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diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle
index 20914b2b7..6f30f378d 100644
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diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree
index 3cb65a135..96617f3dc 100644
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diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree
index 3b97c250d..6cb8c3f72 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 b648c52ba..388c995f2 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": "2024-09-26T16:57:46.905901Z",
- "iopub.status.busy": "2024-09-26T16:57:46.905412Z",
- "iopub.status.idle": "2024-09-26T16:57:48.220424Z",
- "shell.execute_reply": "2024-09-26T16:57:48.219845Z"
+ "iopub.execute_input": "2024-09-27T13:44:12.200916Z",
+ "iopub.status.busy": "2024-09-27T13:44:12.200561Z",
+ "iopub.status.idle": "2024-09-27T13:44:13.479668Z",
+ "shell.execute_reply": "2024-09-27T13:44:13.479088Z"
},
"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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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": "2024-09-26T16:57:48.222820Z",
- "iopub.status.busy": "2024-09-26T16:57:48.222253Z",
- "iopub.status.idle": "2024-09-26T16:57:48.241708Z",
- "shell.execute_reply": "2024-09-26T16:57:48.241070Z"
+ "iopub.execute_input": "2024-09-27T13:44:13.482034Z",
+ "iopub.status.busy": "2024-09-27T13:44:13.481452Z",
+ "iopub.status.idle": "2024-09-27T13:44:13.500047Z",
+ "shell.execute_reply": "2024-09-27T13:44:13.499596Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:48.243929Z",
- "iopub.status.busy": "2024-09-26T16:57:48.243487Z",
- "iopub.status.idle": "2024-09-26T16:57:48.451471Z",
- "shell.execute_reply": "2024-09-26T16:57:48.450893Z"
+ "iopub.execute_input": "2024-09-27T13:44:13.502039Z",
+ "iopub.status.busy": "2024-09-27T13:44:13.501593Z",
+ "iopub.status.idle": "2024-09-27T13:44:13.696938Z",
+ "shell.execute_reply": "2024-09-27T13:44:13.696313Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:48.483707Z",
- "iopub.status.busy": "2024-09-26T16:57:48.483195Z",
- "iopub.status.idle": "2024-09-26T16:57:48.487154Z",
- "shell.execute_reply": "2024-09-26T16:57:48.486583Z"
+ "iopub.execute_input": "2024-09-27T13:44:13.729165Z",
+ "iopub.status.busy": "2024-09-27T13:44:13.728951Z",
+ "iopub.status.idle": "2024-09-27T13:44:13.732830Z",
+ "shell.execute_reply": "2024-09-27T13:44:13.732365Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:48.488961Z",
- "iopub.status.busy": "2024-09-26T16:57:48.488621Z",
- "iopub.status.idle": "2024-09-26T16:57:48.496919Z",
- "shell.execute_reply": "2024-09-26T16:57:48.496323Z"
+ "iopub.execute_input": "2024-09-27T13:44:13.734478Z",
+ "iopub.status.busy": "2024-09-27T13:44:13.734300Z",
+ "iopub.status.idle": "2024-09-27T13:44:13.742648Z",
+ "shell.execute_reply": "2024-09-27T13:44:13.742221Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:48.498974Z",
- "iopub.status.busy": "2024-09-26T16:57:48.498629Z",
- "iopub.status.idle": "2024-09-26T16:57:48.500983Z",
- "shell.execute_reply": "2024-09-26T16:57:48.500538Z"
+ "iopub.execute_input": "2024-09-27T13:44:13.744355Z",
+ "iopub.status.busy": "2024-09-27T13:44:13.744172Z",
+ "iopub.status.idle": "2024-09-27T13:44:13.746680Z",
+ "shell.execute_reply": "2024-09-27T13:44:13.746217Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:48.502678Z",
- "iopub.status.busy": "2024-09-26T16:57:48.502341Z",
- "iopub.status.idle": "2024-09-26T16:57:49.032323Z",
- "shell.execute_reply": "2024-09-26T16:57:49.031807Z"
+ "iopub.execute_input": "2024-09-27T13:44:13.748214Z",
+ "iopub.status.busy": "2024-09-27T13:44:13.748042Z",
+ "iopub.status.idle": "2024-09-27T13:44:14.270554Z",
+ "shell.execute_reply": "2024-09-27T13:44:14.269884Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:49.034548Z",
- "iopub.status.busy": "2024-09-26T16:57:49.034196Z",
- "iopub.status.idle": "2024-09-26T16:57:50.968947Z",
- "shell.execute_reply": "2024-09-26T16:57:50.968319Z"
+ "iopub.execute_input": "2024-09-27T13:44:14.272696Z",
+ "iopub.status.busy": "2024-09-27T13:44:14.272497Z",
+ "iopub.status.idle": "2024-09-27T13:44:16.167242Z",
+ "shell.execute_reply": "2024-09-27T13:44:16.166648Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:50.971511Z",
- "iopub.status.busy": "2024-09-26T16:57:50.970708Z",
- "iopub.status.idle": "2024-09-26T16:57:50.981203Z",
- "shell.execute_reply": "2024-09-26T16:57:50.980707Z"
+ "iopub.execute_input": "2024-09-27T13:44:16.169775Z",
+ "iopub.status.busy": "2024-09-27T13:44:16.169000Z",
+ "iopub.status.idle": "2024-09-27T13:44:16.179484Z",
+ "shell.execute_reply": "2024-09-27T13:44:16.179037Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:50.983160Z",
- "iopub.status.busy": "2024-09-26T16:57:50.982812Z",
- "iopub.status.idle": "2024-09-26T16:57:50.986985Z",
- "shell.execute_reply": "2024-09-26T16:57:50.986552Z"
+ "iopub.execute_input": "2024-09-27T13:44:16.181448Z",
+ "iopub.status.busy": "2024-09-27T13:44:16.181041Z",
+ "iopub.status.idle": "2024-09-27T13:44:16.185086Z",
+ "shell.execute_reply": "2024-09-27T13:44:16.184632Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:50.988771Z",
- "iopub.status.busy": "2024-09-26T16:57:50.988449Z",
- "iopub.status.idle": "2024-09-26T16:57:50.996238Z",
- "shell.execute_reply": "2024-09-26T16:57:50.995660Z"
+ "iopub.execute_input": "2024-09-27T13:44:16.186814Z",
+ "iopub.status.busy": "2024-09-27T13:44:16.186483Z",
+ "iopub.status.idle": "2024-09-27T13:44:16.194898Z",
+ "shell.execute_reply": "2024-09-27T13:44:16.194442Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:50.998409Z",
- "iopub.status.busy": "2024-09-26T16:57:50.997938Z",
- "iopub.status.idle": "2024-09-26T16:57:51.112743Z",
- "shell.execute_reply": "2024-09-26T16:57:51.112140Z"
+ "iopub.execute_input": "2024-09-27T13:44:16.196580Z",
+ "iopub.status.busy": "2024-09-27T13:44:16.196252Z",
+ "iopub.status.idle": "2024-09-27T13:44:16.309588Z",
+ "shell.execute_reply": "2024-09-27T13:44:16.309001Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:51.114671Z",
- "iopub.status.busy": "2024-09-26T16:57:51.114330Z",
- "iopub.status.idle": "2024-09-26T16:57:51.117374Z",
- "shell.execute_reply": "2024-09-26T16:57:51.116803Z"
+ "iopub.execute_input": "2024-09-27T13:44:16.311378Z",
+ "iopub.status.busy": "2024-09-27T13:44:16.311198Z",
+ "iopub.status.idle": "2024-09-27T13:44:16.314110Z",
+ "shell.execute_reply": "2024-09-27T13:44:16.313548Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:51.119122Z",
- "iopub.status.busy": "2024-09-26T16:57:51.118777Z",
- "iopub.status.idle": "2024-09-26T16:57:53.250696Z",
- "shell.execute_reply": "2024-09-26T16:57:53.249828Z"
+ "iopub.execute_input": "2024-09-27T13:44:16.315717Z",
+ "iopub.status.busy": "2024-09-27T13:44:16.315450Z",
+ "iopub.status.idle": "2024-09-27T13:44:18.461870Z",
+ "shell.execute_reply": "2024-09-27T13:44:18.461184Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:53.253432Z",
- "iopub.status.busy": "2024-09-26T16:57:53.252773Z",
- "iopub.status.idle": "2024-09-26T16:57:53.264456Z",
- "shell.execute_reply": "2024-09-26T16:57:53.263964Z"
+ "iopub.execute_input": "2024-09-27T13:44:18.464456Z",
+ "iopub.status.busy": "2024-09-27T13:44:18.463827Z",
+ "iopub.status.idle": "2024-09-27T13:44:18.475330Z",
+ "shell.execute_reply": "2024-09-27T13:44:18.474881Z"
}
},
"outputs": [
@@ -786,10 +786,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:53.266337Z",
- "iopub.status.busy": "2024-09-26T16:57:53.265982Z",
- "iopub.status.idle": "2024-09-26T16:57:53.320394Z",
- "shell.execute_reply": "2024-09-26T16:57:53.319936Z"
+ "iopub.execute_input": "2024-09-27T13:44:18.476970Z",
+ "iopub.status.busy": "2024-09-27T13:44:18.476794Z",
+ "iopub.status.idle": "2024-09-27T13:44:18.534040Z",
+ "shell.execute_reply": "2024-09-27T13:44:18.533545Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
index 1b9d62b84..ee987f2db 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": "2024-09-26T16:57:56.582392Z",
- "iopub.status.busy": "2024-09-26T16:57:56.581922Z",
- "iopub.status.idle": "2024-09-26T16:57:59.568352Z",
- "shell.execute_reply": "2024-09-26T16:57:59.567688Z"
+ "iopub.execute_input": "2024-09-27T13:44:21.818795Z",
+ "iopub.status.busy": "2024-09-27T13:44:21.818359Z",
+ "iopub.status.idle": "2024-09-27T13:44:25.172344Z",
+ "shell.execute_reply": "2024-09-27T13:44:25.171708Z"
},
"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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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": "2024-09-26T16:57:59.570666Z",
- "iopub.status.busy": "2024-09-26T16:57:59.570348Z",
- "iopub.status.idle": "2024-09-26T16:57:59.573999Z",
- "shell.execute_reply": "2024-09-26T16:57:59.573434Z"
+ "iopub.execute_input": "2024-09-27T13:44:25.174634Z",
+ "iopub.status.busy": "2024-09-27T13:44:25.174327Z",
+ "iopub.status.idle": "2024-09-27T13:44:25.177811Z",
+ "shell.execute_reply": "2024-09-27T13:44:25.177332Z"
}
},
"outputs": [],
@@ -185,10 +185,10 @@
"execution_count": 3,
"metadata": {
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@@ -219,10 +219,10 @@
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@@ -312,10 +312,10 @@
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@@ -330,10 +330,10 @@
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@@ -342,7 +342,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'card_about_to_expire', 'cancel_transfer', 'getting_spare_card', 'visa_or_mastercard', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'change_pin', 'beneficiary_not_allowed', 'apple_pay_or_google_pay'}\n"
+ "Classes: {'getting_spare_card', 'change_pin', 'visa_or_mastercard', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'card_about_to_expire', 'cancel_transfer', 'beneficiary_not_allowed', 'card_payment_fee_charged', 'apple_pay_or_google_pay'}\n"
]
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@@ -453,17 +453,17 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
index a5325d72b..4d8b78fd5 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -131,10 +131,10 @@
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@@ -157,10 +157,10 @@
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@@ -208,10 +208,10 @@
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@@ -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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@@ -3191,6 +3168,29 @@
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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 ffc430e68..f96269130 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
@@ -80,10 +80,10 @@
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@@ -93,7 +93,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
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+ "layout": "IPY_MODEL_cbaab24fee4e48e29c8c9a265a769a67",
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+ "style": "IPY_MODEL_e7203aad39f349f5a2ab732b1748e935",
+ "tabbable": null,
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+ "value": " 132/132 [00:00<00:00, 12681.19 examples/s]"
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@@ -1618,43 +1660,30 @@
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+ "_view_name": "HTMLView",
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+ "description_allow_html": false,
+ "layout": "IPY_MODEL_e450d708e2ef4d12b0412b094270b7ba",
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@@ -1707,30 +1736,7 @@
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@@ -1783,28 +1789,22 @@
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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 54e829ffc..ce77562ed 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
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},
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@@ -91,7 +91,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
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@@ -250,10 +250,10 @@
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@@ -356,10 +356,10 @@
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@@ -448,10 +448,10 @@
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@@ -520,10 +520,10 @@
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@@ -559,10 +559,10 @@
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@@ -685,10 +685,10 @@
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@@ -821,10 +821,10 @@
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@@ -935,10 +935,10 @@
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@@ -1005,10 +1005,10 @@
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+ "shell.execute_reply": "2024-09-27T13:45:16.390908Z"
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@@ -1200,10 +1200,10 @@
"execution_count": 14,
"metadata": {
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- "shell.execute_reply": "2024-09-26T16:58:50.456315Z"
+ "iopub.execute_input": "2024-09-27T13:45:16.393310Z",
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+ "shell.execute_reply": "2024-09-27T13:45:16.401281Z"
}
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"outputs": [
@@ -1319,10 +1319,10 @@
"execution_count": 15,
"metadata": {
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+ "shell.execute_reply": "2024-09-27T13:45:16.409374Z"
},
"scrolled": true
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@@ -1447,10 +1447,10 @@
"execution_count": 16,
"metadata": {
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+ "shell.execute_reply": "2024-09-27T13:45:16.419938Z"
}
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"outputs": [
@@ -1553,10 +1553,10 @@
"execution_count": 17,
"metadata": {
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- "iopub.execute_input": "2024-09-26T16:58:50.477133Z",
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+ "iopub.execute_input": "2024-09-27T13:45:16.422097Z",
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+ "iopub.status.idle": "2024-09-27T13:45:16.439717Z",
+ "shell.execute_reply": "2024-09-27T13:45:16.439288Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
index 728d586b2..3f66f37a1 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
@@ -71,10 +71,10 @@
"execution_count": 1,
"metadata": {
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- "iopub.status.idle": "2024-09-26T16:58:56.467577Z",
- "shell.execute_reply": "2024-09-26T16:58:56.467015Z"
+ "iopub.execute_input": "2024-09-27T13:45:19.192307Z",
+ "iopub.status.busy": "2024-09-27T13:45:19.192117Z",
+ "iopub.status.idle": "2024-09-27T13:45:22.256949Z",
+ "shell.execute_reply": "2024-09-27T13:45:22.256396Z"
},
"nbsphinx": "hidden"
},
@@ -112,10 +112,10 @@
"execution_count": 2,
"metadata": {
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- "iopub.status.busy": "2024-09-26T16:58:56.469465Z",
- "iopub.status.idle": "2024-09-26T16:58:56.473173Z",
- "shell.execute_reply": "2024-09-26T16:58:56.472707Z"
+ "iopub.execute_input": "2024-09-27T13:45:22.259042Z",
+ "iopub.status.busy": "2024-09-27T13:45:22.258751Z",
+ "iopub.status.idle": "2024-09-27T13:45:22.262361Z",
+ "shell.execute_reply": "2024-09-27T13:45:22.261892Z"
}
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"outputs": [],
@@ -152,17 +152,17 @@
"execution_count": 3,
"metadata": {
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- "shell.execute_reply": "2024-09-26T16:58:59.847018Z"
+ "iopub.execute_input": "2024-09-27T13:45:22.264021Z",
+ "iopub.status.busy": "2024-09-27T13:45:22.263690Z",
+ "iopub.status.idle": "2024-09-27T13:45:25.535718Z",
+ "shell.execute_reply": "2024-09-27T13:45:25.535139Z"
}
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"outputs": [
{
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- "model_id": "ed3e7469df2c4560897c195c6e1c0003",
+ "model_id": "e9fb2e15855a495eb8393c8b1c470abe",
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@@ -176,7 +176,7 @@
{
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+ "model_id": "62d0e0c88f1a4c2abca87123937bd572",
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@@ -190,7 +190,7 @@
{
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+ "model_id": "fca7e86a7eb34f15a6e35dfad2b37d04",
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"version_minor": 0
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@@ -204,7 +204,7 @@
{
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+ "model_id": "aea869f9cc8d44cf80997dc63f1b0a73",
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@@ -218,7 +218,7 @@
{
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+ "model_id": "907485478951427389e624de9ba0865d",
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@@ -260,10 +260,10 @@
"execution_count": 4,
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- "shell.execute_reply": "2024-09-26T16:58:59.852310Z"
+ "iopub.execute_input": "2024-09-27T13:45:25.537751Z",
+ "iopub.status.busy": "2024-09-27T13:45:25.537387Z",
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+ "shell.execute_reply": "2024-09-27T13:45:25.540977Z"
}
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"outputs": [
@@ -288,17 +288,17 @@
"execution_count": 5,
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+ "iopub.execute_input": "2024-09-27T13:45:25.543067Z",
+ "iopub.status.busy": "2024-09-27T13:45:25.542758Z",
+ "iopub.status.idle": "2024-09-27T13:45:36.948754Z",
+ "shell.execute_reply": "2024-09-27T13:45:36.948076Z"
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+ "model_id": "b9cec9f2501a478298bdf046984e17af",
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@@ -336,10 +336,10 @@
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- "shell.execute_reply": "2024-09-26T16:59:29.522532Z"
+ "iopub.execute_input": "2024-09-27T13:45:36.951145Z",
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+ "shell.execute_reply": "2024-09-27T13:45:55.343530Z"
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@@ -372,10 +372,10 @@
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- "shell.execute_reply": "2024-09-26T16:59:29.530434Z"
+ "iopub.execute_input": "2024-09-27T13:45:55.346499Z",
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+ "shell.execute_reply": "2024-09-27T13:45:55.350506Z"
}
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@@ -413,10 +413,10 @@
"execution_count": 8,
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- "shell.execute_reply": "2024-09-26T16:59:29.535767Z"
+ "iopub.execute_input": "2024-09-27T13:45:55.352921Z",
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+ "shell.execute_reply": "2024-09-27T13:45:55.356311Z"
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@@ -553,10 +553,10 @@
"execution_count": 9,
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+ "iopub.execute_input": "2024-09-27T13:45:55.358366Z",
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@@ -681,10 +681,10 @@
"execution_count": 10,
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- "shell.execute_reply": "2024-09-26T16:59:29.575747Z"
+ "iopub.execute_input": "2024-09-27T13:45:55.368819Z",
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+ "shell.execute_reply": "2024-09-27T13:45:55.406716Z"
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@@ -721,10 +721,10 @@
"execution_count": 11,
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- "iopub.status.idle": "2024-09-26T17:00:03.433901Z",
- "shell.execute_reply": "2024-09-26T17:00:03.433237Z"
+ "iopub.execute_input": "2024-09-27T13:45:55.409382Z",
+ "iopub.status.busy": "2024-09-27T13:45:55.408920Z",
+ "iopub.status.idle": "2024-09-27T13:46:29.730340Z",
+ "shell.execute_reply": "2024-09-27T13:46:29.729712Z"
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"outputs": [
