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‎CHANGELOG.md

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# Changelog
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# Version 3.58.0 (2023-12-11)
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## Added
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* `ontology_id` to the model app instantiation
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* LLM data generation label types
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* `run_foundry_app` to support running model foundry apps
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* Two methods for sending data rows to any workflow task in a project, that can also include predictions from a model run, or annotations from a different project
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## Fixed
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* Documentation index for identifiables
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## Removed
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* Project.datasets and Datasets.projects methods as they have been deprecated
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## Notebooks
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* Added note books for Human labeling(GT/MAL/MEA) + data generation (GT/MAL)
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* Remove relationship annotations from text and conversational imports
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# Version 3.57.0 (2023-11-30)
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## Added
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* Global key support for Project move_data_rows_to_task_queue

‎Makefile

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@@ -45,6 +45,15 @@ test-onprem: build-image
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-e LABELBOX_TEST_ONPREM_HOSTNAME=${LABELBOX_TEST_ONPREM_HOSTNAME} \
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local/labelbox-python:test pytest $(PATH_TO_TEST)
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test-dev0: build-image
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docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \
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-e LABELBOX_TEST_ENVIRON="custom" \
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-e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \
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-e LABELBOX_TEST_API_KEY_CUSTOM=${LABELBOX_TEST_API_KEY_CUSTOM} \
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-e LABELBOX_TEST_GRAPHQL_API_ENDPOINT="https://api.dev0.na-us.lb-dev.xyz/graphql" \
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-e LABELBOX_TEST_REST_API_ENDPOINT="https://api.dev0.na-us.lb-dev.xyz/api/v1" \
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local/labelbox-python:test pytest $(PATH_TO_TEST)
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test-custom: build-image
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docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \
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-e LABELBOX_TEST_ENVIRON="custom" \

‎docs/source/conf.py

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copyright = '2021, Labelbox'
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author = 'Labelbox'
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release = '3.57.0'
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release = '3.58.0'
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# -- General configuration ---------------------------------------------------
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‎docs/source/index.rst

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---------------------------
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.. automodule:: labelbox.schema.export_task
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:members:
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:exclude-members: FileRetrieverByLine, FileRetrieverByOffset, FileRetrieverStrategy, Range, Converter
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:show-inheritance:
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Identifiables

