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kkim-labelboxwhistlerKevin KimVal Brodskyvbrodsky
authored
Release 3.44.0 (v2) (#1072)
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]>
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‎.pre-commit-config.yaml

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repos:
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- repo: https://github.com/google/yapf
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rev: v0.31.0
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rev: v0.33.0
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hooks:
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- id: yapf
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name: "yapf"
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args: [-i, --style, google]
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- repo: https://github.com/datarootsio/databooks
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rev: 1.0.1
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hooks:
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- id: databooks-meta
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args: [examples, --overwrite]
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- repo: https://github.com/datarootsio/databooks
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rev: 1.0.1
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hooks:
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- id: databooks-meta
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args: [examples, --overwrite]

‎CHANGELOG.md

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# Changelog
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# Version 3.44.0 (2023-04-26)
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## Added
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* `predictions` param for optionally exporting predictions in model run export v2
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* Limits on `model_run_ids` and `project_ids` on catalog export v2 params
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* `WORKFLOW_ACTION` webhook topic
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* Added `data_row_ids` filter for dataset and project export v2
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## Fixed
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* ISO timestamp parsing for datetime metadata
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* Docstring typo for `client.delete_feature_schema_from_ontology()`
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## Notebooks
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* Removed mention of embeddings metadata fields
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* Fixed broken colab link on `examples/extras/classification-confusion-matrix.ipynb`
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* Added free text classification example to video annotation import notebook
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* Updated prediction_upload notebooks with Annotation Type examples
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# Version 3.43.0 (2023-04-05)
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## Added

‎Dockerfile

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@@ -8,7 +8,8 @@ RUN apt install -y libsm6 \
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libfontconfig1 \
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libxrender1 \
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libgl1-mesa-glx \
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libgeos-dev
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libgeos-dev \
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gcc
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WORKDIR /usr/src/
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COPY requirements.txt /usr/src/

‎docs/source/conf.py

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copyright = '2021, Labelbox'
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author = 'Labelbox'
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release = '3.43.0'
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release = '3.44.0'
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# -- General configuration ---------------------------------------------------
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‎examples/annotation_import/html.ipynb

