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kkim-labelboxwhistlerKevin KimVal Brodskyvbrodsky
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Release 3.45.0 (#1075)
Co-authored-by: Ibrahim Muhammad <[email protected]> Co-authored-by: Kevin Kim <[email protected]> Co-authored-by: Val Brodsky <[email protected]> Co-authored-by: Val Brodsky <[email protected]> Co-authored-by: Klaus Opreschko <[email protected]> Co-authored-by: Klaus Opreschko <[email protected]> Co-authored-by: mnoszczak <[email protected]> Co-authored-by: Andrea Ovalle <[email protected]> Co-authored-by: Dmitriy <[email protected]> Co-authored-by: Dmitriy Apollonin <[email protected]> Co-authored-by: Zeke <[email protected]> Co-authored-by: Richard Sun <[email protected]> Co-authored-by: Yamini Kancharana <[email protected]> Co-authored-by: Jovan Chohan <[email protected]> Co-authored-by: rahullb <[email protected]> Co-authored-by: rahul sharma <[email protected]> Co-authored-by: rahul sharma <[email protected]> Co-authored-by: ovalle15 <[email protected]>
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‎CHANGELOG.md

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# Changelog
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# Version 3.45.0 (2023-04-27)
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## Changed
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* Reduce threshold for async batch creation to 1000 data rows
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## Notebooks
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* Added subclassifications to ontology notebook
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* Added conversational and pdf predictions notebooks
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# Version 3.44.0 (2023-04-26)
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## Added

‎docs/source/conf.py

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copyright = '2021, Labelbox'
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author = 'Labelbox'
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release = '3.44.0'
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release = '3.45.0'
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# -- General configuration ---------------------------------------------------
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‎examples/annotation_import/conversational.ipynb

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"* Classification Checklist \n",
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"* Classification Free Text \n",
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"* NER\n",
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"* Relationships (only supported for MAL imports)\n",
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"\n",
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"**Not** supported annotations\n",
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"\n",
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],
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"cell_type": "markdown"
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},
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{
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"metadata": {},
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"source": [
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"### NDJSON Annotations "
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],
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"cell_type": "markdown"
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},
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{
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"metadata": {},
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"source": [
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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"# ############ global nested classifications ###########\n",
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"# Message based\n",
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"nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
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" name=\"nested_checklist_question\",\n",
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" message_id=\"10\",\n",
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" value=lb_types.Checklist(\n",
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" answer=[lb_types.ClassificationAnswer(\n",
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" name=\"first_checklist_answer\",\n",
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" classifications=[\n",
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" lb_types.ClassificationAnnotation(\n",
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" name=\"sub_checklist_question\",\n",
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" value=lb_types.Checklist(\n",
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" answer=[lb_types.ClassificationAnswer(\n",
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" name=\"first_sub_checklist_answer\",\n",
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"\n",
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" )]\n",
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" ))\n",
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" ]\n",
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" )]\n",
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" )\n",
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")\n",
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"# Message based\n",
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"nested_checklist_annotation_ndjson = {\n",
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" \"name\": \"nested_checklist_question\",\n",
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" \"messageId\": \"10\",\n",
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" \"answer\": [{\n",
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" \"name\": \"first_checklist_answer\",\n",
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" \"classifications\" : [\n",
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" {\n",
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" \"name\": \"sub_checklist_question\", \n",
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" \"answer\": {\n",
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" \"name\": \"first_sub_checklist_answer\"\n",
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" }\n",
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" } \n",
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" ] \n",
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" }]\n",
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"}\n",
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"# Global\n",
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"nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
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" name=\"nested_radio_question\",\n",
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" value=lb_types.Radio(\n",
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" answer=lb_types.ClassificationAnswer(\n",
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" name=\"first_radio_answer\",\n",
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" classifications=[\n",
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" lb_types.ClassificationAnnotation(\n",
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" name=\"sub_radio_question\",\n",
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" value=lb_types.Radio(\n",
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" answer=lb_types.ClassificationAnswer(\n",
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" name=\"first_sub_radio_answer\"\n",
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" )\n",
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" )\n",
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" )\n",
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" ]\n",
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" )\n",
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" )\n",
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")\n",
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"\n",
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"# Global\n",
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"nested_radio_annotation_ndjson = {\n",
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" \"name\": \"nested_radio_question\",\n",
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" \"answer\": {\n",
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" \"name\": \"first_radio_answer\",\n",
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" \"classifications\": [{\n",
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" \"name\":\"sub_radio_question\",\n",
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" \"answer\": { \"name\" : \"first_sub_radio_answer\"}\n",
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" }]\n",
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" }\n",
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"}\n",
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"\n"
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],
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"cell_type": "code",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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" lb.Option(value=\"first_radio_answer\"),\n",
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" lb.Option(value=\"second_radio_answer\")\n",
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" ]\n",
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" ),\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.CHECKLIST,\n",
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" name=\"nested_checklist_question\",\n",
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" scope = lb.Classification.Scope.INDEX,\n",
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" options=[\n",
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" lb.Option(\"first_checklist_answer\",\n",
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" options=[\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.CHECKLIST,\n",
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" name=\"sub_checklist_question\", \n",
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" options=[lb.Option(\"first_sub_checklist_answer\")]\n",
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" )\n",
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" ])\n",
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" ]\n",
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" ),\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.RADIO,\n",
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" name=\"nested_radio_question\",\n",
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" scope = lb.Classification.Scope.GLOBAL,\n",
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" options=[\n",
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" lb.Option(\"first_radio_answer\",\n",
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" options=[\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.RADIO,\n",
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" name=\"sub_radio_question\",\n",
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" options=[lb.Option(\"first_sub_radio_answer\")]\n",
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" )\n",
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" ])\n",
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" ]\n",
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" )\n",
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" ]\n",
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")\n",
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"metadata": {},
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"source": [
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"# Create Labelbox project\n",
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"project = client.create_project(name=\"conversational_project\", \n",
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"project = client.create_project(name=\"Conversational Text Annotation Import Demo\", \n",
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" media_type=lb.MediaType.Conversational)\n",
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"\n",
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"# Setup your ontology \n",
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" radio_annotation,\n",
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" ner_source,\n",
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" ner_target,\n",
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" ner_relationship\n",
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" ner_relationship,\n",
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" nested_radio_annotation,\n",
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" nested_checklist_annotation\n",
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" ]\n",
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" )\n",
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")"
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" ner_source_ndjson,\n",
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" ner_target_ndjson,\n",
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" ner_relationship_annotation_ndjson,\n",
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" nested_checklist_annotation_ndjson,\n",
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" nested_radio_annotation_ndjson\n",
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" ]:\n",
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" annotations.update({\n",
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" \"dataRow\": {\n",