@@ -740,21 +740,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.020\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.049\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.710\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.896\n",
"Computing feature embeddings ...\n"
]
},
{
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@@ -775,7 +775,7 @@
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@@ -798,21 +798,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.163\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.144\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.662\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.758\n",
"Computing feature embeddings ...\n"
]
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{
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+ "model_id": "8587b883949a4e399dabc4f91c49eb97",
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@@ -833,7 +833,7 @@
{
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+ "model_id": "c217771fa5814aabb7107510b1d6e6a8",
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"version_minor": 0
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@@ -856,21 +856,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.968\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.120\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.706\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.781\n",
"Computing feature embeddings ...\n"
]
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@@ -891,7 +891,7 @@
{
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"version_minor": 0
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@@ -970,10 +970,10 @@
"execution_count": 12,
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- "iopub.status.idle": "2024-09-26T17:00:03.452443Z",
- "shell.execute_reply": "2024-09-26T17:00:03.452024Z"
+ "iopub.execute_input": "2024-09-27T13:46:29.732349Z",
+ "iopub.status.busy": "2024-09-27T13:46:29.732107Z",
+ "iopub.status.idle": "2024-09-27T13:46:29.748596Z",
+ "shell.execute_reply": "2024-09-27T13:46:29.748051Z"
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@@ -998,10 +998,10 @@
"execution_count": 13,
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- "iopub.execute_input": "2024-09-26T17:00:03.454156Z",
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- "shell.execute_reply": "2024-09-26T17:00:03.922671Z"
+ "iopub.execute_input": "2024-09-27T13:46:29.750480Z",
+ "iopub.status.busy": "2024-09-27T13:46:29.750177Z",
+ "iopub.status.idle": "2024-09-27T13:46:30.218781Z",
+ "shell.execute_reply": "2024-09-27T13:46:30.218120Z"
}
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"outputs": [],
@@ -1021,10 +1021,10 @@
"execution_count": 14,
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- "iopub.execute_input": "2024-09-26T17:00:03.925208Z",
- "iopub.status.busy": "2024-09-26T17:00:03.924815Z",
- "iopub.status.idle": "2024-09-26T17:01:55.216532Z",
- "shell.execute_reply": "2024-09-26T17:01:55.215848Z"
+ "iopub.execute_input": "2024-09-27T13:46:30.220918Z",
+ "iopub.status.busy": "2024-09-27T13:46:30.220731Z",
+ "iopub.status.idle": "2024-09-27T13:48:21.510252Z",
+ "shell.execute_reply": "2024-09-27T13:48:21.509624Z"
}
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"outputs": [
@@ -1063,7 +1063,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "20ae57fa05ee4e83901a856b849b3891",
+ "model_id": "9b584fe98d9c4efaa2b4e34b431444f0",
"version_major": 2,
"version_minor": 0
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@@ -1109,10 +1109,10 @@
"execution_count": 15,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:01:55.218688Z",
- "iopub.status.busy": "2024-09-26T17:01:55.218316Z",
- "iopub.status.idle": "2024-09-26T17:01:55.686428Z",
- "shell.execute_reply": "2024-09-26T17:01:55.685792Z"
+ "iopub.execute_input": "2024-09-27T13:48:21.512469Z",
+ "iopub.status.busy": "2024-09-27T13:48:21.511882Z",
+ "iopub.status.idle": "2024-09-27T13:48:21.969651Z",
+ "shell.execute_reply": "2024-09-27T13:48:21.969088Z"
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"outputs": [
@@ -1258,10 +1258,10 @@
"execution_count": 16,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:01:55.688736Z",
- "iopub.status.busy": "2024-09-26T17:01:55.688529Z",
- "iopub.status.idle": "2024-09-26T17:01:55.750648Z",
- "shell.execute_reply": "2024-09-26T17:01:55.750042Z"
+ "iopub.execute_input": "2024-09-27T13:48:21.971962Z",
+ "iopub.status.busy": "2024-09-27T13:48:21.971637Z",
+ "iopub.status.idle": "2024-09-27T13:48:22.033131Z",
+ "shell.execute_reply": "2024-09-27T13:48:22.032635Z"
}
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"outputs": [
@@ -1365,10 +1365,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
index 169e30683..61c139cf0 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\n",
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- "iopub.status.idle": "2024-09-26T17:02:02.875172Z",
- "shell.execute_reply": "2024-09-26T17:02:02.874705Z"
+ "iopub.execute_input": "2024-09-27T13:48:29.911776Z",
+ "iopub.status.busy": "2024-09-27T13:48:29.911485Z",
+ "iopub.status.idle": "2024-09-27T13:48:29.929829Z",
+ "shell.execute_reply": "2024-09-27T13:48:29.929260Z"
}
},
"outputs": [],
@@ -154,10 +154,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:02.877142Z",
- "iopub.status.busy": "2024-09-26T17:02:02.876721Z",
- "iopub.status.idle": "2024-09-26T17:02:02.901260Z",
- "shell.execute_reply": "2024-09-26T17:02:02.900803Z"
+ "iopub.execute_input": "2024-09-27T13:48:29.931726Z",
+ "iopub.status.busy": "2024-09-27T13:48:29.931354Z",
+ "iopub.status.idle": "2024-09-27T13:48:29.955883Z",
+ "shell.execute_reply": "2024-09-27T13:48:29.955429Z"
}
},
"outputs": [
@@ -264,10 +264,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:02.903032Z",
- "iopub.status.busy": "2024-09-26T17:02:02.902670Z",
- "iopub.status.idle": "2024-09-26T17:02:02.906081Z",
- "shell.execute_reply": "2024-09-26T17:02:02.905633Z"
+ "iopub.execute_input": "2024-09-27T13:48:29.957546Z",
+ "iopub.status.busy": "2024-09-27T13:48:29.957198Z",
+ "iopub.status.idle": "2024-09-27T13:48:29.960644Z",
+ "shell.execute_reply": "2024-09-27T13:48:29.960187Z"
}
},
"outputs": [],
@@ -288,10 +288,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:02.907891Z",
- "iopub.status.busy": "2024-09-26T17:02:02.907547Z",
- "iopub.status.idle": "2024-09-26T17:02:02.915059Z",
- "shell.execute_reply": "2024-09-26T17:02:02.914598Z"
+ "iopub.execute_input": "2024-09-27T13:48:29.962526Z",
+ "iopub.status.busy": "2024-09-27T13:48:29.962099Z",
+ "iopub.status.idle": "2024-09-27T13:48:29.970289Z",
+ "shell.execute_reply": "2024-09-27T13:48:29.969831Z"
}
},
"outputs": [],
@@ -336,10 +336,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:02.916797Z",
- "iopub.status.busy": "2024-09-26T17:02:02.916457Z",
- "iopub.status.idle": "2024-09-26T17:02:02.918910Z",
- "shell.execute_reply": "2024-09-26T17:02:02.918455Z"
+ "iopub.execute_input": "2024-09-27T13:48:29.972004Z",
+ "iopub.status.busy": "2024-09-27T13:48:29.971668Z",
+ "iopub.status.idle": "2024-09-27T13:48:29.974120Z",
+ "shell.execute_reply": "2024-09-27T13:48:29.973664Z"
}
},
"outputs": [],
@@ -362,10 +362,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:02.920658Z",
- "iopub.status.busy": "2024-09-26T17:02:02.920329Z",
- "iopub.status.idle": "2024-09-26T17:02:05.951867Z",
- "shell.execute_reply": "2024-09-26T17:02:05.951334Z"
+ "iopub.execute_input": "2024-09-27T13:48:29.975796Z",
+ "iopub.status.busy": "2024-09-27T13:48:29.975523Z",
+ "iopub.status.idle": "2024-09-27T13:48:33.022239Z",
+ "shell.execute_reply": "2024-09-27T13:48:33.021576Z"
}
},
"outputs": [],
@@ -401,10 +401,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:05.953863Z",
- "iopub.status.busy": "2024-09-26T17:02:05.953664Z",
- "iopub.status.idle": "2024-09-26T17:02:05.962841Z",
- "shell.execute_reply": "2024-09-26T17:02:05.962408Z"
+ "iopub.execute_input": "2024-09-27T13:48:33.024546Z",
+ "iopub.status.busy": "2024-09-27T13:48:33.024174Z",
+ "iopub.status.idle": "2024-09-27T13:48:33.033530Z",
+ "shell.execute_reply": "2024-09-27T13:48:33.033087Z"
}
},
"outputs": [],
@@ -436,10 +436,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:05.964552Z",
- "iopub.status.busy": "2024-09-26T17:02:05.964224Z",
- "iopub.status.idle": "2024-09-26T17:02:07.908703Z",
- "shell.execute_reply": "2024-09-26T17:02:07.908090Z"
+ "iopub.execute_input": "2024-09-27T13:48:33.035207Z",
+ "iopub.status.busy": "2024-09-27T13:48:33.035031Z",
+ "iopub.status.idle": "2024-09-27T13:48:35.057425Z",
+ "shell.execute_reply": "2024-09-27T13:48:35.056829Z"
}
},
"outputs": [
@@ -476,10 +476,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:07.910953Z",
- "iopub.status.busy": "2024-09-26T17:02:07.910373Z",
- "iopub.status.idle": "2024-09-26T17:02:07.928712Z",
- "shell.execute_reply": "2024-09-26T17:02:07.928235Z"
+ "iopub.execute_input": "2024-09-27T13:48:35.059795Z",
+ "iopub.status.busy": "2024-09-27T13:48:35.059229Z",
+ "iopub.status.idle": "2024-09-27T13:48:35.078592Z",
+ "shell.execute_reply": "2024-09-27T13:48:35.078085Z"
},
"scrolled": true
},
@@ -609,10 +609,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:07.930415Z",
- "iopub.status.busy": "2024-09-26T17:02:07.930091Z",
- "iopub.status.idle": "2024-09-26T17:02:07.937827Z",
- "shell.execute_reply": "2024-09-26T17:02:07.937268Z"
+ "iopub.execute_input": "2024-09-27T13:48:35.080517Z",
+ "iopub.status.busy": "2024-09-27T13:48:35.080147Z",
+ "iopub.status.idle": "2024-09-27T13:48:35.088054Z",
+ "shell.execute_reply": "2024-09-27T13:48:35.087581Z"
}
},
"outputs": [
@@ -716,10 +716,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:07.939627Z",
- "iopub.status.busy": "2024-09-26T17:02:07.939288Z",
- "iopub.status.idle": "2024-09-26T17:02:07.948313Z",
- "shell.execute_reply": "2024-09-26T17:02:07.947729Z"
+ "iopub.execute_input": "2024-09-27T13:48:35.089935Z",
+ "iopub.status.busy": "2024-09-27T13:48:35.089521Z",
+ "iopub.status.idle": "2024-09-27T13:48:35.098940Z",
+ "shell.execute_reply": "2024-09-27T13:48:35.098374Z"
}
},
"outputs": [
@@ -848,10 +848,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:07.950161Z",
- "iopub.status.busy": "2024-09-26T17:02:07.949841Z",
- "iopub.status.idle": "2024-09-26T17:02:07.957648Z",
- "shell.execute_reply": "2024-09-26T17:02:07.957041Z"
+ "iopub.execute_input": "2024-09-27T13:48:35.100861Z",
+ "iopub.status.busy": "2024-09-27T13:48:35.100449Z",
+ "iopub.status.idle": "2024-09-27T13:48:35.108869Z",
+ "shell.execute_reply": "2024-09-27T13:48:35.108267Z"
}
},
"outputs": [
@@ -965,10 +965,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:07.959493Z",
- "iopub.status.busy": "2024-09-26T17:02:07.959139Z",
- "iopub.status.idle": "2024-09-26T17:02:07.969567Z",
- "shell.execute_reply": "2024-09-26T17:02:07.968948Z"
+ "iopub.execute_input": "2024-09-27T13:48:35.110735Z",
+ "iopub.status.busy": "2024-09-27T13:48:35.110393Z",
+ "iopub.status.idle": "2024-09-27T13:48:35.119177Z",
+ "shell.execute_reply": "2024-09-27T13:48:35.118615Z"
}
},
"outputs": [
@@ -1079,10 +1079,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:07.971246Z",
- "iopub.status.busy": "2024-09-26T17:02:07.971075Z",
- "iopub.status.idle": "2024-09-26T17:02:07.978570Z",
- "shell.execute_reply": "2024-09-26T17:02:07.978076Z"
+ "iopub.execute_input": "2024-09-27T13:48:35.120900Z",
+ "iopub.status.busy": "2024-09-27T13:48:35.120578Z",
+ "iopub.status.idle": "2024-09-27T13:48:35.128239Z",
+ "shell.execute_reply": "2024-09-27T13:48:35.127660Z"
}
},
"outputs": [
@@ -1197,10 +1197,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:07.980461Z",
- "iopub.status.busy": "2024-09-26T17:02:07.980085Z",
- "iopub.status.idle": "2024-09-26T17:02:07.988648Z",
- "shell.execute_reply": "2024-09-26T17:02:07.988195Z"
+ "iopub.execute_input": "2024-09-27T13:48:35.130045Z",
+ "iopub.status.busy": "2024-09-27T13:48:35.129690Z",
+ "iopub.status.idle": "2024-09-27T13:48:35.137653Z",
+ "shell.execute_reply": "2024-09-27T13:48:35.137215Z"
}
},
"outputs": [
@@ -1306,10 +1306,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:07.990420Z",
- "iopub.status.busy": "2024-09-26T17:02:07.990085Z",
- "iopub.status.idle": "2024-09-26T17:02:07.998038Z",
- "shell.execute_reply": "2024-09-26T17:02:07.997573Z"
+ "iopub.execute_input": "2024-09-27T13:48:35.139474Z",
+ "iopub.status.busy": "2024-09-27T13:48:35.139126Z",
+ "iopub.status.idle": "2024-09-27T13:48:35.147699Z",
+ "shell.execute_reply": "2024-09-27T13:48:35.147238Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
index 343f76c9a..b10d7534a 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
@@ -75,10 +75,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:10.815001Z",
- "iopub.status.busy": "2024-09-26T17:02:10.814842Z",
- "iopub.status.idle": "2024-09-26T17:02:13.720806Z",
- "shell.execute_reply": "2024-09-26T17:02:13.720189Z"
+ "iopub.execute_input": "2024-09-27T13:48:38.123019Z",
+ "iopub.status.busy": "2024-09-27T13:48:38.122617Z",
+ "iopub.status.idle": "2024-09-27T13:48:41.156902Z",
+ "shell.execute_reply": "2024-09-27T13:48:41.156293Z"
},
"nbsphinx": "hidden"
},
@@ -96,7 +96,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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": "2024-09-26T17:02:13.723175Z",
- "iopub.status.busy": "2024-09-26T17:02:13.722674Z",
- "iopub.status.idle": "2024-09-26T17:02:13.725899Z",
- "shell.execute_reply": "2024-09-26T17:02:13.725444Z"
+ "iopub.execute_input": "2024-09-27T13:48:41.159363Z",
+ "iopub.status.busy": "2024-09-27T13:48:41.158760Z",
+ "iopub.status.idle": "2024-09-27T13:48:41.162218Z",
+ "shell.execute_reply": "2024-09-27T13:48:41.161666Z"
}
},
"outputs": [],
@@ -145,10 +145,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:13.727650Z",
- "iopub.status.busy": "2024-09-26T17:02:13.727301Z",
- "iopub.status.idle": "2024-09-26T17:02:13.730292Z",
- "shell.execute_reply": "2024-09-26T17:02:13.729849Z"
+ "iopub.execute_input": "2024-09-27T13:48:41.164034Z",
+ "iopub.status.busy": "2024-09-27T13:48:41.163676Z",
+ "iopub.status.idle": "2024-09-27T13:48:41.166941Z",
+ "shell.execute_reply": "2024-09-27T13:48:41.166439Z"
},
"nbsphinx": "hidden"
},
@@ -178,10 +178,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:13.731901Z",
- "iopub.status.busy": "2024-09-26T17:02:13.731621Z",
- "iopub.status.idle": "2024-09-26T17:02:13.756874Z",
- "shell.execute_reply": "2024-09-26T17:02:13.756312Z"
+ "iopub.execute_input": "2024-09-27T13:48:41.168602Z",
+ "iopub.status.busy": "2024-09-27T13:48:41.168322Z",
+ "iopub.status.idle": "2024-09-27T13:48:41.194210Z",
+ "shell.execute_reply": "2024-09-27T13:48:41.193635Z"
}
},
"outputs": [
@@ -271,10 +271,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:13.758691Z",
- "iopub.status.busy": "2024-09-26T17:02:13.758260Z",
- "iopub.status.idle": "2024-09-26T17:02:13.761816Z",
- "shell.execute_reply": "2024-09-26T17:02:13.761257Z"
+ "iopub.execute_input": "2024-09-27T13:48:41.196238Z",
+ "iopub.status.busy": "2024-09-27T13:48:41.195805Z",
+ "iopub.status.idle": "2024-09-27T13:48:41.199937Z",
+ "shell.execute_reply": "2024-09-27T13:48:41.199359Z"
}
},
"outputs": [
@@ -283,7 +283,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'visa_or_mastercard', 'getting_spare_card', 'change_pin', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'card_about_to_expire', 'cancel_transfer', 'card_payment_fee_charged'}\n"
+ "Classes: {'supported_cards_and_currencies', 'cancel_transfer', 'card_about_to_expire', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'change_pin', 'apple_pay_or_google_pay', 'getting_spare_card'}\n"
]
}
],
@@ -307,10 +307,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:13.763566Z",
- "iopub.status.busy": "2024-09-26T17:02:13.763110Z",
- "iopub.status.idle": "2024-09-26T17:02:13.766236Z",
- "shell.execute_reply": "2024-09-26T17:02:13.765787Z"
+ "iopub.execute_input": "2024-09-27T13:48:41.201902Z",
+ "iopub.status.busy": "2024-09-27T13:48:41.201575Z",
+ "iopub.status.idle": "2024-09-27T13:48:41.204610Z",
+ "shell.execute_reply": "2024-09-27T13:48:41.204162Z"
}
},
"outputs": [
@@ -365,10 +365,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:13.767931Z",
- "iopub.status.busy": "2024-09-26T17:02:13.767610Z",
- "iopub.status.idle": "2024-09-26T17:02:17.637562Z",
- "shell.execute_reply": "2024-09-26T17:02:17.636903Z"
+ "iopub.execute_input": "2024-09-27T13:48:41.206413Z",
+ "iopub.status.busy": "2024-09-27T13:48:41.206079Z",
+ "iopub.status.idle": "2024-09-27T13:48:45.163696Z",
+ "shell.execute_reply": "2024-09-27T13:48:45.163141Z"
}
},
"outputs": [
@@ -416,10 +416,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:17.639989Z",
- "iopub.status.busy": "2024-09-26T17:02:17.639568Z",
- "iopub.status.idle": "2024-09-26T17:02:18.534074Z",
- "shell.execute_reply": "2024-09-26T17:02:18.533483Z"
+ "iopub.execute_input": "2024-09-27T13:48:45.165987Z",
+ "iopub.status.busy": "2024-09-27T13:48:45.165561Z",
+ "iopub.status.idle": "2024-09-27T13:48:46.068707Z",
+ "shell.execute_reply": "2024-09-27T13:48:46.068104Z"
},
"scrolled": true
},
@@ -451,10 +451,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:18.536535Z",
- "iopub.status.busy": "2024-09-26T17:02:18.536141Z",
- "iopub.status.idle": "2024-09-26T17:02:18.539097Z",
- "shell.execute_reply": "2024-09-26T17:02:18.538594Z"
+ "iopub.execute_input": "2024-09-27T13:48:46.071667Z",
+ "iopub.status.busy": "2024-09-27T13:48:46.070894Z",
+ "iopub.status.idle": "2024-09-27T13:48:46.074612Z",
+ "shell.execute_reply": "2024-09-27T13:48:46.074101Z"
}
},
"outputs": [],
@@ -474,10 +474,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:18.541035Z",
- "iopub.status.busy": "2024-09-26T17:02:18.540659Z",
- "iopub.status.idle": "2024-09-26T17:02:20.483284Z",
- "shell.execute_reply": "2024-09-26T17:02:20.482560Z"
+ "iopub.execute_input": "2024-09-27T13:48:46.077488Z",
+ "iopub.status.busy": "2024-09-27T13:48:46.076743Z",
+ "iopub.status.idle": "2024-09-27T13:48:48.102638Z",
+ "shell.execute_reply": "2024-09-27T13:48:48.101922Z"
},
"scrolled": true
},
@@ -521,10 +521,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:20.486447Z",
- "iopub.status.busy": "2024-09-26T17:02:20.486003Z",
- "iopub.status.idle": "2024-09-26T17:02:20.511442Z",
- "shell.execute_reply": "2024-09-26T17:02:20.510928Z"
+ "iopub.execute_input": "2024-09-27T13:48:48.106046Z",
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+ "shell.execute_reply": "2024-09-27T13:48:48.130366Z"
},
"scrolled": true
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@@ -654,10 +654,10 @@
"execution_count": 12,
"metadata": {
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- "iopub.status.busy": "2024-09-26T17:02:20.513683Z",
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- "shell.execute_reply": "2024-09-26T17:02:20.523745Z"
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+ "shell.execute_reply": "2024-09-27T13:48:48.143089Z"
},
"scrolled": true
},
@@ -767,10 +767,10 @@
"execution_count": 13,
"metadata": {
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- "iopub.status.busy": "2024-09-26T17:02:20.525788Z",
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- "shell.execute_reply": "2024-09-26T17:02:20.529997Z"
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+ "shell.execute_reply": "2024-09-27T13:48:48.148851Z"
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"outputs": [
@@ -808,10 +808,10 @@
"execution_count": 14,
"metadata": {
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"outputs": [
@@ -928,10 +928,10 @@
"execution_count": 15,
"metadata": {
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"outputs": [
@@ -1014,10 +1014,10 @@
"execution_count": 16,
"metadata": {
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"outputs": [
@@ -1125,10 +1125,10 @@
"execution_count": 17,
"metadata": {
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"outputs": [
@@ -1239,10 +1239,10 @@
"execution_count": 18,
"metadata": {
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"outputs": [
