.491\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ddb26404cd4644d3a9efa8efd7af9104",
+ "model_id": "d6465626e3264fa58f44ddccd18cfef2",
"version_major": 2,
"version_minor": 0
},
@@ -875,7 +875,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c60874cc394b428786de11432b5ba1ce",
+ "model_id": "3fa46dee97a14f9594eb60312b03e045",
"version_major": 2,
"version_minor": 0
},
@@ -898,21 +898,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.951\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.739\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 5.061\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.490\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ca1506e7d3f846adb2f7487be4ad5f1d",
+ "model_id": "76cd9d157bf74d6e93db6f5727c6f900",
"version_major": 2,
"version_minor": 0
},
@@ -933,7 +933,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ff1c46ea9c474d1bb4645c7cbbf298b0",
+ "model_id": "9717f3b4aaae491d9cb2e07d49a003a5",
"version_major": 2,
"version_minor": 0
},
@@ -1012,10 +1012,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:34:29.558914Z",
- "iopub.status.busy": "2024-06-28T15:34:29.558674Z",
- "iopub.status.idle": "2024-06-28T15:34:29.573241Z",
- "shell.execute_reply": "2024-06-28T15:34:29.572642Z"
+ "iopub.execute_input": "2024-07-01T15:04:03.654962Z",
+ "iopub.status.busy": "2024-07-01T15:04:03.654720Z",
+ "iopub.status.idle": "2024-07-01T15:04:03.668632Z",
+ "shell.execute_reply": "2024-07-01T15:04:03.668209Z"
}
},
"outputs": [],
@@ -1040,10 +1040,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:34:29.575486Z",
- "iopub.status.busy": "2024-06-28T15:34:29.575301Z",
- "iopub.status.idle": "2024-06-28T15:34:30.062525Z",
- "shell.execute_reply": "2024-06-28T15:34:30.061951Z"
+ "iopub.execute_input": "2024-07-01T15:04:03.670732Z",
+ "iopub.status.busy": "2024-07-01T15:04:03.670344Z",
+ "iopub.status.idle": "2024-07-01T15:04:04.150524Z",
+ "shell.execute_reply": "2024-07-01T15:04:04.149791Z"
}
},
"outputs": [],
@@ -1063,10 +1063,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:34:30.065567Z",
- "iopub.status.busy": "2024-06-28T15:34:30.065103Z",
- "iopub.status.idle": "2024-06-28T15:36:09.855028Z",
- "shell.execute_reply": "2024-06-28T15:36:09.854365Z"
+ "iopub.execute_input": "2024-07-01T15:04:04.153028Z",
+ "iopub.status.busy": "2024-07-01T15:04:04.152825Z",
+ "iopub.status.idle": "2024-07-01T15:05:40.110641Z",
+ "shell.execute_reply": "2024-07-01T15:05:40.110011Z"
}
},
"outputs": [
@@ -1105,7 +1105,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f55b4eb540fd4f368229bcf7012adf9f",
+ "model_id": "8b242b3757014ca08c0be26603c856e5",
"version_major": 2,
"version_minor": 0
},
@@ -1144,10 +1144,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:09.857655Z",
- "iopub.status.busy": "2024-06-28T15:36:09.857109Z",
- "iopub.status.idle": "2024-06-28T15:36:10.330566Z",
- "shell.execute_reply": "2024-06-28T15:36:10.330003Z"
+ "iopub.execute_input": "2024-07-01T15:05:40.113143Z",
+ "iopub.status.busy": "2024-07-01T15:05:40.112512Z",
+ "iopub.status.idle": "2024-07-01T15:05:40.560298Z",
+ "shell.execute_reply": "2024-07-01T15:05:40.559714Z"
}
},
"outputs": [
@@ -1293,10 +1293,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.333473Z",
- "iopub.status.busy": "2024-06-28T15:36:10.333080Z",
- "iopub.status.idle": "2024-06-28T15:36:10.397225Z",
- "shell.execute_reply": "2024-06-28T15:36:10.396605Z"
+ "iopub.execute_input": "2024-07-01T15:05:40.563315Z",
+ "iopub.status.busy": "2024-07-01T15:05:40.562801Z",
+ "iopub.status.idle": "2024-07-01T15:05:40.624738Z",
+ "shell.execute_reply": "2024-07-01T15:05:40.624116Z"
}
},
"outputs": [
@@ -1400,10 +1400,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.399332Z",
