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[DOCS-424] removed label typing for annotation/prediction import examples (#1606)
Co-authored-by: ovalle15 <aovalle@Andreas-MacBook-Pro-2.local> Co-authored-by: Gabe <33893811+Gabefire@users.noreply.github.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
1 parent 6ca7fe9 commit aa62651

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-114
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‎examples/annotation_import/audio.ipynb

Lines changed: 1 addition & 3 deletions
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@@ -349,9 +349,7 @@
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"label = []\n",
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"label.append(\n",
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" lb_types.Label(\n",
352-
" data=lb_types.AudioData(\n",
353-
" global_key=global_key\n",
354-
" ),\n",
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" data={\"global_key\" : global_key },\n",
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" annotations=[\n",
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" text_annotation,\n",
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" checklist_annotation,\n",

‎examples/annotation_import/conversational.ipynb

Lines changed: 1 addition & 3 deletions
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@@ -495,9 +495,7 @@
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"label = []\n",
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"label.append(\n",
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" lb_types.Label(\n",
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" data=lb_types.ConversationData(\n",
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" global_key=global_key\n",
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" ),\n",
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" data={\"global_key\" : global_key },\n",
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" annotations=[\n",
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" ner_annotation,\n",
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" text_annotation,\n",

‎examples/annotation_import/conversational_LLM.ipynb

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@@ -542,9 +542,7 @@
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"label = []\n",
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"label.append(\n",
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" lb_types.Label(\n",
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" data=lb_types.ConversationData(\n",
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" global_key=global_key\n",
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" ),\n",
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" data={\"global_key\" : global_key },\n",
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" annotations=[\n",
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" ner_annotation,\n",
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" text_annotation,\n",

‎examples/annotation_import/dicom.ipynb

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -343,7 +343,7 @@
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"annotations_list = polyline_annotation + mask_annotation\n",
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"labels = [\n",
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" lb_types.Label(\n",
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" data=lb_types.DicomData(global_key=global_key),\n",
346+
" data={\"global_key\" : global_key },\n",
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" annotations=annotations_list\n",
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" )\n",
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"]"

‎examples/annotation_import/html.ipynb

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@@ -454,9 +454,7 @@
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"label = []\n",
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"label.append(\n",
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" lb_types.Label(\n",
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" data=lb_types.HTMLData(\n",
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" global_key=global_key\n",
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" ),\n",
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" data={\"global_key\" : global_key },\n",
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" annotations=[\n",
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" text_annotation,\n",
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" checklist_annotation,\n",

‎examples/annotation_import/image.ipynb

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Original file line numberDiff line numberDiff line change
@@ -938,7 +938,7 @@
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"] + cp_mask\n",
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"\n",
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"label.append(\n",
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" lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n",
941+
" lb_types.Label(data={\"global_key\" : global_key },\n",
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" annotations=annotations))"
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],
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"cell_type": "code",

‎examples/annotation_import/import_labeled_dataset_image.ipynb

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@@ -70,7 +70,7 @@
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"metadata": {},
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"source": [
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"# Initialize the Labelbox client\n",
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"API_KEY = \"\" # Place API key\n",
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"API_KEY = \"\"\n",
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"client = lb.Client(API_KEY)"
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],
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"cell_type": "code",
@@ -376,7 +376,7 @@
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" name=class_name,\n",
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" value=Rectangle(start=Point(x=bbox[0], y=bbox[1]), end=Point(x=bbox[2]+bbox[0], y=bbox[3]+bbox[1]))\n",
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" ))\n",
379-
" image_data = ImageData(uid=data_row_dict['id'])\n",
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" image_data = {\"uid\":data_row_dict['id']}\n",
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" labels.append(Label(data=image_data, annotations=annotations_list))"
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],
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"cell_type": "code",

‎examples/annotation_import/pdf.ipynb

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@@ -1024,8 +1024,7 @@
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"\n",
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"labels.append(\n",
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" lb_types.Label(\n",
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" data=lb_types.DocumentData(\n",
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" global_key=global_key),\n",
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" data={\"global_key\" : global_key },\n",
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" annotations = [\n",
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" entities_annotation,\n",
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" checklist_annotation,\n",

‎examples/annotation_import/text.ipynb

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@@ -492,8 +492,7 @@
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"labels = []\n",
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"labels.append(\n",
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" lb_types.Label(\n",
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" data=lb_types.TextData(\n",
496-
" global_key=global_key),\n",
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" data={\"global_key\" : global_key },\n",
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" annotations = [\n",
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" named_entitity_annotation,\n",
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" radio_annotation,\n",