@@ -1310,10 +1310,10 @@
"execution_count": 19,
"metadata": {
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"outputs": [
@@ -1392,10 +1392,10 @@
"execution_count": 20,
"metadata": {
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@@ -1449,10 +1449,10 @@
"execution_count": 21,
"metadata": {
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},
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},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
index 0841a1abf..a1e0d1e42 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
@@ -38,10 +38,10 @@
"execution_count": 1,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:02:24.778635Z",
- "iopub.status.busy": "2024-09-26T17:02:24.778451Z",
- "iopub.status.idle": "2024-09-26T17:02:25.474946Z",
- "shell.execute_reply": "2024-09-26T17:02:25.474332Z"
+ "iopub.execute_input": "2024-09-27T13:48:51.496056Z",
+ "iopub.status.busy": "2024-09-27T13:48:51.495876Z",
+ "iopub.status.idle": "2024-09-27T13:48:52.184420Z",
+ "shell.execute_reply": "2024-09-27T13:48:52.183872Z"
}
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"outputs": [],
@@ -87,10 +87,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:25.477084Z",
- "iopub.status.busy": "2024-09-26T17:02:25.476819Z",
- "iopub.status.idle": "2024-09-26T17:02:25.608315Z",
- "shell.execute_reply": "2024-09-26T17:02:25.607729Z"
+ "iopub.execute_input": "2024-09-27T13:48:52.186744Z",
+ "iopub.status.busy": "2024-09-27T13:48:52.186314Z",
+ "iopub.status.idle": "2024-09-27T13:48:52.317662Z",
+ "shell.execute_reply": "2024-09-27T13:48:52.317086Z"
}
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"outputs": [
@@ -181,10 +181,10 @@
"execution_count": 3,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:02:25.610318Z",
- "iopub.status.busy": "2024-09-26T17:02:25.609878Z",
- "iopub.status.idle": "2024-09-26T17:02:25.633373Z",
- "shell.execute_reply": "2024-09-26T17:02:25.632806Z"
+ "iopub.execute_input": "2024-09-27T13:48:52.319925Z",
+ "iopub.status.busy": "2024-09-27T13:48:52.319422Z",
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+ "shell.execute_reply": "2024-09-27T13:48:52.342381Z"
}
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"outputs": [],
@@ -210,10 +210,10 @@
"execution_count": 4,
"metadata": {
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- "shell.execute_reply": "2024-09-26T17:02:28.160724Z"
+ "iopub.execute_input": "2024-09-27T13:48:52.345328Z",
+ "iopub.status.busy": "2024-09-27T13:48:52.344798Z",
+ "iopub.status.idle": "2024-09-27T13:48:54.885695Z",
+ "shell.execute_reply": "2024-09-27T13:48:54.885077Z"
}
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"outputs": [
@@ -700,10 +700,10 @@
"execution_count": 5,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:02:28.163661Z",
- "iopub.status.busy": "2024-09-26T17:02:28.163055Z",
- "iopub.status.idle": "2024-09-26T17:02:36.892887Z",
- "shell.execute_reply": "2024-09-26T17:02:36.892386Z"
+ "iopub.execute_input": "2024-09-27T13:48:54.888111Z",
+ "iopub.status.busy": "2024-09-27T13:48:54.887560Z",
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+ "shell.execute_reply": "2024-09-27T13:49:03.630566Z"
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"outputs": [
@@ -804,10 +804,10 @@
"execution_count": 6,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:02:36.894869Z",
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- "shell.execute_reply": "2024-09-26T17:02:37.053318Z"
+ "iopub.execute_input": "2024-09-27T13:49:03.633038Z",
+ "iopub.status.busy": "2024-09-27T13:49:03.632663Z",
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+ "shell.execute_reply": "2024-09-27T13:49:03.794905Z"
}
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"outputs": [],
@@ -838,10 +838,10 @@
"execution_count": 7,
"metadata": {
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- "shell.execute_reply": "2024-09-26T17:02:38.527389Z"
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+ "shell.execute_reply": "2024-09-27T13:49:05.325634Z"
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"outputs": [
@@ -1000,10 +1000,10 @@
"execution_count": 8,
"metadata": {
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- "shell.execute_reply": "2024-09-26T17:02:39.099974Z"
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+ "shell.execute_reply": "2024-09-27T13:49:05.849331Z"
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"outputs": [
@@ -1082,10 +1082,10 @@
"execution_count": 9,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:02:39.102597Z",
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- "shell.execute_reply": "2024-09-26T17:02:39.115754Z"
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"outputs": [],
@@ -1115,10 +1115,10 @@
"execution_count": 10,
"metadata": {
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- "shell.execute_reply": "2024-09-26T17:02:39.136335Z"
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+ "shell.execute_reply": "2024-09-27T13:49:05.885683Z"
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"outputs": [],
@@ -1146,10 +1146,10 @@
"execution_count": 11,
"metadata": {
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- "shell.execute_reply": "2024-09-26T17:02:39.385663Z"
+ "iopub.execute_input": "2024-09-27T13:49:05.888344Z",
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+ "shell.execute_reply": "2024-09-27T13:49:06.117350Z"
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"outputs": [],
@@ -1189,10 +1189,10 @@
"execution_count": 12,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:02:39.388687Z",
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- "shell.execute_reply": "2024-09-26T17:02:39.406806Z"
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+ "shell.execute_reply": "2024-09-27T13:49:06.138785Z"
}
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"outputs": [
@@ -1390,10 +1390,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:02:39.409088Z",
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- "shell.execute_reply": "2024-09-26T17:02:39.577160Z"
+ "iopub.execute_input": "2024-09-27T13:49:06.140987Z",
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+ "shell.execute_reply": "2024-09-27T13:49:06.309966Z"
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"outputs": [
@@ -1460,10 +1460,10 @@
"execution_count": 14,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:02:39.579768Z",
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- "shell.execute_reply": "2024-09-26T17:02:39.589043Z"
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+ "shell.execute_reply": "2024-09-27T13:49:06.322028Z"
}
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"outputs": [
@@ -1729,10 +1729,10 @@
"execution_count": 15,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:02:39.591438Z",
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}
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"outputs": [
@@ -1919,10 +1919,10 @@
"execution_count": 16,
"metadata": {
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"outputs": [],
@@ -1956,10 +1956,10 @@
"execution_count": 17,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:02:39.631810Z",
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@@ -1981,10 +1981,10 @@
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- "Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.\r\n",
- "HTTP request sent, awaiting response... 200 OK\r\n",
+ "--2024-09-27 13:49:07-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n",
+ "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.108.153, 185.199.110.153, 185.199.111.153, ...\r\n",
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@@ -4130,35 +4137,35 @@
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@@ -5271,7 +5260,7 @@
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@@ -5372,6 +5338,47 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
index 0f1a9673c..7a4a275e8 100644
--- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
@@ -70,10 +70,10 @@
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@@ -85,7 +85,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -285,10 +285,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
index edab415e1..5566fbc6f 100644
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@@ -137,10 +137,10 @@
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@@ -253,10 +253,10 @@
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@@ -278,10 +278,10 @@
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@@ -2171,7 +2187,7 @@
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@@ -2324,22 +2293,53 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb
index ade9796c5..cc4cd8fd6 100644
--- a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb
@@ -60,10 +60,10 @@
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@@ -73,7 +73,7 @@
"dependencies = [\"cleanlab\", \"xgboost\", \"datasets\"]\n",
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -968,10 +968,10 @@
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- "shell.execute_reply": "2024-09-26T17:03:09.751197Z"
+ "iopub.execute_input": "2024-09-27T13:49:37.737395Z",
+ "iopub.status.busy": "2024-09-27T13:49:37.737079Z",
+ "iopub.status.idle": "2024-09-27T13:49:37.860657Z",
+ "shell.execute_reply": "2024-09-27T13:49:37.860138Z"
}
},
"outputs": [
@@ -1711,10 +1711,10 @@
"id": "044c0eb1-299a-4851-b1bf-268d5bce56c1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:09.753780Z",
- "iopub.status.busy": "2024-09-26T17:03:09.753473Z",
- "iopub.status.idle": "2024-09-26T17:03:09.763869Z",
- "shell.execute_reply": "2024-09-26T17:03:09.763379Z"
+ "iopub.execute_input": "2024-09-27T13:49:37.862530Z",
+ "iopub.status.busy": "2024-09-27T13:49:37.862209Z",
+ "iopub.status.idle": "2024-09-27T13:49:37.868653Z",
+ "shell.execute_reply": "2024-09-27T13:49:37.868170Z"
}
},
"outputs": [],
@@ -1738,10 +1738,10 @@
"id": "c43df278-abfe-40e5-9d48-2df3efea9379",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:09.765757Z",
- "iopub.status.busy": "2024-09-26T17:03:09.765399Z",
- "iopub.status.idle": "2024-09-26T17:03:11.745053Z",
- "shell.execute_reply": "2024-09-26T17:03:11.744416Z"
+ "iopub.execute_input": "2024-09-27T13:49:37.871135Z",
+ "iopub.status.busy": "2024-09-27T13:49:37.870425Z",
+ "iopub.status.idle": "2024-09-27T13:49:39.894594Z",
+ "shell.execute_reply": "2024-09-27T13:49:39.893912Z"
}
},
"outputs": [
@@ -1953,10 +1953,10 @@
"id": "77c7f776-54b3-45b5-9207-715d6d2e90c0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:11.747529Z",
- "iopub.status.busy": "2024-09-26T17:03:11.746976Z",
- "iopub.status.idle": "2024-09-26T17:03:11.760015Z",
- "shell.execute_reply": "2024-09-26T17:03:11.759505Z"
+ "iopub.execute_input": "2024-09-27T13:49:39.898305Z",
+ "iopub.status.busy": "2024-09-27T13:49:39.897216Z",
+ "iopub.status.idle": "2024-09-27T13:49:39.912953Z",
+ "shell.execute_reply": "2024-09-27T13:49:39.912405Z"
}
},
"outputs": [
@@ -2073,10 +2073,10 @@
"id": "7e218d04-0729-4f42-b264-51c73601ebe6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:11.762003Z",
- "iopub.status.busy": "2024-09-26T17:03:11.761617Z",
- "iopub.status.idle": "2024-09-26T17:03:11.764511Z",
- "shell.execute_reply": "2024-09-26T17:03:11.764016Z"
+ "iopub.execute_input": "2024-09-27T13:49:39.916108Z",
+ "iopub.status.busy": "2024-09-27T13:49:39.915341Z",
+ "iopub.status.idle": "2024-09-27T13:49:39.919141Z",
+ "shell.execute_reply": "2024-09-27T13:49:39.918633Z"
}
},
"outputs": [],
@@ -2090,10 +2090,10 @@
"id": "7e2bdb41-321e-4929-aa01-1f60948b9e8b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:11.766401Z",
- "iopub.status.busy": "2024-09-26T17:03:11.766021Z",
- "iopub.status.idle": "2024-09-26T17:03:11.770493Z",
- "shell.execute_reply": "2024-09-26T17:03:11.769981Z"
+ "iopub.execute_input": "2024-09-27T13:49:39.922032Z",
+ "iopub.status.busy": "2024-09-27T13:49:39.921249Z",
+ "iopub.status.idle": "2024-09-27T13:49:39.926585Z",
+ "shell.execute_reply": "2024-09-27T13:49:39.926073Z"
}
},
"outputs": [],
@@ -2117,10 +2117,10 @@
"id": "5ce2d89f-e832-448d-bfac-9941da15c895",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:11.772408Z",
- "iopub.status.busy": "2024-09-26T17:03:11.772028Z",
- "iopub.status.idle": "2024-09-26T17:03:11.807613Z",
- "shell.execute_reply": "2024-09-26T17:03:11.807086Z"
+ "iopub.execute_input": "2024-09-27T13:49:39.929683Z",
+ "iopub.status.busy": "2024-09-27T13:49:39.928826Z",
+ "iopub.status.idle": "2024-09-27T13:49:39.959231Z",
+ "shell.execute_reply": "2024-09-27T13:49:39.958525Z"
}
},
"outputs": [
@@ -2160,10 +2160,10 @@
"id": "9f437756-112e-4531-84fc-6ceadd0c9ef5",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:11.809511Z",
- "iopub.status.busy": "2024-09-26T17:03:11.809134Z",
- "iopub.status.idle": "2024-09-26T17:03:12.338745Z",
- "shell.execute_reply": "2024-09-26T17:03:12.338192Z"
+ "iopub.execute_input": "2024-09-27T13:49:39.961458Z",
+ "iopub.status.busy": "2024-09-27T13:49:39.961156Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.480334Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.479746Z"
}
},
"outputs": [],
@@ -2194,10 +2194,10 @@
"id": "707625f6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.341049Z",
- "iopub.status.busy": "2024-09-26T17:03:12.340668Z",
- "iopub.status.idle": "2024-09-26T17:03:12.477140Z",
- "shell.execute_reply": "2024-09-26T17:03:12.476474Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.483535Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.482730Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.622832Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.622203Z"
}
},
"outputs": [
@@ -2408,10 +2408,10 @@
"id": "25afe46c-a521-483c-b168-728c76d970dc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.480251Z",
- "iopub.status.busy": "2024-09-26T17:03:12.479457Z",
- "iopub.status.idle": "2024-09-26T17:03:12.487761Z",
- "shell.execute_reply": "2024-09-26T17:03:12.487250Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.625866Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.625069Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.633744Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.633225Z"
}
},
"outputs": [
@@ -2441,10 +2441,10 @@
"id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.490696Z",
- "iopub.status.busy": "2024-09-26T17:03:12.489940Z",
- "iopub.status.idle": "2024-09-26T17:03:12.497460Z",
- "shell.execute_reply": "2024-09-26T17:03:12.496930Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.636749Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.635964Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.644199Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.643667Z"
}
},
"outputs": [
@@ -2477,10 +2477,10 @@
"id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.500342Z",
- "iopub.status.busy": "2024-09-26T17:03:12.499582Z",
- "iopub.status.idle": "2024-09-26T17:03:12.506416Z",
- "shell.execute_reply": "2024-09-26T17:03:12.505903Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.647316Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.646527Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.653984Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.653441Z"
}
},
"outputs": [
@@ -2513,10 +2513,10 @@
"id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.509304Z",
- "iopub.status.busy": "2024-09-26T17:03:12.508532Z",
- "iopub.status.idle": "2024-09-26T17:03:12.514132Z",
- "shell.execute_reply": "2024-09-26T17:03:12.513613Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.656969Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.656207Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.662089Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.661547Z"
}
},
"outputs": [
@@ -2542,10 +2542,10 @@
"id": "08080458-0cd7-447d-80e6-384cb8d31eaf",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.516950Z",
- "iopub.status.busy": "2024-09-26T17:03:12.516208Z",
- "iopub.status.idle": "2024-09-26T17:03:12.521433Z",
- "shell.execute_reply": "2024-09-26T17:03:12.520973Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.664042Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.663625Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.668487Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.668031Z"
}
},
"outputs": [],
@@ -2569,10 +2569,10 @@
"id": "009bb215-4d26-47da-a230-d0ccf4122629",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.523760Z",
- "iopub.status.busy": "2024-09-26T17:03:12.523152Z",
- "iopub.status.idle": "2024-09-26T17:03:12.600802Z",
- "shell.execute_reply": "2024-09-26T17:03:12.600279Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.670335Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.670148Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.750849Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.750346Z"
}
},
"outputs": [
@@ -3052,10 +3052,10 @@
"id": "dcaeda51-9b24-4c04-889d-7e63563594fc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.602889Z",
- "iopub.status.busy": "2024-09-26T17:03:12.602578Z",
- "iopub.status.idle": "2024-09-26T17:03:12.611123Z",
- "shell.execute_reply": "2024-09-26T17:03:12.610650Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.752897Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.752625Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.761488Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.761000Z"
}
},
"outputs": [
@@ -3111,10 +3111,10 @@
"id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.613116Z",
- "iopub.status.busy": "2024-09-26T17:03:12.612781Z",
- "iopub.status.idle": "2024-09-26T17:03:12.616227Z",
- "shell.execute_reply": "2024-09-26T17:03:12.615762Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.763717Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.763403Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.766376Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.765784Z"
}
},
"outputs": [],
@@ -3150,10 +3150,10 @@
"id": "941ab2a6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.617893Z",
- "iopub.status.busy": "2024-09-26T17:03:12.617563Z",
- "iopub.status.idle": "2024-09-26T17:03:12.626852Z",
- "shell.execute_reply": "2024-09-26T17:03:12.626412Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.768367Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.767967Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.778395Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.777797Z"
}
},
"outputs": [],
@@ -3261,10 +3261,10 @@
"id": "50666fb9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.628682Z",
- "iopub.status.busy": "2024-09-26T17:03:12.628351Z",
- "iopub.status.idle": "2024-09-26T17:03:12.634666Z",
- "shell.execute_reply": "2024-09-26T17:03:12.634214Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.780256Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.779904Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.786750Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.786242Z"
},
"nbsphinx": "hidden"
},
@@ -3346,10 +3346,10 @@
"id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.636358Z",
- "iopub.status.busy": "2024-09-26T17:03:12.636021Z",
- "iopub.status.idle": "2024-09-26T17:03:12.639149Z",
- "shell.execute_reply": "2024-09-26T17:03:12.638707Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.788328Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.788149Z",
+ "iopub.status.idle": "2024-09-27T13:49:40.791623Z",
+ "shell.execute_reply": "2024-09-27T13:49:40.791149Z"
}
},
"outputs": [],
@@ -3373,10 +3373,10 @@
"id": "ce1c0ada-88b1-4654-b43f-3c0b59002979",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:12.640826Z",
- "iopub.status.busy": "2024-09-26T17:03:12.640500Z",
- "iopub.status.idle": "2024-09-26T17:03:16.698340Z",
- "shell.execute_reply": "2024-09-26T17:03:16.697800Z"
+ "iopub.execute_input": "2024-09-27T13:49:40.793340Z",
+ "iopub.status.busy": "2024-09-27T13:49:40.792984Z",