- "iopub.status.busy": "2024-06-28T15:36:10.399151Z",
- "iopub.status.idle": "2024-06-28T15:36:10.408208Z",
- "shell.execute_reply": "2024-06-28T15:36:10.407701Z"
+ "iopub.execute_input": "2024-07-01T15:05:40.627071Z",
+ "iopub.status.busy": "2024-07-01T15:05:40.626639Z",
+ "iopub.status.idle": "2024-07-01T15:05:40.635299Z",
+ "shell.execute_reply": "2024-07-01T15:05:40.634756Z"
}
},
"outputs": [
@@ -1533,10 +1533,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.410606Z",
- "iopub.status.busy": "2024-06-28T15:36:10.410094Z",
- "iopub.status.idle": "2024-06-28T15:36:10.415260Z",
- "shell.execute_reply": "2024-06-28T15:36:10.414712Z"
+ "iopub.execute_input": "2024-07-01T15:05:40.637389Z",
+ "iopub.status.busy": "2024-07-01T15:05:40.636989Z",
+ "iopub.status.idle": "2024-07-01T15:05:40.641711Z",
+ "shell.execute_reply": "2024-07-01T15:05:40.641175Z"
},
"nbsphinx": "hidden"
},
@@ -1582,10 +1582,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.417475Z",
- "iopub.status.busy": "2024-06-28T15:36:10.417052Z",
- "iopub.status.idle": "2024-06-28T15:36:10.958110Z",
- "shell.execute_reply": "2024-06-28T15:36:10.957499Z"
+ "iopub.execute_input": "2024-07-01T15:05:40.643683Z",
+ "iopub.status.busy": "2024-07-01T15:05:40.643498Z",
+ "iopub.status.idle": "2024-07-01T15:05:41.152016Z",
+ "shell.execute_reply": "2024-07-01T15:05:41.151428Z"
}
},
"outputs": [
@@ -1620,10 +1620,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.960727Z",
- "iopub.status.busy": "2024-06-28T15:36:10.960231Z",
- "iopub.status.idle": "2024-06-28T15:36:10.969176Z",
- "shell.execute_reply": "2024-06-28T15:36:10.968687Z"
+ "iopub.execute_input": "2024-07-01T15:05:41.154341Z",
+ "iopub.status.busy": "2024-07-01T15:05:41.154029Z",
+ "iopub.status.idle": "2024-07-01T15:05:41.162706Z",
+ "shell.execute_reply": "2024-07-01T15:05:41.162252Z"
}
},
"outputs": [
@@ -1790,10 +1790,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.971544Z",
- "iopub.status.busy": "2024-06-28T15:36:10.971137Z",
- "iopub.status.idle": "2024-06-28T15:36:10.978595Z",
- "shell.execute_reply": "2024-06-28T15:36:10.978143Z"
+ "iopub.execute_input": "2024-07-01T15:05:41.164766Z",
+ "iopub.status.busy": "2024-07-01T15:05:41.164446Z",
+ "iopub.status.idle": "2024-07-01T15:05:41.171486Z",
+ "shell.execute_reply": "2024-07-01T15:05:41.171059Z"
},
"nbsphinx": "hidden"
},
@@ -1869,10 +1869,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:10.980809Z",
- "iopub.status.busy": "2024-06-28T15:36:10.980353Z",
- "iopub.status.idle": "2024-06-28T15:36:11.774826Z",
- "shell.execute_reply": "2024-06-28T15:36:11.774218Z"
+ "iopub.execute_input": "2024-07-01T15:05:41.173399Z",
+ "iopub.status.busy": "2024-07-01T15:05:41.173075Z",
+ "iopub.status.idle": "2024-07-01T15:05:41.934946Z",
+ "shell.execute_reply": "2024-07-01T15:05:41.934291Z"
}
},
"outputs": [
@@ -1909,10 +1909,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:11.777464Z",
- "iopub.status.busy": "2024-06-28T15:36:11.777117Z",
- "iopub.status.idle": "2024-06-28T15:36:11.793779Z",
- "shell.execute_reply": "2024-06-28T15:36:11.793195Z"
+ "iopub.execute_input": "2024-07-01T15:05:41.937509Z",
+ "iopub.status.busy": "2024-07-01T15:05:41.937076Z",
+ "iopub.status.idle": "2024-07-01T15:05:41.952809Z",
+ "shell.execute_reply": "2024-07-01T15:05:41.952240Z"
}
},
"outputs": [
@@ -2069,10 +2069,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-28T15:36:11.796111Z",
- "iopub.status.busy": "2024-06-28T15:36:11.795755Z",
- "iopub.status.idle": "2024-06-28T15:36:11.801625Z",
- "shell.execute_reply": "2024-06-28T15:36:11.801150Z"
+ "iopub.execute_input": "2024-07-01T15:05:41.954986Z",
+ "iopub.status.busy": "2024-07-01T15:05:41.954646Z",