‎examples/basics/ontologies.ipynb

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{
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"metadata": {},
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"source": [
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"!pip install labelbox"
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"!pip install labelbox -q"
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],
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"cell_type": "code",
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"outputs": [],
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"metadata": {},
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"source": [
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"# Add your api key\n",
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"API_KEY = None\n",
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"API_KEY = \"Enter api key here\"\n",
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"client = lb.Client(api_key=API_KEY)"
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],
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"cell_type": "code",
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{
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"metadata": {},
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"source": [
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"### Update and Delete\n",
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"- At this time, these options are not supported from the SDK.\n",
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"- Updating an ontology is dangerous and could cause labels to be hidden. \n",
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" - Use caution when doing so"
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"### Update and Delete"
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],
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"cell_type": "markdown"
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},
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{
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"metadata": {},
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"source": [
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"# Check if feature is archived\n",
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"feature_schema = next(client.get_feature_schemas(\"cat\"))\n",
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"client.is_feature_schema_archived(ontology_id=ontology.uid, feature_schema_id=feature_schema.uid)"
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],
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"cell_type": "code",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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"Delete or Archived a feature:\n",
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"\n",
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"If the feature schema is a root level node with associated labels, it will be archived.\n",
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"If the feature schema is a nested node in the ontology and does not have associated labels, it will be deleted.\n",
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"If the feature schema is a nested node in the ontology and has associated labels, it will not be deleted."
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],
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"cell_type": "markdown"
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},
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{
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"metadata": {},
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"source": [
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"client.delete_feature_schema_from_ontology(ontology_id=ontology.uid, feature_schema_id=feature_schema.uid)"
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],
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"cell_type": "code",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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" name=\"cat\",\n",
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" classifications=[\n",
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" lb.Classification(class_type=lb.Classification.Type.TEXT,\n",
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" instructions=\"name\")\n",
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" name=\"name\")\n",
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" ])\n",
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" ],\n",
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" classifications=[\n",
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" lb.Classification(class_type=lb.Classification.Type.RADIO,\n",
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" instructions=\"image_quality\",\n",
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" name=\"image_quality\",\n",
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" options=[lb.Option(value=\"clear\"),\n",
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" lb.Option(value=\"blurry\")])\n",
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" ])\n",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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"Example of how to add sub-classfication within an option"
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],
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"cell_type": "markdown"
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},
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{
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"metadata": {},
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"source": [
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"# We will use add_classification to add this classification to a previously built ontology_builder or you can create new ontology_builder = OntologyBuilder() \n",
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"radio_classification = lb.Classification(class_type=lb.Classification.Type.RADIO,\n",
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" name=\"Global classification\",\n",
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" options=[lb.Option(\"1st option\", options=[lb.Classification(class_type=lb.Classification.Type.CHECKLIST,\n",
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" name=\"Inside 1st option\",\n",
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" options=[lb.Option(\"Option A\"), lb.Option(\"Option B\")])]), lb.Option(\"2nd option\", options=[lb.Classification(class_type=lb.Classification.Type.CHECKLIST,\n",
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" name=\"Inside 2nd option\",\n",
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" options=[lb.Option(\"Option A\"), lb.Option(\"Option B\")])])])\n",
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"\n",
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"ontology_builder.add_classification(radio_classification) \n",
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"\n",
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"ontology = client.create_ontology(\"example of nested classification\",\n",
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" ontology_builder.asdict())\n",
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"print(json.dumps(ontology.normalized, indent=2))"
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],
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"cell_type": "code",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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"metadata": {},
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"source": [
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"text_classification = lb.Classification(class_type=lb.Classification.Type.TEXT,\n",
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" instructions=\"dog_name\")\n",
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" name=\"dog_name\")\n",
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"radio_classification = lb.Classification(class_type=lb.Classification.Type.RADIO,\n",
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" instructions=\"dog_breed\",\n",
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" name=\"dog_breed\",\n",
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" options=[lb.Option(\"poodle\")])\n",
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"checklist_classification = lb.Classification(\n",
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" class_type=lb.Classification.Type.CHECKLIST,\n",
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" instructions=\"background\",\n",
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" name=\"background\",\n",
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" options=[lb.Option(\"at_park\"), lb.Option(\"has_leash\")])"
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],
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"cell_type": "code",

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