+ "iopub.status.idle": "2024-09-27T13:49:44.842085Z",
+ "shell.execute_reply": "2024-09-27T13:49:44.841530Z"
}
},
"outputs": [
@@ -3419,10 +3419,10 @@
"id": "3f572acf-31c3-4874-9100-451796e35b06",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:16.700392Z",
- "iopub.status.busy": "2024-09-26T17:03:16.700018Z",
- "iopub.status.idle": "2024-09-26T17:03:16.703134Z",
- "shell.execute_reply": "2024-09-26T17:03:16.702726Z"
+ "iopub.execute_input": "2024-09-27T13:49:44.844374Z",
+ "iopub.status.busy": "2024-09-27T13:49:44.843980Z",
+ "iopub.status.idle": "2024-09-27T13:49:44.847177Z",
+ "shell.execute_reply": "2024-09-27T13:49:44.846779Z"
}
},
"outputs": [
@@ -3460,10 +3460,10 @@
"id": "6a025a88",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:16.704961Z",
- "iopub.status.busy": "2024-09-26T17:03:16.704501Z",
- "iopub.status.idle": "2024-09-26T17:03:16.707303Z",
- "shell.execute_reply": "2024-09-26T17:03:16.706855Z"
+ "iopub.execute_input": "2024-09-27T13:49:44.848611Z",
+ "iopub.status.busy": "2024-09-27T13:49:44.848437Z",
+ "iopub.status.idle": "2024-09-27T13:49:44.851280Z",
+ "shell.execute_reply": "2024-09-27T13:49:44.850836Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
index 79a94be48..3db453a12 100644
--- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
@@ -53,10 +53,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:19.757523Z",
- "iopub.status.busy": "2024-09-26T17:03:19.757004Z",
- "iopub.status.idle": "2024-09-26T17:03:20.993835Z",
- "shell.execute_reply": "2024-09-26T17:03:20.993263Z"
+ "iopub.execute_input": "2024-09-27T13:49:47.955203Z",
+ "iopub.status.busy": "2024-09-27T13:49:47.954713Z",
+ "iopub.status.idle": "2024-09-27T13:49:49.202564Z",
+ "shell.execute_reply": "2024-09-27T13:49:49.201984Z"
},
"nbsphinx": "hidden"
},
@@ -68,7 +68,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -95,10 +95,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:20.995910Z",
- "iopub.status.busy": "2024-09-26T17:03:20.995628Z",
- "iopub.status.idle": "2024-09-26T17:03:21.177031Z",
- "shell.execute_reply": "2024-09-26T17:03:21.176503Z"
+ "iopub.execute_input": "2024-09-27T13:49:49.204710Z",
+ "iopub.status.busy": "2024-09-27T13:49:49.204267Z",
+ "iopub.status.idle": "2024-09-27T13:49:49.385602Z",
+ "shell.execute_reply": "2024-09-27T13:49:49.385040Z"
},
"id": "avXlHJcXjruP"
},
@@ -234,10 +234,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:21.179276Z",
- "iopub.status.busy": "2024-09-26T17:03:21.178912Z",
- "iopub.status.idle": "2024-09-26T17:03:21.190441Z",
- "shell.execute_reply": "2024-09-26T17:03:21.189979Z"
+ "iopub.execute_input": "2024-09-27T13:49:49.387603Z",
+ "iopub.status.busy": "2024-09-27T13:49:49.387414Z",
+ "iopub.status.idle": "2024-09-27T13:49:49.399128Z",
+ "shell.execute_reply": "2024-09-27T13:49:49.398649Z"
},
"nbsphinx": "hidden"
},
@@ -340,10 +340,10 @@
"execution_count": 4,
"metadata": {
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- "shell.execute_reply": "2024-09-26T17:03:21.427841Z"
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+ "iopub.status.busy": "2024-09-27T13:49:49.400681Z",
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+ "shell.execute_reply": "2024-09-27T13:49:49.639632Z"
}
},
"outputs": [
@@ -393,10 +393,10 @@
"execution_count": 5,
"metadata": {
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+ "iopub.status.busy": "2024-09-27T13:49:49.641924Z",
+ "iopub.status.idle": "2024-09-27T13:49:49.668753Z",
+ "shell.execute_reply": "2024-09-27T13:49:49.668289Z"
}
},
"outputs": [],
@@ -428,10 +428,10 @@
"execution_count": 6,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:03:21.462907Z",
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- "shell.execute_reply": "2024-09-26T17:03:23.536960Z"
+ "iopub.execute_input": "2024-09-27T13:49:49.670405Z",
+ "iopub.status.busy": "2024-09-27T13:49:49.670225Z",
+ "iopub.status.idle": "2024-09-27T13:49:51.756166Z",
+ "shell.execute_reply": "2024-09-27T13:49:51.755567Z"
}
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"outputs": [
@@ -474,10 +474,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:23.540024Z",
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- "shell.execute_reply": "2024-09-26T17:03:23.557017Z"
+ "iopub.execute_input": "2024-09-27T13:49:51.758458Z",
+ "iopub.status.busy": "2024-09-27T13:49:51.757901Z",
+ "iopub.status.idle": "2024-09-27T13:49:51.776156Z",
+ "shell.execute_reply": "2024-09-27T13:49:51.775703Z"
},
"scrolled": true
},
@@ -607,10 +607,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:23.559289Z",
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- "shell.execute_reply": "2024-09-26T17:03:25.143145Z"
+ "iopub.execute_input": "2024-09-27T13:49:51.777989Z",
+ "iopub.status.busy": "2024-09-27T13:49:51.777684Z",
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+ "shell.execute_reply": "2024-09-27T13:49:53.369508Z"
},
"id": "AaHC5MRKjruT"
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@@ -729,10 +729,10 @@
"execution_count": 9,
"metadata": {
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- "shell.execute_reply": "2024-09-26T17:03:25.159091Z"
+ "iopub.execute_input": "2024-09-27T13:49:53.372684Z",
+ "iopub.status.busy": "2024-09-27T13:49:53.371801Z",
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"id": "Wy27rvyhjruU"
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@@ -781,10 +781,10 @@
"execution_count": 10,
"metadata": {
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- "iopub.status.busy": "2024-09-26T17:03:25.160870Z",
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- "shell.execute_reply": "2024-09-26T17:03:25.242905Z"
+ "iopub.execute_input": "2024-09-27T13:49:53.387980Z",
+ "iopub.status.busy": "2024-09-27T13:49:53.387516Z",
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+ "shell.execute_reply": "2024-09-27T13:49:53.473196Z"
},
"id": "Db8YHnyVjruU"
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@@ -891,10 +891,10 @@
"execution_count": 11,
"metadata": {
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- "iopub.execute_input": "2024-09-26T17:03:25.245411Z",
- "iopub.status.busy": "2024-09-26T17:03:25.245158Z",
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- "shell.execute_reply": "2024-09-26T17:03:25.459911Z"
+ "iopub.execute_input": "2024-09-27T13:49:53.475651Z",
+ "iopub.status.busy": "2024-09-27T13:49:53.475421Z",
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+ "shell.execute_reply": "2024-09-27T13:49:53.690348Z"
},
"id": "iJqAHuS2jruV"
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@@ -931,10 +931,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:25.462274Z",
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- "shell.execute_reply": "2024-09-26T17:03:25.478671Z"
+ "iopub.execute_input": "2024-09-27T13:49:53.692793Z",
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+ "shell.execute_reply": "2024-09-27T13:49:53.709980Z"
},
"id": "PcPTZ_JJG3Cx"
},
@@ -1400,10 +1400,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:25.480874Z",
- "iopub.status.busy": "2024-09-26T17:03:25.480537Z",
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- "shell.execute_reply": "2024-09-26T17:03:25.489515Z"
+ "iopub.execute_input": "2024-09-27T13:49:53.712127Z",
+ "iopub.status.busy": "2024-09-27T13:49:53.711811Z",
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+ "shell.execute_reply": "2024-09-27T13:49:53.720996Z"
},
"id": "0lonvOYvjruV"
},
@@ -1550,10 +1550,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:25.491899Z",
- "iopub.status.busy": "2024-09-26T17:03:25.491571Z",
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- "shell.execute_reply": "2024-09-26T17:03:25.587869Z"
+ "iopub.execute_input": "2024-09-27T13:49:53.723130Z",
+ "iopub.status.busy": "2024-09-27T13:49:53.722857Z",
+ "iopub.status.idle": "2024-09-27T13:49:53.817375Z",
+ "shell.execute_reply": "2024-09-27T13:49:53.816703Z"
},
"id": "MfqTCa3kjruV"
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@@ -1634,10 +1634,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:25.590607Z",
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- "iopub.status.idle": "2024-09-26T17:03:25.732483Z",
- "shell.execute_reply": "2024-09-26T17:03:25.731847Z"
+ "iopub.execute_input": "2024-09-27T13:49:53.819544Z",
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+ "shell.execute_reply": "2024-09-27T13:49:53.964595Z"
},
"id": "9ZtWAYXqMAPL"
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@@ -1697,10 +1697,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:25.734544Z",
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- "shell.execute_reply": "2024-09-26T17:03:25.737635Z"
+ "iopub.execute_input": "2024-09-27T13:49:53.967132Z",
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},
"id": "0rXP3ZPWjruW"
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@@ -1738,10 +1738,10 @@
"execution_count": 17,
"metadata": {
"execution": {
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+ "iopub.execute_input": "2024-09-27T13:49:53.972544Z",
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},
"id": "-iRPe8KXjruW"
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@@ -1796,10 +1796,10 @@
"execution_count": 18,
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-09-26T17:03:25.782255Z"
+ "iopub.execute_input": "2024-09-27T13:49:53.977483Z",
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},
"id": "ZpipUliyjruW"
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@@ -1850,10 +1850,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:25.784359Z",
- "iopub.status.busy": "2024-09-26T17:03:25.784047Z",
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- "shell.execute_reply": "2024-09-26T17:03:25.825399Z"
+ "iopub.execute_input": "2024-09-27T13:49:54.016309Z",
+ "iopub.status.busy": "2024-09-27T13:49:54.016153Z",
+ "iopub.status.idle": "2024-09-27T13:49:54.058592Z",
+ "shell.execute_reply": "2024-09-27T13:49:54.058128Z"
},
"id": "SLq-3q4xjruX"
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@@ -1922,10 +1922,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:25.827788Z",
- "iopub.status.busy": "2024-09-26T17:03:25.827395Z",
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- "shell.execute_reply": "2024-09-26T17:03:25.929087Z"
+ "iopub.execute_input": "2024-09-27T13:49:54.060328Z",
+ "iopub.status.busy": "2024-09-27T13:49:54.059989Z",
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+ "shell.execute_reply": "2024-09-27T13:49:54.161576Z"
},
"id": "g5LHhhuqFbXK"
},
@@ -1957,10 +1957,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:25.931922Z",
- "iopub.status.busy": "2024-09-26T17:03:25.931548Z",
- "iopub.status.idle": "2024-09-26T17:03:26.038825Z",
- "shell.execute_reply": "2024-09-26T17:03:26.038172Z"
+ "iopub.execute_input": "2024-09-27T13:49:54.164584Z",
+ "iopub.status.busy": "2024-09-27T13:49:54.164238Z",
+ "iopub.status.idle": "2024-09-27T13:49:54.272152Z",
+ "shell.execute_reply": "2024-09-27T13:49:54.271584Z"
},
"id": "p7w8F8ezBcet"
},
@@ -2017,10 +2017,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:26.040684Z",
- "iopub.status.busy": "2024-09-26T17:03:26.040446Z",
- "iopub.status.idle": "2024-09-26T17:03:26.253554Z",
- "shell.execute_reply": "2024-09-26T17:03:26.252936Z"
+ "iopub.execute_input": "2024-09-27T13:49:54.274181Z",
+ "iopub.status.busy": "2024-09-27T13:49:54.273772Z",
+ "iopub.status.idle": "2024-09-27T13:49:54.485007Z",
+ "shell.execute_reply": "2024-09-27T13:49:54.484491Z"
},
"id": "WETRL74tE_sU"
},
@@ -2055,10 +2055,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:26.255496Z",
- "iopub.status.busy": "2024-09-26T17:03:26.255135Z",
- "iopub.status.idle": "2024-09-26T17:03:26.476149Z",
- "shell.execute_reply": "2024-09-26T17:03:26.475467Z"
+ "iopub.execute_input": "2024-09-27T13:49:54.486961Z",
+ "iopub.status.busy": "2024-09-27T13:49:54.486600Z",
+ "iopub.status.idle": "2024-09-27T13:49:54.707860Z",
+ "shell.execute_reply": "2024-09-27T13:49:54.707182Z"
},
"id": "kCfdx2gOLmXS"
},
@@ -2220,10 +2220,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:26.478208Z",
- "iopub.status.busy": "2024-09-26T17:03:26.477882Z",
- "iopub.status.idle": "2024-09-26T17:03:26.484331Z",
- "shell.execute_reply": "2024-09-26T17:03:26.483897Z"
+ "iopub.execute_input": "2024-09-27T13:49:54.709923Z",
+ "iopub.status.busy": "2024-09-27T13:49:54.709461Z",
+ "iopub.status.idle": "2024-09-27T13:49:54.716021Z",
+ "shell.execute_reply": "2024-09-27T13:49:54.715572Z"
},
"id": "-uogYRWFYnuu"
},
@@ -2277,10 +2277,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:26.485988Z",
- "iopub.status.busy": "2024-09-26T17:03:26.485677Z",
- "iopub.status.idle": "2024-09-26T17:03:26.705415Z",
- "shell.execute_reply": "2024-09-26T17:03:26.704879Z"
+ "iopub.execute_input": "2024-09-27T13:49:54.717563Z",
+ "iopub.status.busy": "2024-09-27T13:49:54.717397Z",
+ "iopub.status.idle": "2024-09-27T13:49:54.936984Z",
+ "shell.execute_reply": "2024-09-27T13:49:54.936390Z"
},
"id": "pG-ljrmcYp9Q"
},
@@ -2327,10 +2327,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:26.707343Z",
- "iopub.status.busy": "2024-09-26T17:03:26.706986Z",
- "iopub.status.idle": "2024-09-26T17:03:27.770864Z",
- "shell.execute_reply": "2024-09-26T17:03:27.770364Z"
+ "iopub.execute_input": "2024-09-27T13:49:54.938887Z",
+ "iopub.status.busy": "2024-09-27T13:49:54.938530Z",
+ "iopub.status.idle": "2024-09-27T13:49:56.008191Z",
+ "shell.execute_reply": "2024-09-27T13:49:56.007633Z"
},
"id": "wL3ngCnuLEWd"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
index 0a628ca80..066a3be3f 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": "2024-09-26T17:03:32.155568Z",
- "iopub.status.busy": "2024-09-26T17:03:32.155382Z",
- "iopub.status.idle": "2024-09-26T17:03:33.322663Z",
- "shell.execute_reply": "2024-09-26T17:03:33.322106Z"
+ "iopub.execute_input": "2024-09-27T13:50:00.283031Z",
+ "iopub.status.busy": "2024-09-27T13:50:00.282850Z",
+ "iopub.status.idle": "2024-09-27T13:50:01.529982Z",
+ "shell.execute_reply": "2024-09-27T13:50:01.529363Z"
},
"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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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": "2024-09-26T17:03:33.325051Z",
- "iopub.status.busy": "2024-09-26T17:03:33.324543Z",
- "iopub.status.idle": "2024-09-26T17:03:33.327571Z",
- "shell.execute_reply": "2024-09-26T17:03:33.327125Z"
+ "iopub.execute_input": "2024-09-27T13:50:01.532249Z",
+ "iopub.status.busy": "2024-09-27T13:50:01.531766Z",
+ "iopub.status.idle": "2024-09-27T13:50:01.535029Z",
+ "shell.execute_reply": "2024-09-27T13:50:01.534558Z"
}
},
"outputs": [],
@@ -263,10 +263,10 @@
"id": "c37c0a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:33.329481Z",
- "iopub.status.busy": "2024-09-26T17:03:33.329106Z",
- "iopub.status.idle": "2024-09-26T17:03:33.337328Z",
- "shell.execute_reply": "2024-09-26T17:03:33.336753Z"
+ "iopub.execute_input": "2024-09-27T13:50:01.537031Z",
+ "iopub.status.busy": "2024-09-27T13:50:01.536667Z",
+ "iopub.status.idle": "2024-09-27T13:50:01.544914Z",
+ "shell.execute_reply": "2024-09-27T13:50:01.544396Z"
},
"nbsphinx": "hidden"
},
@@ -350,10 +350,10 @@
"id": "99f69523",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:33.338976Z",
- "iopub.status.busy": "2024-09-26T17:03:33.338803Z",
- "iopub.status.idle": "2024-09-26T17:03:33.384904Z",
- "shell.execute_reply": "2024-09-26T17:03:33.384320Z"
+ "iopub.execute_input": "2024-09-27T13:50:01.546711Z",
+ "iopub.status.busy": "2024-09-27T13:50:01.546356Z",
+ "iopub.status.idle": "2024-09-27T13:50:01.593910Z",
+ "shell.execute_reply": "2024-09-27T13:50:01.593338Z"
}
},
"outputs": [],
@@ -379,10 +379,10 @@
"id": "8f241c16",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:33.386626Z",
- "iopub.status.busy": "2024-09-26T17:03:33.386442Z",
- "iopub.status.idle": "2024-09-26T17:03:33.403626Z",
- "shell.execute_reply": "2024-09-26T17:03:33.403085Z"
+ "iopub.execute_input": "2024-09-27T13:50:01.595899Z",
+ "iopub.status.busy": "2024-09-27T13:50:01.595701Z",
+ "iopub.status.idle": "2024-09-27T13:50:01.614081Z",
+ "shell.execute_reply": "2024-09-27T13:50:01.613547Z"
}
},
"outputs": [
@@ -597,10 +597,10 @@
"id": "4f0819ba",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:33.405451Z",
- "iopub.status.busy": "2024-09-26T17:03:33.405118Z",
- "iopub.status.idle": "2024-09-26T17:03:33.409011Z",
- "shell.execute_reply": "2024-09-26T17:03:33.408476Z"
+ "iopub.execute_input": "2024-09-27T13:50:01.615863Z",
+ "iopub.status.busy": "2024-09-27T13:50:01.615656Z",
+ "iopub.status.idle": "2024-09-27T13:50:01.619883Z",
+ "shell.execute_reply": "2024-09-27T13:50:01.619423Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "d009f347",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:33.410796Z",
- "iopub.status.busy": "2024-09-26T17:03:33.410382Z",
- "iopub.status.idle": "2024-09-26T17:03:33.427653Z",
- "shell.execute_reply": "2024-09-26T17:03:33.427072Z"
+ "iopub.execute_input": "2024-09-27T13:50:01.621805Z",
+ "iopub.status.busy": "2024-09-27T13:50:01.621459Z",
+ "iopub.status.idle": "2024-09-27T13:50:01.636304Z",
+ "shell.execute_reply": "2024-09-27T13:50:01.635842Z"
}
},
"outputs": [],
@@ -698,10 +698,10 @@
"id": "cbd1e415",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:33.429482Z",
- "iopub.status.busy": "2024-09-26T17:03:33.429162Z",
- "iopub.status.idle": "2024-09-26T17:03:33.455291Z",
- "shell.execute_reply": "2024-09-26T17:03:33.454811Z"
+ "iopub.execute_input": "2024-09-27T13:50:01.638170Z",
+ "iopub.status.busy": "2024-09-27T13:50:01.637803Z",
+ "iopub.status.idle": "2024-09-27T13:50:01.664411Z",
+ "shell.execute_reply": "2024-09-27T13:50:01.663771Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"id": "6ca92617",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:33.456954Z",
- "iopub.status.busy": "2024-09-26T17:03:33.456624Z",
- "iopub.status.idle": "2024-09-26T17:03:35.369827Z",
- "shell.execute_reply": "2024-09-26T17:03:35.369227Z"
+ "iopub.execute_input": "2024-09-27T13:50:01.666543Z",
+ "iopub.status.busy": "2024-09-27T13:50:01.666195Z",
+ "iopub.status.idle": "2024-09-27T13:50:03.656395Z",
+ "shell.execute_reply": "2024-09-27T13:50:03.655827Z"
}
},
"outputs": [],
@@ -771,10 +771,10 @@
"id": "bf945113",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:35.372127Z",
- "iopub.status.busy": "2024-09-26T17:03:35.371668Z",
- "iopub.status.idle": "2024-09-26T17:03:35.378386Z",
- "shell.execute_reply": "2024-09-26T17:03:35.377922Z"
+ "iopub.execute_input": "2024-09-27T13:50:03.658501Z",
+ "iopub.status.busy": "2024-09-27T13:50:03.658175Z",
+ "iopub.status.idle": "2024-09-27T13:50:03.665262Z",
+ "shell.execute_reply": "2024-09-27T13:50:03.664800Z"
},
"scrolled": true
},
@@ -885,10 +885,10 @@
"id": "14251ee0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:35.380068Z",
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- "iopub.status.idle": "2024-09-26T17:03:35.392208Z",
- "shell.execute_reply": "2024-09-26T17:03:35.391671Z"
+ "iopub.execute_input": "2024-09-27T13:50:03.666987Z",
+ "iopub.status.busy": "2024-09-27T13:50:03.666805Z",
+ "iopub.status.idle": "2024-09-27T13:50:03.679807Z",
+ "shell.execute_reply": "2024-09-27T13:50:03.679246Z"
}
},
"outputs": [
@@ -1138,10 +1138,10 @@
"id": "efe16638",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:35.393975Z",
- "iopub.status.busy": "2024-09-26T17:03:35.393668Z",
- "iopub.status.idle": "2024-09-26T17:03:35.399995Z",
- "shell.execute_reply": "2024-09-26T17:03:35.399449Z"
+ "iopub.execute_input": "2024-09-27T13:50:03.681542Z",
+ "iopub.status.busy": "2024-09-27T13:50:03.681289Z",
+ "iopub.status.idle": "2024-09-27T13:50:03.687949Z",
+ "shell.execute_reply": "2024-09-27T13:50:03.687503Z"
},
"scrolled": true
},
@@ -1315,10 +1315,10 @@
"id": "abd0fb0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:35.401696Z",
