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‎examples/annotation_import/pdf_mal.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"id": "dcc32476",
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"id": "7ede57bf",
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"metadata": {},
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"source": [
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"<td>\n",
@@ -12,7 +12,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "43328417",
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"id": "e2c69e42",
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"metadata": {},
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"source": [
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"<td>\n",
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},
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{
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"cell_type": "markdown",
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"id": "6311ec07",
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"id": "cef9c510",
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"metadata": {},
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"source": [
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"# PDF Annotation Import\n",
@@ -38,7 +38,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "e111a6e2",
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"id": "8c877b9c",
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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",
@@ -47,7 +47,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "c6a696c7",
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"id": "e7e5d296",
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"metadata": {},
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"source": [
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"* Notes:\n",
@@ -57,18 +57,16 @@
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "098fd6c8",
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"id": "8d8d554f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# !pip install -q 'labelbox[data]'\n",
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"!pip install -q pip install https://github.com/Labelbox/labelbox-python/archive/refs/heads/jtso/al-3330.zip\n",
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"# !pip install -q -e ../../."
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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": "5f5f479e",
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"id": "573525c5",
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"metadata": {},
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"source": [
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"# Imports"
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "51f77acd",
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"id": "e3522d4b",
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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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"cell_type": "markdown",
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"id": "20834a5f",
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"id": "e3036435",
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"metadata": {},
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"source": [
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"# API Key and Client\n",
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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": "7e706b58",
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"execution_count": 56,
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"id": "6ffec272",
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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 = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VySWQiOiJja2NjOWZtbXc0aGNkMDczOHFpeWM2YW54Iiwib3JnYW5pemF0aW9uSWQiOiJja2N6NmJ1YnVkeWZpMDg1NW8xZHQxZzlzIiwiYXBpS2V5SWQiOiJja2V2cDF2enAwdDg0MDc1N3I2ZWZldGgzIiwiaWF0IjoxNTk5Njc0NzY0LCJleHAiOjIyMzA4MjY3NjR9.iyqPpEWNpfcjcTid5WVkXLi51g22e_l3FrK-DlFJ2mM\"\n",
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"API_KEY = \"YOUR API KEY\"\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": "f3d70bcf",
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"id": "70c4d820",
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"metadata": {},
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"source": [
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"---- \n",
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},
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{
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"cell_type": "markdown",
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"id": "9e88fd12",
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"id": "5d7fc082",
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"metadata": {},
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"source": [
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"### Project setup"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b6b26171",
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"id": "d0bc3c39",
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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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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "00b4884a",
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"id": "f93aebd8",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "e6ce5b06",
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"id": "e290fd6e",
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"metadata": {},
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"outputs": [
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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": "c0bbf502",
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"id": "27961645",
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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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"cell_type": "markdown",
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"id": "eb7bb40c",
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"id": "db26d55d",
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"metadata": {},
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"source": [
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"### Create Label using Annotation Type Objects\n",
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},
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{
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"cell_type": "markdown",
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"id": "1ee26ad9",
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"id": "f409d3cc",
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"metadata": {},
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"source": [
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"### Object Annotations"
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{
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"cell_type": "code",
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"execution_count": 38,
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"id": "a877a9d8",
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"id": "5cbe5b5a",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 39,
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"id": "e0c63922",
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"id": "4896ea42",
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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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"cell_type": "markdown",
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"id": "d5abfce2",
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"id": "0f79ecc9",
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"metadata": {},
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"source": [
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"### Create a Label object with all of our annotations"
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{
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"cell_type": "code",