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{
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"metadata": {},
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"source": [
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"!pip install -q 'labelbox[data]'"
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"!pip install -q \"labelbox[data]\""
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],
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"cell_type": "code",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
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]
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}
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],
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"outputs": [],
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"execution_count": null
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},
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{
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")\n",
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"\n",
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"text_annotation_ndjson = {\n",
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" 'name': 'text_html',\n",
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" 'answer': 'sample text',\n",
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" \"name\": \"text_html\",\n",
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" \"answer\": \"sample text\",\n",
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"}"
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],
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"cell_type": "code",
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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_html\", # must match your ontology feature's name\n",
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" name=\"checklist_html\", # 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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"\n",
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"\n",
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"checklist_annotation_ndjson = {\n",
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" 'name': 'checklist_html',\n",
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" 'answers': [\n",
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" {'name': 'first_checklist_answer'},\n",
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" {'name': 'second_checklist_answer'}\n",
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" \"name\": \"checklist_html\",\n",
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" \"answers\": [\n",
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" {\"name\": \"first_checklist_answer\"},\n",
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" {\"name\": \"second_checklist_answer\"}\n",
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" ],\n",
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"}"
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],
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" name=\"second_radio_answer\")))\n",
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"\n",
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"radio_annotation_ndjson = {\n",
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" 'name': 'radio_html',\n",
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" 'answer': {\n",
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" 'name': 'first_radio_answer'\n",
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" \"name\": \"radio_html\",\n",
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" \"answer\": {\n",
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" \"name\": \"first_radio_answer\"\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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"########## Classification - Radio and Checklist (with subclassifcations) ##########\n",
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"\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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"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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"\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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" 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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"nested_checklist_annotation_ndjson = {\n",
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" \"name\": \"nested_checklist_question\",\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\": {\"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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"}"
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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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" \"global_key\": global_key\n",
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"}\n",
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"\n",
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"dataset = client.create_dataset(name=\"html_annotation_import_demo_dataset\")\n",
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"dataset = client.create_dataset(\n",
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" name=\"html_annotation_import_demo_dataset\", \n",
298+
" iam_integration=None # Removing this argument will default to the organziation's default iam integration\n",
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") \n",
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"task = dataset.create_data_rows([asset])\n",
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"task.wait_till_done()\n",
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"print(\"Errors:\", task.errors)\n",
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"print(\"Failed data rows: \", task.failed_data_rows)"
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],
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"cell_type": "code",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Errors: None\n",
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"Failed data rows: None\n"
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]
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}
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],
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"outputs": [],
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"execution_count": null
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},
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{
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" lb.Option(value=\"first_radio_answer\"),\n",
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" lb.Option(value=\"second_radio_answer\")\n",
288342
" ]\n",
343+
" ),\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.CHECKLIST,\n",
346+
" name=\"nested_checklist_question\",\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",
351+
" 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",
354+
" )\n",
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" ]\n",
356+
" )\n",
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" ]\n",
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" ),\n",
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" lb.Classification(\n",
360+
" class_type=lb.Classification.Type.RADIO,\n",
361+
" name=\"nested_radio_question\", \n",
362+
" options=[\n",
363+
" lb.Option(value=\"first_radio_answer\",\n",
364+
" options=[\n",
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" lb.Classification(\n",
366+
" class_type=lb.Classification.Type.RADIO,\n",
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" name=\"sub_radio_question\",\n",
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" options=[\n",
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" lb.Option(value=\"first_sub_radio_answer\")\n",
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" ]\n",
371+
" ),\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",
312-
"project = client.create_project(name=\"html_project\", \n",
398+
"project = client.create_project(name=\"HTML Import Annotation Demo\", \n",
313399
" media_type=lb.MediaType.Html)\n",
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"\n",
315401
"# Setup your ontology \n",
316402
"project.setup_editor(ontology) # Connect your ontology and editor to your project"
317403
],
318404
"cell_type": "code",
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Default createProject behavior will soon be adjusted to prefer batch projects. Pass in `queue_mode` parameter explicitly to opt-out for the time being.\n"
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]
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}
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],
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"outputs": [],
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"execution_count": null
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},
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{
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349427
"print(\"Batch: \", batch)"
350428
],
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"cell_type": "code",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Batch: <Batch {\n",
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" \"consensus_settings_json\": \"{\\\"numberOfLabels\\\":1,\\\"coveragePercentage\\\":0}\",\n",
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" \"created_at\": \"2023-03-28 18:31:16+00:00\",\n",
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" \"name\": \"first-batch-html-demo\",\n",
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" \"size\": 0,\n",
362-
" \"uid\": \"b9c6dec0-cd96-11ed-a79b-bd72128661a2\",\n",
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" \"updated_at\": \"2023-03-28 18:31:16+00:00\"\n",
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"}>\n"
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]
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}
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],
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"outputs": [],
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"execution_count": null
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},
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{
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397460
" annotations=[\n",
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" text_annotation,\n",
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" checklist_annotation,\n",
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" radio_annotation\n",
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" radio_annotation,\n",
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" nested_checklist_annotation,\n",
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" nested_radio_annotation\n",
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" ]\n",
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" )\n",
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")"
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420485
"label_ndjson = []\n",
421486
"for annotations in [text_annotation_ndjson,\n",
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" checklist_annotation_ndjson,\n",
423-
" radio_annotation_ndjson]:\n",
488+
" radio_annotation_ndjson,\n",
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" nested_radio_annotation_ndjson,\n",
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" nested_checklist_annotation_ndjson\n",
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" ]:\n",
424492
" annotations.update({\n",
425-
" 'dataRow': {\n",
426-
" 'globalKey': global_key\n",
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" \"dataRow\": {\n",
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" \"globalKey\": global_key\n",
427495
" }\n",
428496
" })\n",
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" label_ndjson.append(annotations)"
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462530
"print(\"Status of uploads: \", upload_job.statuses)"
463531
],
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"cell_type": "code",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Errors: []\n",
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"Status of uploads: [{'uuid': 'cd188c7e-167f-48ce-98e6-571f255ba319', 'dataRow': {'id': 'clfslgefk0um4079rfyajcf18', 'globalKey': 'sample_html_1.html'}, 'status': 'SUCCESS'}, {'uuid': '3153214d-831c-4aa4-bf95-280d657cfe51', 'dataRow': {'id': 'clfslgefk0um4079rfyajcf18', 'globalKey': 'sample_html_1.html'}, 'status': 'SUCCESS'}, {'uuid': '297a9f08-30b4-427f-b8f0-d1139709b97d', 'dataRow': {'id': 'clfslgefk0um4079rfyajcf18', 'globalKey': 'sample_html_1.html'}, 'status': 'SUCCESS'}]\n"
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]
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}
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"print(\"Status of uploads: \", upload_job.statuses)"
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],
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"cell_type": "code",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Errors: []\n",
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"Status of uploads: [{'uuid': 'f1c3e6b6-1e7e-41ca-abd5-a0fd919082a6', 'dataRow': {'id': 'clfslgefk0um4079rfyajcf18', 'globalKey': 'sample_html_1.html'}, 'status': 'SUCCESS'}, {'uuid': '44714fd1-e8a8-4da4-aea9-b71392ac78a4', 'dataRow': {'id': 'clfslgefk0um4079rfyajcf18', 'globalKey': 'sample_html_1.html'}, 'status': 'SUCCESS'}, {'uuid': '9d520664-9565-46e7-8b0b-c0615158d004', 'dataRow': {'id': 'clfslgefk0um4079rfyajcf18', 'globalKey': 'sample_html_1.html'}, 'status': 'SUCCESS'}]\n"
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]
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}
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],
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"outputs": [],
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"execution_count": null
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},
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{

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