‎examples/annotation_import/tiled.ipynb

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@@ -962,12 +962,12 @@
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"labels =[]\n",
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"labels.append(\n",
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" lb_types.Label(\n",
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" data=lb_types.TiledImageData(\n",
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" global_key=global_key,\n",
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" tile_layer=tile_layer,\n",
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" tile_bounds=bounds,\n",
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" zoom_levels=[12, 20]\n",
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" ),\n",
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" data={\n",
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" \"global_key\": global_key,\n",
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" \"tile_layer\": tile_layer,\n",
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" \"tile_bounds\":bounds,\n",
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" \"zoom_levels\": [12, 20]\n",
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" },\n",
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" annotations = [\n",
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" point_annotation,\n",
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" polyline_annotation,\n",

‎examples/annotation_import/video.ipynb

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@@ -1140,7 +1140,7 @@
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"for annotation in annotations_list:\n",
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" label.append(\n",
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" lb_types.Label(\n",
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" data=lb_types.VideoData(global_key=global_key),\n",
1143+
" data= {\"global_key\": global_key},\n",
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" annotations = annotation\n",
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" )\n",
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" )\n"

‎examples/integrations/huggingface/huggingface.ipynb

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Original file line numberDiff line numberDiff line change
@@ -1,16 +1,18 @@
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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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"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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],
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"cell_type": "markdown"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<td>\n",
@@ -22,37 +24,35 @@
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"<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface.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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"cell_type": "markdown"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Install required libraries"
32-
]
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],
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"cell_type": "markdown"
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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 -q \"labelbox\"\n",
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"!pip install -q transformers"
42-
]
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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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"cell_type": "markdown",
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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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"cell_type": "markdown"
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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\n",
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"import transformers\n",
@@ -63,58 +63,58 @@
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"import requests\n",
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"from tqdm import tqdm\n",
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"import numpy as np"
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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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Labelbox Credentials"
73-
]
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],
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"cell_type": "markdown"
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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 = \"\"\n",
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"client = lb.Client(API_KEY)"
84-
]
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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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Select data rows in Labelbox for custom embeddings"
91-
]
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],
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"cell_type": "markdown"
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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": [
9999
"# Get images from a Labelbox dataset,\n",
100100
"# Ensure the images are available by obtaining a token from your cloud provider if necessary\n",
101101
"DATASET_ID = \"\""
102-
]
102+
],
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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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"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": [
110110
"dataset = client.get_dataset(DATASET_ID)"
111-
]
111+
],
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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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"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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"export_task = dataset.export_v2()\n",
120120
"\n",
@@ -124,58 +124,58 @@
124124
"export_json = export_task.result\n",
125125
"\n",
126126
"data_row_urls = [dr_url['data_row']['row_data'] for dr_url in export_json]"
127-
]
127+
],
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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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"cell_type": "markdown",
131133
"metadata": {},
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"source": [
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"# Get a HuggingFace Model to generate custom embeddings"
134-
]
136+
],
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"cell_type": "markdown"
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},
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{
137-
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
141141
"source": [
142142
"# Get ResNet-50 from HuggingFace\n",
143143
"image_processor = transformers.AutoImageProcessor.from_pretrained(\"microsoft/resnet-50\")\n",
144144
"model = transformers.ResNetModel.from_pretrained(\"microsoft/resnet-50\")"
145-
]
145+
],
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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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Pick an existing custom embedding in Labelbox, or create a custom embedding"
152-
]
154+
],
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"cell_type": "markdown"
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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": [
160160
"# Create a new embedding in your workspace, use the right dimensions to your use case, here we use 2048 for ResNet-50\n",
161161
"new_custom_embedding_id = client.create_embedding(name=\"My new awesome embedding\", dims=2048).id\n",
162162
"\n",
163163
"# Or use an existing embedding from your workspace\n",
164164
"# existing_embedding_id = client.get_embedding_by_name(name=\"ResNet img 2048\").id"
165-
]
165+
],
166+
"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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Generate and upload custom embeddings"
172-
]
174+
],
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"cell_type": "markdown"
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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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"img_emb = []\n",
181181
"\n",
@@ -209,26 +209,22 @@
209209
" \"row_data\": url,\n",
210210
" \"embeddings\": [{\"embedding_id\": new_custom_embedding_id, \"vector\": embedding[0].tolist()}]\n",
211211
" })"
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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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"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": [
220220
"# Upload to a new dataset\n",
221221
"dataset = client.create_dataset(name='image_custom_embedding_resnet', iam_integration=None)\n",
222222
"task = dataset.create_data_rows(data_rows)\n",
223223
"print(task.errors)"
224-
]
225-
}
226-
],
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"metadata": {
228-
"language_info": {
229-
"name": "python"
224+
],
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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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"nbformat": 4,
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"nbformat_minor": 0
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}
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]
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}

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