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Oct 14, 2022
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[AL-3764] Conversational Text Example MAL Notebook #722

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388 changes: 388 additions & 0 deletions examples/annotation_import/conversational.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "26b9c486",
"metadata": {},
"source": [
"<td>\n",
" <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
"</td>"
]
},
{
"cell_type": "markdown",
"id": "51eb4b54",
"metadata": {},
"source": [
"<td>\n",
"<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img\n",
"src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
"</td>\n",
"\n",
"<td>\n",
"<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img\n",
"src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
"</td>"
]
},
{
"cell_type": "markdown",
"id": "27d147e7",
"metadata": {},
"source": [
"# Conversational Text Annotation Import\n",
"* This notebook will provide examples of each supported annotation type for conversational text assets. It will cover the following:\n",
" * Model-Assisted Labeling (MAL) - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission."
]
},
{
"cell_type": "markdown",
"id": "19b346e2",
"metadata": {},
"source": [
"* For information on what types of annotations are supported per data type, refer to this documentation:\n",
" * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
]
},
{
"cell_type": "markdown",
"id": "f4375aef",
"metadata": {},
"source": [
"* Notes:\n",
" * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "00ad1e27",
"metadata": {},
"outputs": [],
"source": [
"!pip install -q 'labelbox[data]'"
]
},
{
"cell_type": "markdown",
"id": "ccc4c3c3",
"metadata": {},
"source": [
"# Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f0de1cde",
"metadata": {},
"outputs": [],
"source": [
"from labelbox.schema.ontology import OntologyBuilder, Tool, Classification, Option\n",
"from labelbox import Client, LabelingFrontend, MALPredictionImport\n",
"from labelbox.data.annotation_types import (\n",
" Label, ImageData, ObjectAnnotation, \n",
" TextEntity,\n",
" Radio, Checklist, Text,\n",
" ClassificationAnnotation, ClassificationAnswer\n",
")\n",
"from labelbox.data.serialization import NDJsonConverter\n",
"from labelbox.schema.media_type import MediaType\n",
"import uuid\n",
"import json"
]
},
{
"cell_type": "markdown",
"id": "54a028dd",
"metadata": {},
"source": [
"# API Key and Client\n",
"Provide a valid api key below in order to properly connect to the Labelbox Client."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4aab38e2",
"metadata": {},
"outputs": [],
"source": [
"# Add your api key\n",
"API_KEY = \"YOUR API KEY\"\n",
"API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VySWQiOiJja2NjOWZtbXc0aGNkMDczOHFpeWM2YW54Iiwib3JnYW5pemF0aW9uSWQiOiJja2N6NmJ1YnVkeWZpMDg1NW8xZHQxZzlzIiwiYXBpS2V5SWQiOiJja2V2cDF2enAwdDg0MDc1N3I2ZWZldGgzIiwiaWF0IjoxNTk5Njc0NzY0LCJleHAiOjIyMzA4MjY3NjR9.iyqPpEWNpfcjcTid5WVkXLi51g22e_l3FrK-DlFJ2mM\"\n",
"client = Client(api_key=API_KEY)"
]
},
{
"cell_type": "markdown",
"id": "c1763e44",
"metadata": {},
"source": [
"---- \n",
"### Steps\n",
"1. Make sure project is setup\n",
"2. Collect annotations\n",
"3. Upload"
]
},
{
"cell_type": "markdown",
"id": "d30024a7",
"metadata": {},
"source": [
"First, we create an ontology with all the possible tools and classifications supported for PDF. The official list of supported annotations to import can be found here:\n",
"- [Model-Assisted Labeling](https://docs.labelbox.com/docs/model-assisted-labeling) (annotations/labels are not submitted)\n",
"- [Conversational Text Annotations](https://docs.labelbox.com/docs/conversational-annotations)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ae6f0919",
"metadata": {},
"outputs": [],
"source": [
"ontology_builder = OntologyBuilder(\n",
" tools=[ \n",
" Tool( # NER tool given the name \"ner\"\n",
" tool=Tool.Type.NER, \n",
" name=\"ner\")], \n",
" classifications=[ \n",
" Classification( # Text classification given the name \"text\"\n",
" class_type=Classification.Type.TEXT,\n",
" scope=Classification.Scope.INDEX, \n",
" instructions=\"text\"), \n",
" Classification( # Checklist classification given the name \"text\" with two options: \"first_checklist_answer\" and \"second_checklist_answer\"\n",
" class_type=Classification.Type.CHECKLIST, \n",
" scope=Classification.Scope.INDEX, \n",
" instructions=\"checklist\", \n",
" options=[\n",
" Option(value=\"first_checklist_answer\"),\n",
" Option(value=\"second_checklist_answer\") \n",
" ]\n",
" ), \n",
" Classification( # Radio classification given the name \"text\" with two options: \"first_radio_answer\" and \"second_radio_answer\"\n",
" class_type=Classification.Type.RADIO, \n",
" instructions=\"radio\", \n",
" scope=Classification.Scope.INDEX, \n",
" options=[\n",
" Option(value=\"first_radio_answer\"),\n",
" Option(value=\"second_radio_answer\")\n",
" ]\n",