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- "iopub.status.idle": "2024-09-26T17:03:35.404204Z",
- "shell.execute_reply": "2024-09-26T17:03:35.403751Z"
+ "iopub.execute_input": "2024-09-27T13:50:03.689686Z",
+ "iopub.status.busy": "2024-09-27T13:50:03.689509Z",
+ "iopub.status.idle": "2024-09-27T13:50:03.692058Z",
+ "shell.execute_reply": "2024-09-27T13:50:03.691626Z"
}
},
"outputs": [],
@@ -1340,10 +1340,10 @@
"id": "cdf061df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:35.405738Z",
- "iopub.status.busy": "2024-09-26T17:03:35.405571Z",
- "iopub.status.idle": "2024-09-26T17:03:35.409161Z",
- "shell.execute_reply": "2024-09-26T17:03:35.408696Z"
+ "iopub.execute_input": "2024-09-27T13:50:03.693849Z",
+ "iopub.status.busy": "2024-09-27T13:50:03.693409Z",
+ "iopub.status.idle": "2024-09-27T13:50:03.697114Z",
+ "shell.execute_reply": "2024-09-27T13:50:03.696549Z"
},
"scrolled": true
},
@@ -1395,10 +1395,10 @@
"id": "08949890",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:35.410672Z",
- "iopub.status.busy": "2024-09-26T17:03:35.410505Z",
- "iopub.status.idle": "2024-09-26T17:03:35.413101Z",
- "shell.execute_reply": "2024-09-26T17:03:35.412663Z"
+ "iopub.execute_input": "2024-09-27T13:50:03.698776Z",
+ "iopub.status.busy": "2024-09-27T13:50:03.698468Z",
+ "iopub.status.idle": "2024-09-27T13:50:03.701226Z",
+ "shell.execute_reply": "2024-09-27T13:50:03.700662Z"
}
},
"outputs": [],
@@ -1422,10 +1422,10 @@
"id": "6948b073",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:35.414705Z",
- "iopub.status.busy": "2024-09-26T17:03:35.414538Z",
- "iopub.status.idle": "2024-09-26T17:03:35.418607Z",
- "shell.execute_reply": "2024-09-26T17:03:35.418062Z"
+ "iopub.execute_input": "2024-09-27T13:50:03.703170Z",
+ "iopub.status.busy": "2024-09-27T13:50:03.702730Z",
+ "iopub.status.idle": "2024-09-27T13:50:03.706827Z",
+ "shell.execute_reply": "2024-09-27T13:50:03.706370Z"
}
},
"outputs": [
@@ -1480,10 +1480,10 @@
"id": "6f8e6914",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:35.420421Z",
- "iopub.status.busy": "2024-09-26T17:03:35.420103Z",
- "iopub.status.idle": "2024-09-26T17:03:35.449647Z",
- "shell.execute_reply": "2024-09-26T17:03:35.449056Z"
+ "iopub.execute_input": "2024-09-27T13:50:03.708679Z",
+ "iopub.status.busy": "2024-09-27T13:50:03.708375Z",
+ "iopub.status.idle": "2024-09-27T13:50:03.737494Z",
+ "shell.execute_reply": "2024-09-27T13:50:03.736888Z"
}
},
"outputs": [],
@@ -1526,10 +1526,10 @@
"id": "b806d2ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:35.451503Z",
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- "shell.execute_reply": "2024-09-26T17:03:35.455091Z"
+ "iopub.execute_input": "2024-09-27T13:50:03.739537Z",
+ "iopub.status.busy": "2024-09-27T13:50:03.739354Z",
+ "iopub.status.idle": "2024-09-27T13:50:03.743924Z",
+ "shell.execute_reply": "2024-09-27T13:50:03.743478Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
index 9ceb5596d..15ae19f22 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": "2024-09-26T17:03:38.235356Z",
- "iopub.status.busy": "2024-09-26T17:03:38.235187Z",
- "iopub.status.idle": "2024-09-26T17:03:39.464936Z",
- "shell.execute_reply": "2024-09-26T17:03:39.464384Z"
+ "iopub.execute_input": "2024-09-27T13:50:06.725990Z",
+ "iopub.status.busy": "2024-09-27T13:50:06.725779Z",
+ "iopub.status.idle": "2024-09-27T13:50:07.973724Z",
+ "shell.execute_reply": "2024-09-27T13:50:07.973155Z"
},
"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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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": "2024-09-26T17:03:39.467094Z",
- "iopub.status.busy": "2024-09-26T17:03:39.466633Z",
- "iopub.status.idle": "2024-09-26T17:03:39.661808Z",
- "shell.execute_reply": "2024-09-26T17:03:39.661225Z"
+ "iopub.execute_input": "2024-09-27T13:50:07.975731Z",
+ "iopub.status.busy": "2024-09-27T13:50:07.975460Z",
+ "iopub.status.idle": "2024-09-27T13:50:08.171427Z",
+ "shell.execute_reply": "2024-09-27T13:50:08.170874Z"
}
},
"outputs": [],
@@ -268,10 +268,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:39.663940Z",
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- "iopub.status.idle": "2024-09-26T17:03:39.676592Z",
- "shell.execute_reply": "2024-09-26T17:03:39.676141Z"
+ "iopub.execute_input": "2024-09-27T13:50:08.173720Z",
+ "iopub.status.busy": "2024-09-27T13:50:08.173246Z",
+ "iopub.status.idle": "2024-09-27T13:50:08.186415Z",
+ "shell.execute_reply": "2024-09-27T13:50:08.185928Z"
},
"nbsphinx": "hidden"
},
@@ -407,10 +407,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:39.678430Z",
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- "iopub.status.idle": "2024-09-26T17:03:42.338055Z",
- "shell.execute_reply": "2024-09-26T17:03:42.337536Z"
+ "iopub.execute_input": "2024-09-27T13:50:08.188193Z",
+ "iopub.status.busy": "2024-09-27T13:50:08.187863Z",
+ "iopub.status.idle": "2024-09-27T13:50:10.832960Z",
+ "shell.execute_reply": "2024-09-27T13:50:10.832424Z"
}
},
"outputs": [
@@ -454,10 +454,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:42.339955Z",
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- "iopub.status.idle": "2024-09-26T17:03:43.681959Z",
- "shell.execute_reply": "2024-09-26T17:03:43.681410Z"
+ "iopub.execute_input": "2024-09-27T13:50:10.834988Z",
+ "iopub.status.busy": "2024-09-27T13:50:10.834545Z",
+ "iopub.status.idle": "2024-09-27T13:50:12.182428Z",
+ "shell.execute_reply": "2024-09-27T13:50:12.181868Z"
}
},
"outputs": [],
@@ -499,10 +499,10 @@
"id": "ac1a60df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:43.683980Z",
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- "iopub.status.idle": "2024-09-26T17:03:43.687775Z",
- "shell.execute_reply": "2024-09-26T17:03:43.687305Z"
+ "iopub.execute_input": "2024-09-27T13:50:12.184478Z",
+ "iopub.status.busy": "2024-09-27T13:50:12.184103Z",
+ "iopub.status.idle": "2024-09-27T13:50:12.187833Z",
+ "shell.execute_reply": "2024-09-27T13:50:12.187391Z"
}
},
"outputs": [
@@ -544,10 +544,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:43.689610Z",
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- "iopub.status.idle": "2024-09-26T17:03:45.722043Z",
- "shell.execute_reply": "2024-09-26T17:03:45.721346Z"
+ "iopub.execute_input": "2024-09-27T13:50:12.189617Z",
+ "iopub.status.busy": "2024-09-27T13:50:12.189276Z",
+ "iopub.status.idle": "2024-09-27T13:50:14.250365Z",
+ "shell.execute_reply": "2024-09-27T13:50:14.249644Z"
}
},
"outputs": [
@@ -594,10 +594,10 @@
"id": "c18dd83b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:45.724606Z",
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- "iopub.status.idle": "2024-09-26T17:03:45.733795Z",
- "shell.execute_reply": "2024-09-26T17:03:45.733323Z"
+ "iopub.execute_input": "2024-09-27T13:50:14.253014Z",
+ "iopub.status.busy": "2024-09-27T13:50:14.252227Z",
+ "iopub.status.idle": "2024-09-27T13:50:14.261829Z",
+ "shell.execute_reply": "2024-09-27T13:50:14.261366Z"
}
},
"outputs": [
@@ -633,10 +633,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:45.735506Z",
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- "iopub.status.idle": "2024-09-26T17:03:48.302047Z",
- "shell.execute_reply": "2024-09-26T17:03:48.301457Z"
+ "iopub.execute_input": "2024-09-27T13:50:14.263623Z",
+ "iopub.status.busy": "2024-09-27T13:50:14.263292Z",
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+ "shell.execute_reply": "2024-09-27T13:50:16.825201Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:48.303862Z",
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- "shell.execute_reply": "2024-09-26T17:03:48.306449Z"
+ "iopub.execute_input": "2024-09-27T13:50:16.827773Z",
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+ "shell.execute_reply": "2024-09-27T13:50:16.830226Z"
}
},
"outputs": [
@@ -721,10 +721,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:48.308470Z",
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- "shell.execute_reply": "2024-09-26T17:03:48.311399Z"
+ "iopub.execute_input": "2024-09-27T13:50:16.832389Z",
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+ "shell.execute_reply": "2024-09-27T13:50:16.834951Z"
}
},
"outputs": [],
@@ -769,10 +769,10 @@
"id": "d1a2c008",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:48.313429Z",
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- "shell.execute_reply": "2024-09-26T17:03:48.315904Z"
+ "iopub.execute_input": "2024-09-27T13:50:16.837072Z",
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+ "shell.execute_reply": "2024-09-27T13:50:16.839294Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
index 4cf1baa9c..74581ae08 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": "2024-09-26T17:03:50.872695Z",
- "iopub.status.busy": "2024-09-26T17:03:50.872527Z",
- "iopub.status.idle": "2024-09-26T17:03:52.108435Z",
- "shell.execute_reply": "2024-09-26T17:03:52.107922Z"
+ "iopub.execute_input": "2024-09-27T13:50:19.443579Z",
+ "iopub.status.busy": "2024-09-27T13:50:19.443403Z",
+ "iopub.status.idle": "2024-09-27T13:50:20.702879Z",
+ "shell.execute_reply": "2024-09-27T13:50:20.702310Z"
},
"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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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": "2024-09-26T17:03:52.110652Z",
- "iopub.status.busy": "2024-09-26T17:03:52.110373Z",
- "iopub.status.idle": "2024-09-26T17:03:53.718304Z",
- "shell.execute_reply": "2024-09-26T17:03:53.717592Z"
+ "iopub.execute_input": "2024-09-27T13:50:20.705084Z",
+ "iopub.status.busy": "2024-09-27T13:50:20.704636Z",
+ "iopub.status.idle": "2024-09-27T13:50:22.822151Z",
+ "shell.execute_reply": "2024-09-27T13:50:22.821410Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:53.720333Z",
- "iopub.status.busy": "2024-09-26T17:03:53.720131Z",
- "iopub.status.idle": "2024-09-26T17:03:53.723716Z",
- "shell.execute_reply": "2024-09-26T17:03:53.723249Z"
+ "iopub.execute_input": "2024-09-27T13:50:22.824450Z",
+ "iopub.status.busy": "2024-09-27T13:50:22.823982Z",
+ "iopub.status.idle": "2024-09-27T13:50:22.827315Z",
+ "shell.execute_reply": "2024-09-27T13:50:22.826864Z"
}
},
"outputs": [],
@@ -169,10 +169,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:53.725367Z",
- "iopub.status.busy": "2024-09-26T17:03:53.725176Z",
- "iopub.status.idle": "2024-09-26T17:03:53.731881Z",
- "shell.execute_reply": "2024-09-26T17:03:53.731422Z"
+ "iopub.execute_input": "2024-09-27T13:50:22.829177Z",
+ "iopub.status.busy": "2024-09-27T13:50:22.828729Z",
+ "iopub.status.idle": "2024-09-27T13:50:22.835505Z",
+ "shell.execute_reply": "2024-09-27T13:50:22.835064Z"
}
},
"outputs": [],
@@ -198,10 +198,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:53.733470Z",
- "iopub.status.busy": "2024-09-26T17:03:53.733289Z",
- "iopub.status.idle": "2024-09-26T17:03:54.226908Z",
- "shell.execute_reply": "2024-09-26T17:03:54.226298Z"
+ "iopub.execute_input": "2024-09-27T13:50:22.837239Z",
+ "iopub.status.busy": "2024-09-27T13:50:22.836893Z",
+ "iopub.status.idle": "2024-09-27T13:50:23.331183Z",
+ "shell.execute_reply": "2024-09-27T13:50:23.330607Z"
},
"scrolled": true
},
@@ -242,10 +242,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:54.228918Z",
- "iopub.status.busy": "2024-09-26T17:03:54.228500Z",
- "iopub.status.idle": "2024-09-26T17:03:54.233862Z",
- "shell.execute_reply": "2024-09-26T17:03:54.233427Z"
+ "iopub.execute_input": "2024-09-27T13:50:23.333657Z",
+ "iopub.status.busy": "2024-09-27T13:50:23.333258Z",
+ "iopub.status.idle": "2024-09-27T13:50:23.338644Z",
+ "shell.execute_reply": "2024-09-27T13:50:23.338176Z"
}
},
"outputs": [
@@ -497,10 +497,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:54.235629Z",
- "iopub.status.busy": "2024-09-26T17:03:54.235308Z",
- "iopub.status.idle": "2024-09-26T17:03:54.239163Z",
- "shell.execute_reply": "2024-09-26T17:03:54.238723Z"
+ "iopub.execute_input": "2024-09-27T13:50:23.340282Z",
+ "iopub.status.busy": "2024-09-27T13:50:23.339946Z",
+ "iopub.status.idle": "2024-09-27T13:50:23.343951Z",
+ "shell.execute_reply": "2024-09-27T13:50:23.343382Z"
}
},
"outputs": [
@@ -557,10 +557,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:54.240884Z",
- "iopub.status.busy": "2024-09-26T17:03:54.240539Z",
- "iopub.status.idle": "2024-09-26T17:03:55.132461Z",
- "shell.execute_reply": "2024-09-26T17:03:55.131884Z"
+ "iopub.execute_input": "2024-09-27T13:50:23.345634Z",
+ "iopub.status.busy": "2024-09-27T13:50:23.345444Z",
+ "iopub.status.idle": "2024-09-27T13:50:24.311351Z",
+ "shell.execute_reply": "2024-09-27T13:50:24.310678Z"
}
},
"outputs": [
@@ -616,10 +616,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:55.134485Z",
- "iopub.status.busy": "2024-09-26T17:03:55.134101Z",
- "iopub.status.idle": "2024-09-26T17:03:55.344926Z",
- "shell.execute_reply": "2024-09-26T17:03:55.344350Z"
+ "iopub.execute_input": "2024-09-27T13:50:24.313472Z",
+ "iopub.status.busy": "2024-09-27T13:50:24.313086Z",
+ "iopub.status.idle": "2024-09-27T13:50:24.522945Z",
+ "shell.execute_reply": "2024-09-27T13:50:24.522473Z"
}
},
"outputs": [
@@ -660,10 +660,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:55.347039Z",
- "iopub.status.busy": "2024-09-26T17:03:55.346617Z",
- "iopub.status.idle": "2024-09-26T17:03:55.351223Z",
- "shell.execute_reply": "2024-09-26T17:03:55.350673Z"
+ "iopub.execute_input": "2024-09-27T13:50:24.524988Z",
+ "iopub.status.busy": "2024-09-27T13:50:24.524624Z",
+ "iopub.status.idle": "2024-09-27T13:50:24.529040Z",
+ "shell.execute_reply": "2024-09-27T13:50:24.528463Z"
}
},
"outputs": [
@@ -700,10 +700,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:55.353046Z",
- "iopub.status.busy": "2024-09-26T17:03:55.352712Z",
- "iopub.status.idle": "2024-09-26T17:03:55.808907Z",
- "shell.execute_reply": "2024-09-26T17:03:55.808307Z"
+ "iopub.execute_input": "2024-09-27T13:50:24.530989Z",
+ "iopub.status.busy": "2024-09-27T13:50:24.530646Z",
+ "iopub.status.idle": "2024-09-27T13:50:24.991345Z",
+ "shell.execute_reply": "2024-09-27T13:50:24.990700Z"
}
},
"outputs": [
@@ -762,10 +762,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:55.811591Z",
- "iopub.status.busy": "2024-09-26T17:03:55.811241Z",
- "iopub.status.idle": "2024-09-26T17:03:56.145507Z",
- "shell.execute_reply": "2024-09-26T17:03:56.144909Z"
+ "iopub.execute_input": "2024-09-27T13:50:24.994317Z",
+ "iopub.status.busy": "2024-09-27T13:50:24.993692Z",
+ "iopub.status.idle": "2024-09-27T13:50:25.302482Z",
+ "shell.execute_reply": "2024-09-27T13:50:25.301826Z"
}
},
"outputs": [
@@ -812,10 +812,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:56.147534Z",
- "iopub.status.busy": "2024-09-26T17:03:56.147172Z",
- "iopub.status.idle": "2024-09-26T17:03:56.522806Z",
- "shell.execute_reply": "2024-09-26T17:03:56.522185Z"
+ "iopub.execute_input": "2024-09-27T13:50:25.304445Z",
+ "iopub.status.busy": "2024-09-27T13:50:25.304098Z",
+ "iopub.status.idle": "2024-09-27T13:50:25.672846Z",
+ "shell.execute_reply": "2024-09-27T13:50:25.672241Z"
}
},
"outputs": [
@@ -862,10 +862,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:56.525094Z",
- "iopub.status.busy": "2024-09-26T17:03:56.524803Z",
- "iopub.status.idle": "2024-09-26T17:03:56.938146Z",
- "shell.execute_reply": "2024-09-26T17:03:56.937571Z"
+ "iopub.execute_input": "2024-09-27T13:50:25.675532Z",
+ "iopub.status.busy": "2024-09-27T13:50:25.675160Z",
+ "iopub.status.idle": "2024-09-27T13:50:26.138514Z",
+ "shell.execute_reply": "2024-09-27T13:50:26.137926Z"
}
},
"outputs": [
@@ -925,10 +925,10 @@
"id": "7e770d23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:56.942264Z",
- "iopub.status.busy": "2024-09-26T17:03:56.941870Z",
- "iopub.status.idle": "2024-09-26T17:03:57.370517Z",
- "shell.execute_reply": "2024-09-26T17:03:57.369935Z"
+ "iopub.execute_input": "2024-09-27T13:50:26.142672Z",
+ "iopub.status.busy": "2024-09-27T13:50:26.142279Z",
+ "iopub.status.idle": "2024-09-27T13:50:26.594693Z",
+ "shell.execute_reply": "2024-09-27T13:50:26.594081Z"
}
},
"outputs": [
@@ -971,10 +971,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:57.373172Z",
- "iopub.status.busy": "2024-09-26T17:03:57.372782Z",
- "iopub.status.idle": "2024-09-26T17:03:57.563441Z",
- "shell.execute_reply": "2024-09-26T17:03:57.562843Z"
+ "iopub.execute_input": "2024-09-27T13:50:26.597306Z",
+ "iopub.status.busy": "2024-09-27T13:50:26.597107Z",
+ "iopub.status.idle": "2024-09-27T13:50:26.821495Z",
+ "shell.execute_reply": "2024-09-27T13:50:26.820944Z"
}
},
"outputs": [
@@ -1017,10 +1017,10 @@
"id": "0302818a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:57.565901Z",
- "iopub.status.busy": "2024-09-26T17:03:57.565444Z",
- "iopub.status.idle": "2024-09-26T17:03:57.769189Z",
- "shell.execute_reply": "2024-09-26T17:03:57.768596Z"
+ "iopub.execute_input": "2024-09-27T13:50:26.823432Z",
+ "iopub.status.busy": "2024-09-27T13:50:26.823089Z",
+ "iopub.status.idle": "2024-09-27T13:50:27.023714Z",
+ "shell.execute_reply": "2024-09-27T13:50:27.023125Z"
}
},
"outputs": [
@@ -1067,10 +1067,10 @@
"id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:57.771705Z",
- "iopub.status.busy": "2024-09-26T17:03:57.771376Z",
- "iopub.status.idle": "2024-09-26T17:03:57.774381Z",
- "shell.execute_reply": "2024-09-26T17:03:57.773929Z"
+ "iopub.execute_input": "2024-09-27T13:50:27.025739Z",
+ "iopub.status.busy": "2024-09-27T13:50:27.025301Z",
+ "iopub.status.idle": "2024-09-27T13:50:27.028302Z",
+ "shell.execute_reply": "2024-09-27T13:50:27.027862Z"
}
},
"outputs": [],
@@ -1090,10 +1090,10 @@
"id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:57.775799Z",
- "iopub.status.busy": "2024-09-26T17:03:57.775632Z",
- "iopub.status.idle": "2024-09-26T17:03:58.717491Z",
- "shell.execute_reply": "2024-09-26T17:03:58.716886Z"
+ "iopub.execute_input": "2024-09-27T13:50:27.030091Z",
+ "iopub.status.busy": "2024-09-27T13:50:27.029679Z",
+ "iopub.status.idle": "2024-09-27T13:50:28.012427Z",
+ "shell.execute_reply": "2024-09-27T13:50:28.011865Z"
}
},
"outputs": [
@@ -1172,10 +1172,10 @@
"id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:58.719646Z",
- "iopub.status.busy": "2024-09-26T17:03:58.719200Z",
- "iopub.status.idle": "2024-09-26T17:03:58.860617Z",
- "shell.execute_reply": "2024-09-26T17:03:58.860127Z"
+ "iopub.execute_input": "2024-09-27T13:50:28.014834Z",
+ "iopub.status.busy": "2024-09-27T13:50:28.014455Z",
+ "iopub.status.idle": "2024-09-27T13:50:28.134002Z",
+ "shell.execute_reply": "2024-09-27T13:50:28.133439Z"
}
},
"outputs": [
@@ -1214,10 +1214,10 @@
"id": "59d7ee39-3785-434b-8680-9133014851cd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:58.862547Z",
- "iopub.status.busy": "2024-09-26T17:03:58.862194Z",
- "iopub.status.idle": "2024-09-26T17:03:58.994557Z",
- "shell.execute_reply": "2024-09-26T17:03:58.994095Z"
+ "iopub.execute_input": "2024-09-27T13:50:28.136003Z",
+ "iopub.status.busy": "2024-09-27T13:50:28.135574Z",
+ "iopub.status.idle": "2024-09-27T13:50:28.317635Z",
+ "shell.execute_reply": "2024-09-27T13:50:28.317140Z"
}
},
"outputs": [],
@@ -1266,10 +1266,10 @@
"id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:58.996401Z",
- "iopub.status.busy": "2024-09-26T17:03:58.996000Z",
- "iopub.status.idle": "2024-09-26T17:03:59.724120Z",
- "shell.execute_reply": "2024-09-26T17:03:59.723503Z"
+ "iopub.execute_input": "2024-09-27T13:50:28.319646Z",
+ "iopub.status.busy": "2024-09-27T13:50:28.319301Z",
+ "iopub.status.idle": "2024-09-27T13:50:29.078487Z",
+ "shell.execute_reply": "2024-09-27T13:50:29.077858Z"
}
},
"outputs": [
@@ -1351,10 +1351,10 @@
"id": "8ce74938",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:03:59.725920Z",
- "iopub.status.busy": "2024-09-26T17:03:59.725591Z",
- "iopub.status.idle": "2024-09-26T17:03:59.729320Z",
- "shell.execute_reply": "2024-09-26T17:03:59.728740Z"
+ "iopub.execute_input": "2024-09-27T13:50:29.080254Z",
+ "iopub.status.busy": "2024-09-27T13:50:29.080059Z",