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"execution_count": 51,
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"id": "616132d3",
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"id": "64ac1f74",
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"id": "ef0c28cc",
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"id": "c7988155",
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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": "markdown",
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"id": "7364844b",
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"id": "b33ef622",
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"metadata": {},
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"source": [
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"To do model-assisted labeling, we need to convert a Label object into an NDJSON. \n",
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{
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"cell_type": "code",
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"execution_count": 52,
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"id": "b77d5a9e",
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"metadata": {},
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"id": "8794d8aa",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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{
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"cell_type": "code",
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"execution_count": 53,
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"id": "ab9b89fd",
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"id": "787524ac",
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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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"cell_type": "code",
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"execution_count": 54,
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"id": "966c5d8c",
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"execution_count": 57,
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"id": "eb5116f9",
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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: [{'uuid': 'fc1913c6-b735-4dea-bd25-c18152a4715f', 'dataRow': {'id': 'cl76eelhd0fm3071n9ropffkr'}, 'status': 'FAILURE', 'errors': [{'name': 'InvalidAnnotation', 'message': 'Document annotation is missing page or unit fields.', 'additionalInfo': None}]}, {'uuid': '9d7b2e57-d68f-4388-867a-af2a9b233719', 'dataRow': {'id': 'cl76eelhd0fm3071n9ropffkr'}, 'status': 'FAILURE', 'errors': [{'name': 'InvalidAnnotation', 'message': 'Document annotation is missing page or unit fields.', 'additionalInfo': None}]}, {'uuid': 'ad60897f-ea1a-47de-b923-459339764921', 'dataRow': {'id': 'cl76eelhd0fm3071n9ropffkr'}, 'status': 'FAILURE', 'errors': [{'name': 'InvalidAnnotation', 'message': 'Document annotation is missing page or unit fields.', 'additionalInfo': None}]}]\n"
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]
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}
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],
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"outputs": [],
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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": 55,
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"id": "dc0f963f",
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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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"{'tools': [{'tool': 'rectangle',\n",
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" 'name': 'box',\n",
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" 'required': False,\n",
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" 'color': '#ff0000',\n",
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" 'classifications': [],\n",
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" 'schemaNodeId': None,\n",
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" 'featureSchemaId': None}],\n",
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" 'classifications': [{'type': 'text',\n",
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" 'instructions': 'text',\n",
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" 'name': 'text',\n",
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" 'required': False,\n",
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" 'options': [],\n",
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" 'schemaNodeId': None,\n",
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" 'featureSchemaId': None,\n",
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" 'scope': 'global'},\n",
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" {'type': 'checklist',\n",
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" 'instructions': 'checklist',\n",
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" 'name': 'checklist',\n",
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" 'required': False,\n",
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" 'options': [{'schemaNodeId': None,\n",
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" 'featureSchemaId': None,\n",
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" 'label': 'first_checklist_answer',\n",
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" 'value': 'first_checklist_answer',\n",
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" 'options': []},\n",
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" {'schemaNodeId': None,\n",
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" 'featureSchemaId': None,\n",
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" 'label': 'second_checklist_answer',\n",
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" 'value': 'second_checklist_answer',\n",
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" 'options': []}],\n",
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" 'schemaNodeId': None,\n",
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" 'featureSchemaId': None,\n",
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" 'scope': 'global'},\n",
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" {'type': 'radio',\n",
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" 'instructions': 'radio',\n",
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" 'name': 'radio',\n",
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" 'required': False,\n",
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" 'options': [{'schemaNodeId': None,\n",
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" 'featureSchemaId': None,\n",
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" 'label': 'first_radio_answer',\n",
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" 'value': 'first_radio_answer',\n",
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" 'options': []},\n",
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" {'schemaNodeId': None,\n",
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" 'featureSchemaId': None,\n",
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" 'label': 'second_radio_answer',\n",
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" 'value': 'second_radio_answer',\n",
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" 'options': []}],\n",
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" 'schemaNodeId': None,\n",
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" 'featureSchemaId': None,\n",
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" 'scope': 'global'}]}"
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]
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},
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"execution_count": 55,
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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.asdict()"
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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": "223e32f9",
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"id": "ba9dc45a",
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"metadata": {},
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"outputs": [],
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"source": []

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