" )\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b95935a7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"OntologyBuilder(tools=[Tool(tool=<Type.NER: 'named-entity'>, name='ner', required=False, color=None, classifications=[], schema_id=None, feature_schema_id=None)], classifications=[Classification(class_type=<Type.TEXT: 'text'>, instructions='text', required=False, options=[], schema_id=None, feature_schema_id=None, scope=<Scope.INDEX: 'index'>), Classification(class_type=<Type.CHECKLIST: 'checklist'>, instructions='checklist', required=False, options=[Option(value='first_checklist_answer', label='first_checklist_answer', schema_id=None, feature_schema_id=None, options=[]), Option(value='second_checklist_answer', label='second_checklist_answer', schema_id=None, feature_schema_id=None, options=[])], schema_id=None, feature_schema_id=None, scope=<Scope.INDEX: 'index'>), Classification(class_type=<Type.RADIO: 'radio'>, instructions='radio', required=False, options=[Option(value='first_radio_answer', label='first_radio_answer', schema_id=None, feature_schema_id=None, options=[]), Option(value='second_radio_answer', label='second_radio_answer', schema_id=None, feature_schema_id=None, options=[])], schema_id=None, feature_schema_id=None, scope=<Scope.INDEX: 'index'>)])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ontology_builder"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6b6403a1",
"metadata": {},
"outputs": [],
"source": [
"# Create Labelbox project\n",
"mal_project = client.create_project(name=\"conversational_mal_project\", media_type=MediaType.Document)\n",
"\n",
"# Create one Labelbox dataset\n",
"dataset = client.create_dataset(name=\"conversational_annotation_import_demo_dataset\")\n",
"\n",
"# Grab an example asset and create a Labelbox data row\n",
"data_row = dataset.create_data_row(\n",
" external_id = \"conversation-1\",\n",
" row_data = \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\"\n",
")\n",
"\n",
"# Setup your ontology / labeling editor\n",
"editor = next(client.get_labeling_frontends(where=LabelingFrontend.name == \"Editor\")) # Unless using a custom editor, do not modify this\n",
"\n",
"mal_project.setup(editor, ontology_builder.asdict()) # Connect your ontology and editor to your MAL project\n",
"mal_project.datasets.connect(dataset) # Connect your dataset to your MAL project"
]
},
{
"cell_type": "markdown",
"id": "f4d3694e",
"metadata": {},
"source": [
"### Object Annotations"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "551ca09a",
"metadata": {},
"outputs": [],
"source": [
"# message based ner\n",
"ner_annotation = { \n",
" \"uuid\": str(uuid.uuid4()),\n",
" \"name\": \"ner\",\n",
" \"dataRow\": {\"id\": data_row.uid},\n",
" \"location\": { \n",
" \"start\": 0, \n",
" \"end\": 8 \n",
" },\n",
" \"messageId\": \"4\"\n",
" }"
]
},
{
"cell_type": "markdown",
"id": "1deaf1f1",
"metadata": {},
"source": [
"### Classification Annotations"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "9c5d93de",
"metadata": {},
"outputs": [],
"source": [
"# message based classifications\n",
"text_annotation = {\n",
" 'name': 'text',\n",
" 'answer': 'the answer to the text questions right here',\n",
" 'uuid': str(uuid.uuid4()),\n",
" \"dataRow\": {\"id\": data_row.uid},\n",
" \"messageId\": \"0\",\n",
"}\n",
"checklist_annotation = {\n",
" 'name': 'checklist',\n",
" 'uuid': str(uuid.uuid4()),\n",
" 'answers': [\n",
" {'name': 'first_checklist_answer'},\n",
" {'name': 'second_checklist_answer'},\n",
" ],\n",
" \"dataRow\": {\"id\": data_row.uid},\n",
" \"messageId\": \"2\",\n",
"}\n",
"\n",
"radio_annotation = {\n",
" 'name': 'radio',\n",
" 'uuid': str(uuid.uuid4()), \n",
" \"dataRow\": {\"id\": data_row.uid},\n",
" 'answer': {\n",
" 'name': 'first_radio_answer'\n",
" },\n",
" \"messageId\": \"0\",\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "762db1d2",
"metadata": {},
"outputs": [],
"source": [
"annotations = [\n",
" ner_annotation,\n",
" text_annotation,\n",
" checklist_annotation,\n",
" radio_annotation\n",
"]"
]
},
{
"cell_type": "markdown",
"id": "55be64cf",
"metadata": {},
"source": [
"### Model Assisted Labeling "
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "10a1f924",
"metadata": {},
"outputs": [],
"source": [
"# Upload our label using Model-Assisted Labeling\n",
"upload_job = MALPredictionImport.create_from_objects(\n",
" client = client, \n",
" project_id = mal_project.uid, \n",
" name=f\"mal_job-{str(uuid.uuid4())}\", \n",
" predictions=annotations)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "b17f6ba9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Errors: []\n"
]
}
],
"source": [
"# Errors will appear for each annotation that failed.\n",
"# Empty list means that there were no errors\n",
"# This will provide information only after the upload_job is complete, so we do not need to worry about having to rerun\n",
"print(\"Errors:\", upload_job.errors)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7ee6bc98",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
2 changes: 1 addition & 1 deletion examples/annotation_import/pdf_mal.ipynb → examples/annotation_import/pdf.ipynb
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},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"id": "e3522d4b",
"metadata": {},
"outputs": [],
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