+ "iopub.status.idle": "2024-09-27T13:50:29.083764Z",
+ "shell.execute_reply": "2024-09-27T13:50:29.083320Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
index 178f049b6..a22f47f5c 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": "2024-09-26T17:04:01.971300Z",
- "iopub.status.busy": "2024-09-26T17:04:01.971121Z",
- "iopub.status.idle": "2024-09-26T17:04:04.854776Z",
- "shell.execute_reply": "2024-09-26T17:04:04.854218Z"
+ "iopub.execute_input": "2024-09-27T13:50:31.336559Z",
+ "iopub.status.busy": "2024-09-27T13:50:31.336374Z",
+ "iopub.status.idle": "2024-09-27T13:50:34.288125Z",
+ "shell.execute_reply": "2024-09-27T13:50:34.287547Z"
},
"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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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": "2024-09-26T17:04:04.856883Z",
- "iopub.status.busy": "2024-09-26T17:04:04.856573Z",
- "iopub.status.idle": "2024-09-26T17:04:05.177735Z",
- "shell.execute_reply": "2024-09-26T17:04:05.177154Z"
+ "iopub.execute_input": "2024-09-27T13:50:34.290281Z",
+ "iopub.status.busy": "2024-09-27T13:50:34.289977Z",
+ "iopub.status.idle": "2024-09-27T13:50:34.624581Z",
+ "shell.execute_reply": "2024-09-27T13:50:34.624016Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "3792f82e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:04:05.180049Z",
- "iopub.status.busy": "2024-09-26T17:04:05.179567Z",
- "iopub.status.idle": "2024-09-26T17:04:05.183627Z",
- "shell.execute_reply": "2024-09-26T17:04:05.183194Z"
+ "iopub.execute_input": "2024-09-27T13:50:34.626906Z",
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@@ -225,10 +225,10 @@
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@@ -252,7 +252,7 @@
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+ " 1%| | 1736704/170498071 [00:00<00:09, 17324553.72it/s]"
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@@ -268,7 +268,7 @@
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+ " 10%|▉ | 16384000/170498071 [00:00<00:02, 58867112.24it/s]"
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@@ -308,7 +308,7 @@
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@@ -324,7 +324,7 @@
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@@ -332,7 +332,7 @@
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@@ -348,7 +348,7 @@
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@@ -364,7 +364,7 @@
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@@ -380,7 +380,87 @@
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@@ -498,10 +578,10 @@
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@@ -552,10 +632,10 @@
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@@ -588,10 +668,10 @@
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@@ -629,10 +709,10 @@
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@@ -655,17 +735,17 @@
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@@ -724,10 +804,10 @@
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@@ -863,10 +943,10 @@
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@@ -914,10 +994,10 @@
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@@ -973,10 +1053,10 @@
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@@ -997,10 +1077,10 @@
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@@ -1037,10 +1117,10 @@
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@@ -1071,10 +1151,10 @@
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@@ -1088,10 +1168,10 @@
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@@ -1129,10 +1209,10 @@
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@@ -1177,7 +1257,56 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
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diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
index 4e0f503ec..a781c88d1 100644
--- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
@@ -102,10 +102,10 @@
"id": "2e1af7d8",
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@@ -116,7 +116,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
"\n",
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -164,10 +164,10 @@
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@@ -198,10 +198,10 @@
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@@ -374,10 +374,10 @@
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@@ -417,10 +417,10 @@
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@@ -456,10 +456,10 @@
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@@ -477,10 +477,10 @@
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@@ -527,10 +527,10 @@
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@@ -545,10 +545,10 @@
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@@ -572,10 +572,10 @@
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- "shell.execute_reply": "2024-09-26T17:04:55.892454Z"
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@@ -678,10 +678,10 @@
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@@ -696,10 +696,10 @@
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},
"outputs": [
@@ -734,10 +734,10 @@
"id": "00949977",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:04:55.904316Z",
- "iopub.status.busy": "2024-09-26T17:04:55.903915Z",
- "iopub.status.idle": "2024-09-26T17:04:55.906956Z",
- "shell.execute_reply": "2024-09-26T17:04:55.906478Z"
+ "iopub.execute_input": "2024-09-27T13:51:26.087772Z",
+ "iopub.status.busy": "2024-09-27T13:51:26.087368Z",
+ "iopub.status.idle": "2024-09-27T13:51:26.090518Z",
+ "shell.execute_reply": "2024-09-27T13:51:26.090064Z"
}
},
"outputs": [],
@@ -756,10 +756,10 @@
"id": "b6c1ae3a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:04:55.908409Z",
- "iopub.status.busy": "2024-09-26T17:04:55.908236Z",
- "iopub.status.idle": "2024-09-26T17:04:55.916410Z",
- "shell.execute_reply": "2024-09-26T17:04:55.915860Z"
+ "iopub.execute_input": "2024-09-27T13:51:26.092191Z",
+ "iopub.status.busy": "2024-09-27T13:51:26.091860Z",
+ "iopub.status.idle": "2024-09-27T13:51:26.099609Z",
+ "shell.execute_reply": "2024-09-27T13:51:26.099158Z"
}
},
"outputs": [
@@ -883,10 +883,10 @@
"id": "9131d82d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:04:55.918227Z",
- "iopub.status.busy": "2024-09-26T17:04:55.917906Z",
- "iopub.status.idle": "2024-09-26T17:04:55.920593Z",
- "shell.execute_reply": "2024-09-26T17:04:55.920127Z"
+ "iopub.execute_input": "2024-09-27T13:51:26.101260Z",
+ "iopub.status.busy": "2024-09-27T13:51:26.100944Z",
+ "iopub.status.idle": "2024-09-27T13:51:26.103665Z",
+ "shell.execute_reply": "2024-09-27T13:51:26.103114Z"
},
"nbsphinx": "hidden"
},
@@ -921,10 +921,10 @@
"id": "31c704e7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:04:55.922554Z",
- "iopub.status.busy": "2024-09-26T17:04:55.922044Z",
- "iopub.status.idle": "2024-09-26T17:04:56.045940Z",
- "shell.execute_reply": "2024-09-26T17:04:56.045422Z"
+ "iopub.execute_input": "2024-09-27T13:51:26.105327Z",
+ "iopub.status.busy": "2024-09-27T13:51:26.105016Z",
+ "iopub.status.idle": "2024-09-27T13:51:26.230588Z",
+ "shell.execute_reply": "2024-09-27T13:51:26.229994Z"
}
},
"outputs": [
@@ -963,10 +963,10 @@
"id": "0bcc43db",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:04:56.047840Z",
- "iopub.status.busy": "2024-09-26T17:04:56.047468Z",
- "iopub.status.idle": "2024-09-26T17:04:56.167819Z",
- "shell.execute_reply": "2024-09-26T17:04:56.167293Z"
+ "iopub.execute_input": "2024-09-27T13:51:26.232496Z",
+ "iopub.status.busy": "2024-09-27T13:51:26.232118Z",
+ "iopub.status.idle": "2024-09-27T13:51:26.342308Z",
+ "shell.execute_reply": "2024-09-27T13:51:26.341751Z"
}
},
"outputs": [
@@ -1022,10 +1022,10 @@
"id": "7021bd68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:04:56.169945Z",
- "iopub.status.busy": "2024-09-26T17:04:56.169503Z",
- "iopub.status.idle": "2024-09-26T17:04:56.685934Z",
- "shell.execute_reply": "2024-09-26T17:04:56.685296Z"
+ "iopub.execute_input": "2024-09-27T13:51:26.344213Z",
+ "iopub.status.busy": "2024-09-27T13:51:26.343885Z",
+ "iopub.status.idle": "2024-09-27T13:51:26.866342Z",
+ "shell.execute_reply": "2024-09-27T13:51:26.865682Z"
}
},
"outputs": [],
@@ -1041,10 +1041,10 @@
"id": "d49c990b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:04:56.688248Z",
- "iopub.status.busy": "2024-09-26T17:04:56.687817Z",
- "iopub.status.idle": "2024-09-26T17:04:56.783644Z",
- "shell.execute_reply": "2024-09-26T17:04:56.783001Z"
+ "iopub.execute_input": "2024-09-27T13:51:26.868362Z",
+ "iopub.status.busy": "2024-09-27T13:51:26.868178Z",
+ "iopub.status.idle": "2024-09-27T13:51:26.964126Z",
+ "shell.execute_reply": "2024-09-27T13:51:26.963551Z"
}
},
"outputs": [
@@ -1079,10 +1079,10 @@
"id": "dbab6fb3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:04:56.785728Z",
- "iopub.status.busy": "2024-09-26T17:04:56.785306Z",
- "iopub.status.idle": "2024-09-26T17:04:56.793844Z",
- "shell.execute_reply": "2024-09-26T17:04:56.793274Z"
+ "iopub.execute_input": "2024-09-27T13:51:26.966053Z",
+ "iopub.status.busy": "2024-09-27T13:51:26.965821Z",
+ "iopub.status.idle": "2024-09-27T13:51:26.974429Z",
+ "shell.execute_reply": "2024-09-27T13:51:26.973838Z"
}
},
"outputs": [
@@ -1189,10 +1189,10 @@
"id": "5b39b8b5",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:04:56.795479Z",
- "iopub.status.busy": "2024-09-26T17:04:56.795160Z",
- "iopub.status.idle": "2024-09-26T17:04:56.797955Z",
- "shell.execute_reply": "2024-09-26T17:04:56.797402Z"
+ "iopub.execute_input": "2024-09-27T13:51:26.976211Z",
+ "iopub.status.busy": "2024-09-27T13:51:26.975775Z",
+ "iopub.status.idle": "2024-09-27T13:51:26.978660Z",
+ "shell.execute_reply": "2024-09-27T13:51:26.978085Z"
},
"nbsphinx": "hidden"
},
@@ -1217,10 +1217,10 @@
"id": "df06525b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:04:56.799958Z",
- "iopub.status.busy": "2024-09-26T17:04:56.799621Z",
- "iopub.status.idle": "2024-09-26T17:05:02.434662Z",
- "shell.execute_reply": "2024-09-26T17:05:02.434117Z"
+ "iopub.execute_input": "2024-09-27T13:51:26.980464Z",
+ "iopub.status.busy": "2024-09-27T13:51:26.980130Z",
+ "iopub.status.idle": "2024-09-27T13:51:32.684948Z",
+ "shell.execute_reply": "2024-09-27T13:51:32.684334Z"
}
},
"outputs": [
@@ -1264,10 +1264,10 @@
"id": "05282559",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:05:02.436647Z",
- "iopub.status.busy": "2024-09-26T17:05:02.436258Z",
- "iopub.status.idle": "2024-09-26T17:05:02.444773Z",
- "shell.execute_reply": "2024-09-26T17:05:02.444312Z"
+ "iopub.execute_input": "2024-09-27T13:51:32.686957Z",
+ "iopub.status.busy": "2024-09-27T13:51:32.686573Z",
+ "iopub.status.idle": "2024-09-27T13:51:32.695195Z",
+ "shell.execute_reply": "2024-09-27T13:51:32.694586Z"
}
},
"outputs": [
@@ -1392,10 +1392,10 @@
"id": "95531cda",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:05:02.446489Z",
- "iopub.status.busy": "2024-09-26T17:05:02.446154Z",
- "iopub.status.idle": "2024-09-26T17:05:02.514195Z",
- "shell.execute_reply": "2024-09-26T17:05:02.513712Z"
+ "iopub.execute_input": "2024-09-27T13:51:32.696968Z",
+ "iopub.status.busy": "2024-09-27T13:51:32.696618Z",
+ "iopub.status.idle": "2024-09-27T13:51:32.764496Z",
+ "shell.execute_reply": "2024-09-27T13:51:32.763852Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
index 0c953a37d..9dca2eb7d 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": "2024-09-26T17:05:05.717015Z",
- "iopub.status.busy": "2024-09-26T17:05:05.716843Z",
- "iopub.status.idle": "2024-09-26T17:05:08.037778Z",
- "shell.execute_reply": "2024-09-26T17:05:08.037084Z"
+ "iopub.execute_input": "2024-09-27T13:51:35.933316Z",
+ "iopub.status.busy": "2024-09-27T13:51:35.933122Z",
+ "iopub.status.idle": "2024-09-27T13:51:38.270090Z",
+ "shell.execute_reply": "2024-09-27T13:51:38.269373Z"
}
},
"outputs": [],
@@ -79,10 +79,10 @@
"id": "58fd4c55",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:05:08.040304Z",
- "iopub.status.busy": "2024-09-26T17:05:08.039827Z",
- "iopub.status.idle": "2024-09-26T17:06:15.788536Z",
- "shell.execute_reply": "2024-09-26T17:06:15.787814Z"
+ "iopub.execute_input": "2024-09-27T13:51:38.272195Z",
+ "iopub.status.busy": "2024-09-27T13:51:38.271991Z",
+ "iopub.status.idle": "2024-09-27T13:52:43.930890Z",
+ "shell.execute_reply": "2024-09-27T13:52:43.930121Z"
}
},
"outputs": [],
@@ -97,10 +97,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:06:15.791492Z",
- "iopub.status.busy": "2024-09-26T17:06:15.790929Z",
- "iopub.status.idle": "2024-09-26T17:06:17.030438Z",
- "shell.execute_reply": "2024-09-26T17:06:17.029934Z"
+ "iopub.execute_input": "2024-09-27T13:52:43.933175Z",
+ "iopub.status.busy": "2024-09-27T13:52:43.932718Z",
+ "iopub.status.idle": "2024-09-27T13:52:45.152829Z",
+ "shell.execute_reply": "2024-09-27T13:52:45.152260Z"
},
"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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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": "2024-09-26T17:06:17.032578Z",
- "iopub.status.busy": "2024-09-26T17:06:17.032181Z",
- "iopub.status.idle": "2024-09-26T17:06:17.035397Z",
- "shell.execute_reply": "2024-09-26T17:06:17.034940Z"
+ "iopub.execute_input": "2024-09-27T13:52:45.154808Z",
+ "iopub.status.busy": "2024-09-27T13:52:45.154531Z",
+ "iopub.status.idle": "2024-09-27T13:52:45.158007Z",
+ "shell.execute_reply": "2024-09-27T13:52:45.157435Z"
}
},
"outputs": [],
@@ -203,10 +203,10 @@
"id": "07dc5678",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:06:17.037239Z",
- "iopub.status.busy": "2024-09-26T17:06:17.036895Z",
- "iopub.status.idle": "2024-09-26T17:06:17.040814Z",
- "shell.execute_reply": "2024-09-26T17:06:17.040350Z"
+ "iopub.execute_input": "2024-09-27T13:52:45.159874Z",
+ "iopub.status.busy": "2024-09-27T13:52:45.159484Z",
+ "iopub.status.idle": "2024-09-27T13:52:45.163392Z",
+ "shell.execute_reply": "2024-09-27T13:52:45.162836Z"
}
},
"outputs": [
@@ -247,10 +247,10 @@
"id": "25ebe22a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:06:17.042465Z",
- "iopub.status.busy": "2024-09-26T17:06:17.042192Z",
- "iopub.status.idle": "2024-09-26T17:06:17.045640Z",
- "shell.execute_reply": "2024-09-26T17:06:17.045177Z"
+ "iopub.execute_input": "2024-09-27T13:52:45.165264Z",
+ "iopub.status.busy": "2024-09-27T13:52:45.164843Z",
+ "iopub.status.idle": "2024-09-27T13:52:45.168434Z",
+ "shell.execute_reply": "2024-09-27T13:52:45.168001Z"
}
},
"outputs": [
@@ -290,10 +290,10 @@
"id": "3faedea9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:06:17.047315Z",
- "iopub.status.busy": "2024-09-26T17:06:17.046980Z",
- "iopub.status.idle": "2024-09-26T17:06:17.049687Z",
- "shell.execute_reply": "2024-09-26T17:06:17.049193Z"
+ "iopub.execute_input": "2024-09-27T13:52:45.170026Z",
+ "iopub.status.busy": "2024-09-27T13:52:45.169831Z",
+ "iopub.status.idle": "2024-09-27T13:52:45.172854Z",
+ "shell.execute_reply": "2024-09-27T13:52:45.172440Z"
}
},
"outputs": [],
@@ -333,17 +333,17 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:06:17.051353Z",
- "iopub.status.busy": "2024-09-26T17:06:17.051011Z",
- "iopub.status.idle": "2024-09-26T17:06:55.164616Z",
- "shell.execute_reply": "2024-09-26T17:06:55.163984Z"
+ "iopub.execute_input": "2024-09-27T13:52:45.174469Z",
+ "iopub.status.busy": "2024-09-27T13:52:45.174131Z",
+ "iopub.status.idle": "2024-09-27T13:53:23.240572Z",
+ "shell.execute_reply": "2024-09-27T13:53:23.239851Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "9433b8180b7c45728863cb9c40d5e567",
+ "model_id": "0cacd283386e42a5bdc7ef667a30ed27",
"version_major": 2,
"version_minor": 0
},
@@ -357,7 +357,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "069caa427ad347c5bd1333db3bd5ec8b",
+ "model_id": "4644996a967241dfa8d773a9ca551092",
"version_major": 2,
"version_minor": 0
},
@@ -400,10 +400,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:06:55.166835Z",
- "iopub.status.busy": "2024-09-26T17:06:55.166581Z",
- "iopub.status.idle": "2024-09-26T17:06:55.838631Z",
- "shell.execute_reply": "2024-09-26T17:06:55.838008Z"
+ "iopub.execute_input": "2024-09-27T13:53:23.243096Z",
+ "iopub.status.busy": "2024-09-27T13:53:23.242875Z",
+ "iopub.status.idle": "2024-09-27T13:53:23.927697Z",
+ "shell.execute_reply": "2024-09-27T13:53:23.927078Z"
}
},
"outputs": [
@@ -446,10 +446,10 @@
"id": "57fed473",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:06:55.840661Z",
- "iopub.status.busy": "2024-09-26T17:06:55.840128Z",
- "iopub.status.idle": "2024-09-26T17:06:58.654558Z",
- "shell.execute_reply": "2024-09-26T17:06:58.653954Z"
+ "iopub.execute_input": "2024-09-27T13:53:23.929799Z",
+ "iopub.status.busy": "2024-09-27T13:53:23.929338Z",
+ "iopub.status.idle": "2024-09-27T13:53:26.776889Z",
+ "shell.execute_reply": "2024-09-27T13:53:26.776303Z"
}
},
"outputs": [
@@ -519,17 +519,17 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:06:58.656660Z",
- "iopub.status.busy": "2024-09-26T17:06:58.656193Z",
- "iopub.status.idle": "2024-09-26T17:07:31.282511Z",
- "shell.execute_reply": "2024-09-26T17:07:31.282022Z"
+ "iopub.execute_input": "2024-09-27T13:53:26.778857Z",
+ "iopub.status.busy": "2024-09-27T13:53:26.778501Z",
+ "iopub.status.idle": "2024-09-27T13:54:00.727701Z",
+ "shell.execute_reply": "2024-09-27T13:54:00.727143Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "419483351ddf440f89b247293c5dcdc0",
+ "model_id": "ba6bd71b6bee41189f78a1c572677822",
"version_major": 2,
"version_minor": 0
},
@@ -769,10 +769,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:07:31.284330Z",
- "iopub.status.busy": "2024-09-26T17:07:31.283976Z",
- "iopub.status.idle": "2024-09-26T17:07:47.118288Z",
- "shell.execute_reply": "2024-09-26T17:07:47.117716Z"
+ "iopub.execute_input": "2024-09-27T13:54:00.729587Z",
+ "iopub.status.busy": "2024-09-27T13:54:00.729293Z",
+ "iopub.status.idle": "2024-09-27T13:54:16.723181Z",
+ "shell.execute_reply": "2024-09-27T13:54:16.722543Z"
}
},
"outputs": [],
@@ -786,10 +786,10 @@
"id": "716c74f3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:07:47.120299Z",
- "iopub.status.busy": "2024-09-26T17:07:47.119974Z",
- "iopub.status.idle": "2024-09-26T17:07:50.977077Z",
- "shell.execute_reply": "2024-09-26T17:07:50.976571Z"
+ "iopub.execute_input": "2024-09-27T13:54:16.725370Z",
+ "iopub.status.busy": "2024-09-27T13:54:16.725011Z",
+ "iopub.status.idle": "2024-09-27T13:54:20.576835Z",
+ "shell.execute_reply": "2024-09-27T13:54:20.576292Z"
}
},
"outputs": [
@@ -858,17 +858,17 @@
"id": "db0b5179",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T17:07:50.978894Z",
- "iopub.status.busy": "2024-09-26T17:07:50.978599Z",
- "iopub.status.idle": "2024-09-26T17:07:52.469581Z",
- "shell.execute_reply": "2024-09-26T17:07:52.468926Z"
+ "iopub.execute_input": "2024-09-27T13:54:20.578749Z",
+ "iopub.status.busy": "2024-09-27T13:54:20.578398Z",
+ "iopub.status.idle": "2024-09-27T13:54:22.071673Z",
+ "shell.execute_reply": "2024-09-27T13:54:22.071167Z"
}
},
"outputs": [
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- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "ead2da2da60e4805a2043cd58fc66569": {
+ "fa32975e7754475ba3d7e3d4cae1cee7": {
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"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2407,7 +2425,7 @@
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}
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+ "fdddcd906c904034b566400adef66822": {
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"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2459,24 +2477,6 @@
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}
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- "f9cbc0159e8e401daeafe1258f07445e": {
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- "model_name": "HTMLStyleModel",
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- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
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- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