+ "iopub.status.idle": "2024-07-01T15:05:41.960097Z",
+ "shell.execute_reply": "2024-07-01T15:05:41.959674Z"
},
"nbsphinx": "hidden"
},
@@ -2117,10 +2117,10 @@
"execution_count": 25,
"metadata": {
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- "iopub.execute_input": "2024-06-28T15:36:11.803713Z",
- "iopub.status.busy": "2024-06-28T15:36:11.803391Z",
- "iopub.status.idle": "2024-06-28T15:36:12.281855Z",
- "shell.execute_reply": "2024-06-28T15:36:12.281284Z"
+ "iopub.execute_input": "2024-07-01T15:05:41.961941Z",
+ "iopub.status.busy": "2024-07-01T15:05:41.961770Z",
+ "iopub.status.idle": "2024-07-01T15:05:42.348365Z",
+ "shell.execute_reply": "2024-07-01T15:05:42.347794Z"
}
},
"outputs": [
@@ -2202,10 +2202,10 @@
"execution_count": 26,
"metadata": {
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- "iopub.execute_input": "2024-06-28T15:36:12.285071Z",
- "iopub.status.busy": "2024-06-28T15:36:12.284635Z",
- "iopub.status.idle": "2024-06-28T15:36:12.295272Z",
- "shell.execute_reply": "2024-06-28T15:36:12.294767Z"
+ "iopub.execute_input": "2024-07-01T15:05:42.350754Z",
+ "iopub.status.busy": "2024-07-01T15:05:42.350573Z",
+ "iopub.status.idle": "2024-07-01T15:05:42.359462Z",
+ "shell.execute_reply": "2024-07-01T15:05:42.358866Z"
}
},
"outputs": [
@@ -2333,10 +2333,10 @@
"execution_count": 27,
"metadata": {
"execution": {
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+ "_view_name": "HTMLView",
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@@ -8633,7 +8570,7 @@
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diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb
index f67c6a971..452755a26 100644
--- a/master/tutorials/datalab/tabular.ipynb
+++ b/master/tutorials/datalab/tabular.ipynb
@@ -73,10 +73,10 @@
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@@ -86,7 +86,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -154,10 +154,10 @@
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@@ -264,10 +264,10 @@
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@@ -288,10 +288,10 @@
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@@ -336,10 +336,10 @@
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@@ -362,10 +362,10 @@
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@@ -401,10 +401,10 @@
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@@ -436,10 +436,10 @@
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@@ -476,10 +476,10 @@
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@@ -609,10 +609,10 @@
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@@ -716,10 +716,10 @@
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@@ -848,10 +848,10 @@
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@@ -965,10 +965,10 @@
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@@ -1079,10 +1079,10 @@
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@@ -1300,10 +1300,10 @@
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diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html
index 6f6318e65..cc4207eec 100644
--- a/master/tutorials/datalab/text.html
+++ b/master/tutorials/datalab/text.html
@@ -791,7 +791,7 @@ 2. Load and format the text dataset
+
|
- Age |
- Gender |
- Location |
- Annual_Spending |
- Number_of_Transactions |
- Last_Purchase_Date |
- | |
- is_null_issue |
- null_score |
+ Age |
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+ Location |
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- 2024-04-08 00:00:00 |
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+
+ 1 |
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+ 3 |
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@@ -3542,7 +3542,7 @@ 1. Load the dataset