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[X-0] Add predictions to model run example (#1098)
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‎examples/label_export/images.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {},
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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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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"cell_type": "markdown"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<td>\n",
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"<a href=\"https://github.com/Labelbox/labelbox-python/tree/master/examples/label_export/images.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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"cell_type": "markdown"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"How to export data, with examples for each type of export along with details on optional parameters and filters."
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],
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"cell_type": "markdown"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Image Data Export"
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],
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"cell_type": "markdown"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install \"labelbox[data]\" -q"
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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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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import labelbox as lb"
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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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{
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"attachments": {},
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"cell_type": "markdown",
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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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"cell_type": "markdown"
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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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"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 = \"API KEY here\"\n",
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"client = lb.Client(api_key=API_KEY)"
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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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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Export data rows from a project\n",
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"\n",
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"When you export data rows from a project, you can narrow down your data rows by last_activity_at, label_created_at, and data_row_ids. Then, when you export from a project, you may choose to include or exclude certain attributes in your export.\n",
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"\n",
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"You can view the JSON export formats for each data type here: https://docs.labelbox.com/reference/label-export#export-specifications"
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],
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"cell_type": "markdown"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Set the export params to include/exclude certain fields. Make sure each of these fields are correctly grabbed \n",
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"export_params= {\n",
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"\n",
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"export_json = export_task.result\n",
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"print(\"results: \", export_json)"
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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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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Export from a dataset\n",
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"When exporting from Catalog, you can include information about a data row from all projects and model runs to which it belongs. Specifically, for the selected data rows, you can export the labels from multiple projects and/or the predictions from multiple model runs.\n",
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"\n",
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"As shown below, the project_ids and model_run_ids parameters accept a list of IDs"
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],
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"cell_type": "markdown"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Set the export params to include/exclude certain fields. Make sure each of these fields are correctly grabbed \n",
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"export_params= {\n",
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" print(export_task.errors)\n",
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"export_json = export_task.result\n",
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"print(\"results: \", export_json)"
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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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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Export from a slice"
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],
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"cell_type": "markdown"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Set the export params to include/exclude certain fields. Make sure each of these fields are correctly grabbed \n",
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"export_params= {\n",
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" print(export_task.errors)\n",
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"export_json = export_task.result\n",
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"print(\"results: \", export_json)"
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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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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Export data rows from a model run"
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],
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"cell_type": "markdown"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Set the export params to include/exclude certain fields. Make sure each of these fields are correctly grabbed \n",
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"export_params= {\n",
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" \"attachments\": True,\n",
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" \"metadata_fields\": True,\n",
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" \"data_row_details\": True,\n",
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" \"predictions\": True,\n",
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"}\n",
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"\n",
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"model_run = client.get_model_run(\"Enter Model run ID\")\n",
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"print(export_task.errors)\n",
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"export_json = export_task.result\n",
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"print(\"results: \", export_json)"
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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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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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]
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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