}
},
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diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
index d173b02c1..77eaf7ec4 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": "2024-09-26T17:08:01.172227Z",
- "iopub.status.busy": "2024-09-26T17:08:01.172043Z",
- "iopub.status.idle": "2024-09-26T17:08:03.693417Z",
- "shell.execute_reply": "2024-09-26T17:08:03.692836Z"
+ "iopub.execute_input": "2024-09-27T13:54:30.682391Z",
+ "iopub.status.busy": "2024-09-27T13:54:30.682226Z",
+ "iopub.status.idle": "2024-09-27T13:54:32.499916Z",
+ "shell.execute_reply": "2024-09-27T13:54:32.499221Z"
}
},
"outputs": [
@@ -86,7 +86,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-09-26 17:08:01-- https://data.deepai.org/conll2003.zip\r\n",
+ "--2024-09-27 13:54:30-- https://data.deepai.org/conll2003.zip\r\n",
"Resolving data.deepai.org (data.deepai.org)... "
]
},
@@ -94,8 +94,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "169.150.236.105, 2400:52e0:1a00::1067:1\r\n",
- "Connecting to data.deepai.org (data.deepai.org)|169.150.236.105|:443... connected.\r\n",
+ "185.93.1.244, 2400:52e0:1a00::940:1\r\n",
+ "Connecting to data.deepai.org (data.deepai.org)|185.93.1.244|:443... connected.\r\n",
"HTTP request sent, awaiting response... 200 OK\r\n",
"Length: 982975 (960K) [application/zip]\r\n",
"Saving to: ‘conll2003.zip’\r\n",
@@ -109,9 +109,9 @@
"output_type": "stream",
"text": [
"\r",
- "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.01s \r\n",
+ "conll2003.zip 100%[===================>] 959.94K 5.72MB/s in 0.2s \r\n",
"\r\n",
- "2024-09-26 17:08:01 (95.3 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+ "2024-09-27 13:54:31 (5.72 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
"\r\n",
"mkdir: cannot create directory ‘data’: File exists\r\n"
]
@@ -131,16 +131,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-09-26 17:08:01-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
- "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.29.64, 3.5.16.102, 3.5.29.57, ...\r\n",
- "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.29.64|:443... "
+ "--2024-09-27 13:54:31-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+ "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.1.185, 3.5.27.97, 3.5.28.23, ...\r\n",
+ "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.1.185|:443... connected.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "connected.\r\n",
"HTTP request sent, awaiting response... "
]
},
@@ -161,7 +160,7 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 2%[ ] 391.92K 1.81MB/s "
+ "pred_probs.npz 10%[=> ] 1.67M 8.13MB/s "
]
},
{
@@ -169,7 +168,7 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 6%[> ] 1.02M 2.40MB/s "
+ "pred_probs.npz 30%[=====> ] 4.95M 12.0MB/s "
]
},
{
@@ -177,7 +176,7 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 12%[=> ] 1.98M 3.12MB/s "
+ "pred_probs.npz 63%[===========> ] 10.30M 16.7MB/s "
]
},
{
@@ -185,41 +184,9 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 21%[===> ] 3.50M 4.13MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 36%[======> ] 5.85M 5.49MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 58%[==========> ] 9.48M 7.48MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 89%[================> ] 14.59M 9.94MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 100%[===================>] 16.26M 10.7MB/s in 1.5s \r\n",
+ "pred_probs.npz 100%[===================>] 16.26M 21.2MB/s in 0.8s \r\n",
"\r\n",
- "2024-09-26 17:08:03 (10.7 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+ "2024-09-27 13:54:32 (21.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
"\r\n"
]
}
@@ -236,10 +203,10 @@
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+ "iopub.status.idle": "2024-09-27T13:54:33.874271Z",
+ "shell.execute_reply": "2024-09-27T13:54:33.873712Z"
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@@ -250,7 +217,7 @@
"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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -276,10 +243,10 @@
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- "iopub.status.idle": "2024-09-26T17:08:05.020348Z",
- "shell.execute_reply": "2024-09-26T17:08:05.019888Z"
+ "iopub.execute_input": "2024-09-27T13:54:33.876182Z",
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+ "shell.execute_reply": "2024-09-27T13:54:33.878885Z"
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@@ -350,10 +317,10 @@
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}
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+ "iopub.execute_input": "2024-09-27T13:54:43.004773Z",
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@@ -470,10 +437,10 @@
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@@ -585,10 +552,10 @@
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@@ -610,10 +577,10 @@
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@@ -649,10 +616,10 @@
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@@ -1187,10 +1154,10 @@
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+ "shell.execute_reply": "2024-09-27T13:54:47.426150Z"
},
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diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 18235ca5f..9660e8c4c 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 e595ac996..8f12b18d3 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 9ed73d61e..96f365a6d 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 2a38619dd..082aad5a2 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 290d2d68e..a6323a6fc 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb
index 1a72cc0e5..2cce8dad7 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 43236d6a2..624ffee91 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 4a0863913..15e6e837c 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/improving_ml_performance.ipynb b/master/_sources/tutorials/improving_ml_performance.ipynb
index 23cfb45bd..f7773bd33 100644
--- a/master/_sources/tutorials/improving_ml_performance.ipynb
+++ b/master/_sources/tutorials/improving_ml_performance.ipynb
@@ -67,7 +67,7 @@
"dependencies = [\"cleanlab\", \"xgboost\", \"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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 fe6e0dd4b..a0a135a40 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 b5d9c7ad8..627ac5c8e 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 5b85852ef..0a42c04eb 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 09816c99e..dabee06ee 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 e7633fa88..84e31edbd 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 c853be875..0e6481870 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 c583fedf7..60ceff194 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 e030a0975..ca01c8f89 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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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 83e19088d..30df2fc81 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
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Train a more robust model from noisy labels": [[88, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Spending too much time on data quality?": [[88, "Spending-too-much-time-on-data-quality?"], [89, "Spending-too-much-time-on-data-quality?"], [92, "Spending-too-much-time-on-data-quality?"], [95, "Spending-too-much-time-on-data-quality?"], [96, "Spending-too-much-time-on-data-quality?"], [98, "Spending-too-much-time-on-data-quality?"], [101, "Spending-too-much-time-on-data-quality?"], [104, "Spending-too-much-time-on-data-quality?"], [106, "Spending-too-much-time-on-data-quality?"], [107, "spending-too-much-time-on-data-quality"], [108, "Spending-too-much-time-on-data-quality?"]], "Text Classification with Noisy Labels": [[89, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[89, "2.-Load-and-format-the-text-dataset"], [96, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[89, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[89, "4.-Train-a-more-robust-model-from-noisy-labels"], [108, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[90, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[90, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[90, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[90, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[90, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[90, "5.-Use-cleanlab-to-find-label-issues"], [95, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[91, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[91, "Install-and-import-required-dependencies"]], "Create and load the data": [[91, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[91, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[91, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[91, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[91, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[91, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[91, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[92, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[92, "1.-Install-and-import-required-dependencies"], [93, "1.-Install-and-import-required-dependencies"], [103, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[92, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[92, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[92, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[92, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[92, "Get-additional-information"]], "Near duplicate issues": [[92, "Near-duplicate-issues"], [93, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[93, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[93, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[93, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[93, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[93, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[93, "7.-Use-cleanlab-to-find-issues"]], "View report": [[93, "View-report"]], "Label issues": [[93, "Label-issues"], [95, "Label-issues"], [96, "Label-issues"]], "View most likely examples with label errors": [[93, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[93, "Outlier-issues"], [95, "Outlier-issues"], [96, "Outlier-issues"]], "View most severe outliers": [[93, "View-most-severe-outliers"]], "View sets of near duplicate images": [[93, "View-sets-of-near-duplicate-images"]], "Dark images": [[93, "Dark-images"]], "View top examples of dark images": [[93, "View-top-examples-of-dark-images"]], "Low information images": [[93, "Low-information-images"]], "Datalab Tutorials": [[94, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[95, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[95, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[95, "Near-duplicate-issues"], [96, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[96, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[96, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[96, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[96, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[97, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[97, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[97, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[97, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[97, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[97, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[97, "Explanation:"]], "Data Valuation": [[97, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[97, "1.-Load-and-Prepare-the-Dataset"], [97, "id2"], [97, "id5"]], "2. Vectorize the Text Data": [[97, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[97, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[97, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[97, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[97, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[97, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [97, "id3"]], "3. (Optional) Cluster the Data": [[97, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[97, "4.-Identify-Underperforming-Groups-with-Datalab"], [97, "id4"]], "5. (Optional) Visualize the Results": [[97, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[97, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[97, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[97, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[97, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[97, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[97, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[97, "1.-Load-the-Dataset"], [97, "id8"]], "2: Encode Categorical Values": [[97, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[97, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[97, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[97, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[97, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[97, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[97, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[97, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[97, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[97, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[97, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[97, "3.-Interpret-the-Results"]], "Understanding Dataset-level Labeling Issues": [[98, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[98, "Install-dependencies-and-import-them"], [101, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[98, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[98, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[99, "FAQ"]], "What data can cleanlab detect issues in?": [[99, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[99, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[99, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[99, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[99, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[99, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[99, "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?": [[99, "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?": [[99, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[99, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[99, "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?": [[99, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[99, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[99, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[100, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[100, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[100, "1.-Install-dependencies"]], "2. Preprocess the data": [[100, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[100, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[100, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[100, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[100, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[100, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[100, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[100, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[100, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[101, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[101, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[101, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[101, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[101, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[101, "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.": [[101, "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": [[101, "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": [[101, "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!": [[101, "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": [[101, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[101, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[101, "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)": [[101, "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:": [[101, "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": [[101, "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.": [[101, "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.": [[101, "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.": [[101, "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.": [[101, "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?": [[101, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[101, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[102, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[103, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[103, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[103, "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": [[103, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[103, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[103, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[103, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[103, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[103, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[104, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[104, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[104, "2.-Format-data,-labels,-and-model-predictions"], [105, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[104, "3.-Use-cleanlab-to-find-label-issues"], [105, "3.-Use-cleanlab-to-find-label-issues"], [109, "3.-Use-cleanlab-to-find-label-issues"], [110, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[104, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[104, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[104, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[104, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[104, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[105, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[105, "1.-Install-required-dependencies-and-download-data"], [109, "1.-Install-required-dependencies-and-download-data"], [110, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[105, "Get-label-quality-scores"], [109, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[105, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[105, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[105, "Other-uses-of-visualize"]], "Exploratory data analysis": [[105, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[106, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[106, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[106, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[106, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[106, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[106, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[107, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[107, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[107, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[108, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[108, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[108, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[109, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[109, "2.