‎examples/annotation_import/conversational.ipynb

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"# Conversational Text Annotation Import\n",
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"* This notebook will provide examples of each supported annotation type for conversational text assets, and also cover MAL and Label Import methods:\n",
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"\n",
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"Suported annotations that can be uploaded through the SDK\n",
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"Supported annotations that can be uploaded through the SDK\n",
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"\n",
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"* Classification Radio \n",
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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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"\n",
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"**Not** supported annotations\n",
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"\n",
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"* Relationships\n",
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"* Bouding box \n",
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"* Polygon \n",
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"* Point\n",
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" )\n",
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")\n",
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"\n",
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"ner_annotation_ndjson = { \n",
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"ner_annotation_ndjson = {\n",
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" \"name\": \"ner\",\n",
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" \"location\": { \n",
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" \"start\": 0, \n",
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" \"end\": 8 \n",
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" \"location\": {\n",
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" \"start\": 0,\n",
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" \"end\": 8\n",
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" },\n",
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" \"messageId\": \"4\"\n",
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" }"
@@ -177,15 +178,15 @@
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{
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"metadata": {},
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"source": [
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"##### Checklist Classification ####### \n",
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"##### Checklist Classification #######\n",
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"\n",
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"checklist_annotation= lb_types.ClassificationAnnotation(\n",
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" name=\"checklist_convo\", # must match your ontology feature\"s name\n",
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" value=lb_types.Checklist(\n",
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" answer = [\n",
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" lb_types.ClassificationAnswer(\n",
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" name = \"first_checklist_answer\"\n",
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" ), \n",
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" ),\n",
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" lb_types.ClassificationAnswer(\n",
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" name = \"second_checklist_answer\"\n",
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" )\n",
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"######## Radio Classification ######\n",
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"\n",
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"radio_annotation = lb_types.ClassificationAnnotation(\n",
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" name=\"radio_convo\", \n",
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" name=\"radio_convo\",\n",
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" value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"first_radio_answer\")),\n",
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" message_id=\"0\"\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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"####### Relationships ########## \n",
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"ner_source = lb_types.ObjectAnnotation(\n",
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" name=\"ner\",\n",
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" value=lb_types.ConversationEntity(\n",
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" start=16,\n",
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" end=26,\n",
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" message_id=\"4\"\n",
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" )\n",
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")\n",
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"ner_target = lb_types.ObjectAnnotation(\n",
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" name=\"ner\",\n",
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" value=lb_types.ConversationEntity(\n",
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" start=29, \n",
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" end=34, \n",
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" message_id=\"4\"\n",
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" )\n",
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")\n",
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"\n",
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"ner_relationship = lb_types.RelationshipAnnotation(\n",
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" name=\"relationship\",\n",
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" value=lb_types.Relationship(\n",
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" source=ner_source,\n",
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" target=ner_target,\n",
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" type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n",
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" ))\n",
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"\n",
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"uuid_source = str(uuid.uuid4())\n",
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"uuid_target = str(uuid.uuid4())\n",
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"\n",
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"ner_source_ndjson = { \n",
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" \"uuid\": uuid_source, \n",
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" \"name\": \"ner\",\n",
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" \"location\": { \n",
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" \"start\": 16, \n",
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" \"end\": 26 \n",
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" },\n",
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" \"messageId\": \"4\"\n",
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" }\n",
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"\n",
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"ner_target_ndjson = { \n",
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" \"uuid\": uuid_target,\n",
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" \"name\": \"ner\",\n",
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" \"location\": { \n",
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" \"start\": 29, \n",
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" \"end\": 34\n",
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" },\n",
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" \"messageId\": \"4\"\n",
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" }\n",
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"\n",
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"ner_relationship_annotation_ndjson = {\n",
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" \"name\": \"relationship\", \n",
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" \"relationship\": {\n",
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" \"source\": uuid_source,\n",
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" \"target\": uuid_target,\n",
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" \"type\": \"bidirectional\"\n",
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" }\n",
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"}"
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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\": \"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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" \"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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" }]\n",
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"}\n",
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"# Global\n",
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"metadata": {},
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"source": [
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"ontology_builder = lb.OntologyBuilder(\n",
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" tools=[ \n",
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" tools=[\n",
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" lb.Tool(tool=lb.Tool.Type.NER,name=\"ner\"),\n",
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" lb.Tool(tool=lb.Tool.Type.RELATIONSHIP,name=\"relationship\")\n",
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" ], \n",
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" classifications=[ \n",
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" lb.Classification( \n",
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" ],\n",
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" classifications=[\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.TEXT,\n",
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" scope=lb.Classification.Scope.INDEX, \n",
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" name=\"text_convo\"), \n",
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" lb.Classification( \n",
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" class_type=lb.Classification.Type.CHECKLIST, \n",
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" scope=lb.Classification.Scope.INDEX, \n",
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" name=\"checklist_convo\", \n",
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" scope=lb.Classification.Scope.INDEX,\n",
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" name=\"text_convo\"),\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.CHECKLIST,\n",
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" scope=lb.Classification.Scope.INDEX,\n",
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" name=\"checklist_convo\",\n",
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" options=[\n",
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" lb.Option(value=\"first_checklist_answer\"),\n",
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" lb.Option(value=\"second_checklist_answer\") \n",
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" lb.Option(value=\"second_checklist_answer\")\n",
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" ]\n",
444-
" ), \n",
445-
" lb.Classification( \n",
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" class_type=lb.Classification.Type.RADIO, \n",
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" name=\"radio_convo\", \n",
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" scope=lb.Classification.Scope.INDEX, \n",
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" ),\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.RADIO,\n",
382+
" name=\"radio_convo\",\n",
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" scope=lb.Classification.Scope.INDEX,\n",
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" options=[\n",
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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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" 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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" 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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"metadata": {},
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"source": [
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"# Create Labelbox project\n",
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"project = client.create_project(name=\"Conversational Text Annotation Import Demo\", \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",
509-
"# Setup your ontology \n",
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"# Setup your ontology\n",
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"project.setup_editor(ontology) # Connect your ontology and editor to your project"
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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": [
526-
"# Setup Batches and Ontology\n",
527-
"\n",
528461
"# Create a batch to send to your MAL project\n",
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"batch = project.create_batch(\n",
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" \"first-batch-convo-demo\", # Each batch in a project must have a unique name\n",
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570503
" text_annotation,\n",
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" checklist_annotation,\n",
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" radio_annotation,\n",
573-
" ner_source,\n",
574-
" ner_target,\n",
575-
" ner_relationship,\n",
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" nested_radio_annotation,\n",
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" nested_checklist_annotation\n",
578508
" ]\n",
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" text_annotation_ndjson,\n",
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" checklist_annotation_ndjson,\n",
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" radio_annotation_ndjson,\n",
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" ner_source_ndjson,\n",
604-
" 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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"source": [
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"# Upload our label using Model-Assisted Labeling\n",
639566
"upload_job = lb.MALPredictionImport.create_from_objects(\n",
640-
" client = client, \n",
641-
" project_id = project.uid, \n",
642-
" name=f\"mal_job-{str(uuid.uuid4())}\", \n",
567+
" client = client,\n",
568+
" project_id = project.uid,\n",
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" name=f\"mal_job-{str(uuid.uuid4())}\",\n",
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" predictions=label)\n",
644571
"\n",
645572
"upload_job.wait_until_done()\n",
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660587
{
661588
"metadata": {},
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"source": [
663-
"# Upload label for this data row in project \n",
664-
"# Uncomment this code when excluding relationships from label import\n",
665-
"# Relationships are not currently supported for label import\n",
590+
"# Upload label for this data row in project\n",
591+
"upload_job = lb.LabelImport.create_from_objects(\n",
592+
" client = client,\n",
593+
" project_id = project.uid,\n",
594+
" name=\"label_import_job\"+str(uuid.uuid4()),\n",
595+
" labels=label)\n",
666596
"\n",
667-
"\n",
668-
"# upload_job = lb.LabelImport.create_from_objects(\n",
669-
"# client = client, \n",
670-
"# project_id = project.uid, \n",
671-
"# name=\"label_import_job\"+str(uuid.uuid4()), \n",
672-
"# labels=label)\n",
673-
"\n",
674-
"# upload_job.wait_until_done();\n",
675-
"# print(\"Errors:\", upload_job.errors)\n",
676-
"# print(\"Status of uploads: \", upload_job.statuses)"
597+
"upload_job.wait_until_done()\n",
598+
"print(\"Errors:\", upload_job.errors)\n",
599+
"print(\"Status of uploads: \", upload_job.statuses)"
677600
],
678601
"cell_type": "code",
679602
"outputs": [],

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