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new conversational notebook
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‎examples/annotation_import/conversational.ipynb‎

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

‎examples/annotation_import/pdf_mal.ipynb‎ renamed to ‎examples/annotation_import/pdf.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"id": "e3522d4b",
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"metadata": {},
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"outputs": [],

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