-Get-data,-labels,-and-pred_probs"], [110, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[109, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[109, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[109, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[110, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[110, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[110, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[110, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[110, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"Creating Your Own Issues Manager": [[7, "creating-your-own-issues-manager"]], "Prerequisites": [[7, "prerequisites"]], "Implementing IssueManagers": [[7, "implementing-issuemanagers"]], "Basic Issue Check": [[7, "basic-issue-check"]], "Intermediate Issue Check": [[7, "intermediate-issue-check"]], "Advanced Issue Check": [[7, "advanced-issue-check"]], "Use with Datalab": [[7, "use-with-datalab"]], "Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Inputs to Datalab": [[10, "inputs-to-datalab"]], "Label Issue": [[10, 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Install cleanlab": [[85, "install-cleanlab"]], "2. Check your data for all sorts of issues": [[85, "check-your-data-for-all-sorts-of-issues"]], "3. Handle label errors and train robust models with noisy labels": [[85, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[85, "dataset-curation-fix-dataset-level-issues"]], "5. 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Install required dependencies": [[88, "1.-Install-required-dependencies"], [89, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [96, "1.-Install-required-dependencies"], [108, "1.-Install-required-dependencies"]], "2. Load and process the data": [[88, "2.-Load-and-process-the-data"], [95, "2.-Load-and-process-the-data"], [108, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[88, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [95, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[88, "4.-Use-cleanlab-to-find-label-issues"]], "5. 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Train a Classifier and Obtain Predicted Probabilities": [[97, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[97, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[97, "Explanation:"]], "Data Valuation": [[97, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[97, "1.-Load-and-Prepare-the-Dataset"], [97, "id2"], [97, "id5"]], "2. Vectorize the Text Data": [[97, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[97, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[97, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[97, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[97, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[97, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [97, "id3"]], "3. (Optional) Cluster the Data": [[97, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[97, "4.-Identify-Underperforming-Groups-with-Datalab"], [97, "id4"]], "5. (Optional) Visualize the Results": [[97, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[97, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[97, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[97, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[97, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[97, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[97, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[97, "1.-Load-the-Dataset"], [97, "id8"]], "2: Encode Categorical Values": [[97, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[97, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[97, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[97, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[97, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[97, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[97, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[97, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[97, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[97, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[97, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[97, "3.-Interpret-the-Results"]], "Understanding Dataset-level Labeling Issues": [[98, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[98, "Install-dependencies-and-import-them"], [101, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[98, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[98, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[99, "FAQ"]], "What data can cleanlab detect issues in?": [[99, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[99, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[99, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[99, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[99, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[99, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[99, "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?": [[99, "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?": [[99, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[99, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[99, "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?": [[99, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[99, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[99, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[100, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[100, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[100, "1.-Install-dependencies"]], "2. Preprocess the data": [[100, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[100, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[100, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[100, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[100, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[100, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[100, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[100, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[100, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[101, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[101, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[101, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[101, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[101, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[101, "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.": [[101, "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": [[101, "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": [[101, "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!": [[101, "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": [[101, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[101, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[101, "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)": [[101, "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:": [[101, "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": [[101, "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.": [[101, "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.": [[101, "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.": [[101, "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.": [[101, "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?": [[101, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[101, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[102, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[103, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[103, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[103, "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": [[103, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[103, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[103, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[103, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[103, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[103, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[104, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[104, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[104, "2.-Format-data,-labels,-and-model-predictions"], [105, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[104, "3.-Use-cleanlab-to-find-label-issues"], [105, "3.-Use-cleanlab-to-find-label-issues"], [109, "3.-Use-cleanlab-to-find-label-issues"], [110, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[104, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[104, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[104, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[104, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[104, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[105, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[105, "1.-Install-required-dependencies-and-download-data"], [109, "1.-Install-required-dependencies-and-download-data"], [110, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[105, "Get-label-quality-scores"], [109, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[105, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[105, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[105, "Other-uses-of-visualize"]], "Exploratory data analysis": [[105, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[106, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[106, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[106, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[106, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[106, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[106, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[107, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[107, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[107, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[108, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[108, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[108, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[109, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[109, "2.-Get-data,-labels,-and-pred_probs"], [110, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[109, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[109, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[109, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[110, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[110, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[110, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[110, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[110, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[66, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[67, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[68, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[68, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[69, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[70, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[71, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[72, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[72, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[73, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[74, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[75, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[75, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[75, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[76, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[76, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[77, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[77, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[78, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[79, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[80, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[81, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[81, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[82, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[83, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[84, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[84, "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 b648c52ba..388c995f2 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": "2024-09-26T16:57:46.905901Z",
- "iopub.status.busy": "2024-09-26T16:57:46.905412Z",
- "iopub.status.idle": "2024-09-26T16:57:48.220424Z",
- "shell.execute_reply": "2024-09-26T16:57:48.219845Z"
+ "iopub.execute_input": "2024-09-27T13:44:12.200916Z",
+ "iopub.status.busy": "2024-09-27T13:44:12.200561Z",
+ "iopub.status.idle": "2024-09-27T13:44:13.479668Z",
+ "shell.execute_reply": "2024-09-27T13:44:13.479088Z"
},
"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@4a1a1fc4e03d74f176fb1a05e67805e9548be4ff\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@58573e181a2e4beba7f8f4ed160356a7505ee223\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": "2024-09-26T16:57:48.222820Z",
- "iopub.status.busy": "2024-09-26T16:57:48.222253Z",
- "iopub.status.idle": "2024-09-26T16:57:48.241708Z",
- "shell.execute_reply": "2024-09-26T16:57:48.241070Z"
+ "iopub.execute_input": "2024-09-27T13:44:13.482034Z",
+ "iopub.status.busy": "2024-09-27T13:44:13.481452Z",
+ "iopub.status.idle": "2024-09-27T13:44:13.500047Z",
+ "shell.execute_reply": "2024-09-27T13:44:13.499596Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:48.243929Z",
- "iopub.status.busy": "2024-09-26T16:57:48.243487Z",
- "iopub.status.idle": "2024-09-26T16:57:48.451471Z",
- "shell.execute_reply": "2024-09-26T16:57:48.450893Z"
+ "iopub.execute_input": "2024-09-27T13:44:13.502039Z",
+ "iopub.status.busy": "2024-09-27T13:44:13.501593Z",
+ "iopub.status.idle": "2024-09-27T13:44:13.696938Z",
+ "shell.execute_reply": "2024-09-27T13:44:13.696313Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:48.483707Z",
- "iopub.status.busy": "2024-09-26T16:57:48.483195Z",
- "iopub.status.idle": "2024-09-26T16:57:48.487154Z",
- "shell.execute_reply": "2024-09-26T16:57:48.486583Z"
+ "iopub.execute_input": "2024-09-27T13:44:13.729165Z",
+ "iopub.status.busy": "2024-09-27T13:44:13.728951Z",
+ "iopub.status.idle": "2024-09-27T13:44:13.732830Z",
+ "shell.execute_reply": "2024-09-27T13:44:13.732365Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:48.488961Z",
- "iopub.status.busy": "2024-09-26T16:57:48.488621Z",
- "iopub.status.idle": "2024-09-26T16:57:48.496919Z",
- "shell.execute_reply": "2024-09-26T16:57:48.496323Z"
+ "iopub.execute_input": "2024-09-27T13:44:13.734478Z",
+ "iopub.status.busy": "2024-09-27T13:44:13.734300Z",
+ "iopub.status.idle": "2024-09-27T13:44:13.742648Z",
+ "shell.execute_reply": "2024-09-27T13:44:13.742221Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:48.498974Z",
- "iopub.status.busy": "2024-09-26T16:57:48.498629Z",
- "iopub.status.idle": "2024-09-26T16:57:48.500983Z",
- "shell.execute_reply": "2024-09-26T16:57:48.500538Z"
+ "iopub.execute_input": "2024-09-27T13:44:13.744355Z",
+ "iopub.status.busy": "2024-09-27T13:44:13.744172Z",
+ "iopub.status.idle": "2024-09-27T13:44:13.746680Z",
+ "shell.execute_reply": "2024-09-27T13:44:13.746217Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:48.502678Z",
- "iopub.status.busy": "2024-09-26T16:57:48.502341Z",
- "iopub.status.idle": "2024-09-26T16:57:49.032323Z",
- "shell.execute_reply": "2024-09-26T16:57:49.031807Z"
+ "iopub.execute_input": "2024-09-27T13:44:13.748214Z",
+ "iopub.status.busy": "2024-09-27T13:44:13.748042Z",
+ "iopub.status.idle": "2024-09-27T13:44:14.270554Z",
+ "shell.execute_reply": "2024-09-27T13:44:14.269884Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:49.034548Z",
- "iopub.status.busy": "2024-09-26T16:57:49.034196Z",
- "iopub.status.idle": "2024-09-26T16:57:50.968947Z",
- "shell.execute_reply": "2024-09-26T16:57:50.968319Z"
+ "iopub.execute_input": "2024-09-27T13:44:14.272696Z",
+ "iopub.status.busy": "2024-09-27T13:44:14.272497Z",
+ "iopub.status.idle": "2024-09-27T13:44:16.167242Z",
+ "shell.execute_reply": "2024-09-27T13:44:16.166648Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:50.971511Z",
- "iopub.status.busy": "2024-09-26T16:57:50.970708Z",
- "iopub.status.idle": "2024-09-26T16:57:50.981203Z",
- "shell.execute_reply": "2024-09-26T16:57:50.980707Z"
+ "iopub.execute_input": "2024-09-27T13:44:16.169775Z",
+ "iopub.status.busy": "2024-09-27T13:44:16.169000Z",
+ "iopub.status.idle": "2024-09-27T13:44:16.179484Z",
+ "shell.execute_reply": "2024-09-27T13:44:16.179037Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:50.983160Z",
- "iopub.status.busy": "2024-09-26T16:57:50.982812Z",
- "iopub.status.idle": "2024-09-26T16:57:50.986985Z",
- "shell.execute_reply": "2024-09-26T16:57:50.986552Z"
+ "iopub.execute_input": "2024-09-27T13:44:16.181448Z",
+ "iopub.status.busy": "2024-09-27T13:44:16.181041Z",
+ "iopub.status.idle": "2024-09-27T13:44:16.185086Z",
+ "shell.execute_reply": "2024-09-27T13:44:16.184632Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:50.988771Z",
- "iopub.status.busy": "2024-09-26T16:57:50.988449Z",
- "iopub.status.idle": "2024-09-26T16:57:50.996238Z",
- "shell.execute_reply": "2024-09-26T16:57:50.995660Z"
+ "iopub.execute_input": "2024-09-27T13:44:16.186814Z",
+ "iopub.status.busy": "2024-09-27T13:44:16.186483Z",
+ "iopub.status.idle": "2024-09-27T13:44:16.194898Z",
+ "shell.execute_reply": "2024-09-27T13:44:16.194442Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:50.998409Z",
- "iopub.status.busy": "2024-09-26T16:57:50.997938Z",
- "iopub.status.idle": "2024-09-26T16:57:51.112743Z",
- "shell.execute_reply": "2024-09-26T16:57:51.112140Z"
+ "iopub.execute_input": "2024-09-27T13:44:16.196580Z",
+ "iopub.status.busy": "2024-09-27T13:44:16.196252Z",
+ "iopub.status.idle": "2024-09-27T13:44:16.309588Z",
+ "shell.execute_reply": "2024-09-27T13:44:16.309001Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:51.114671Z",
- "iopub.status.busy": "2024-09-26T16:57:51.114330Z",
- "iopub.status.idle": "2024-09-26T16:57:51.117374Z",
- "shell.execute_reply": "2024-09-26T16:57:51.116803Z"
+ "iopub.execute_input": "2024-09-27T13:44:16.311378Z",
+ "iopub.status.busy": "2024-09-27T13:44:16.311198Z",
+ "iopub.status.idle": "2024-09-27T13:44:16.314110Z",
+ "shell.execute_reply": "2024-09-27T13:44:16.313548Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:51.119122Z",
- "iopub.status.busy": "2024-09-26T16:57:51.118777Z",
- "iopub.status.idle": "2024-09-26T16:57:53.250696Z",
- "shell.execute_reply": "2024-09-26T16:57:53.249828Z"
+ "iopub.execute_input": "2024-09-27T13:44:16.315717Z",
+ "iopub.status.busy": "2024-09-27T13:44:16.315450Z",
+ "iopub.status.idle": "2024-09-27T13:44:18.461870Z",
+ "shell.execute_reply": "2024-09-27T13:44:18.461184Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:53.253432Z",
- "iopub.status.busy": "2024-09-26T16:57:53.252773Z",
- "iopub.status.idle": "2024-09-26T16:57:53.264456Z",
- "shell.execute_reply": "2024-09-26T16:57:53.263964Z"
+ "iopub.execute_input": "2024-09-27T13:44:18.464456Z",
+ "iopub.status.busy": "2024-09-27T13:44:18.463827Z",
+ "iopub.status.idle": "2024-09-27T13:44:18.475330Z",
+ "shell.execute_reply": "2024-09-27T13:44:18.474881Z"
}
},
"outputs": [
@@ -786,10 +786,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-26T16:57:53.266337Z",
- "iopub.status.busy": "2024-09-26T16:57:53.265982Z",
- "iopub.status.idle": "2024-09-26T16:57:53.320394Z",
- "shell.execute_reply": "2024-09-26T16:57:53.319936Z"
+ "iopub.execute_input": "2024-09-27T13:44:18.476970Z",
+ "iopub.status.busy": "2024-09-27T13:44:18.476794Z",
+ "iopub.status.idle": "2024-09-27T13:44:18.534040Z",
+ "shell.execute_reply": "2024-09-27T13:44:18.533545Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index c663bca7f..102113368 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -830,7 +830,7 @@ 2. Load and format the text dataset
+
|
- Age |
- Gender |
- Location |
- Annual_Spending |
- Number_of_Transactions |
- Last_Purchase_Date |
- | |
- is_null_issue |
- null_score |
+ Age |
+ Gender |
+ Location |
+ Annual_Spending |
+ Number_of_Transactions |
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diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb
index 0f1a9673c..7a4a275e8 100644
--- a/master/tutorials/dataset_health.ipynb
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diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html
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