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Removed broken link , rename annotations
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‎examples/prediction_upload/html_predictions.ipynb‎

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@@ -87,10 +87,25 @@
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"!pip install -q 'labelbox[data]'"
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],
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"metadata": {
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"id": "cm8xMaLbGb7v"
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"id": "cm8xMaLbGb7v",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "65ba50e8-4a5d-45c5-90b7-7f8c2c168c62"
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},
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"execution_count": 49,
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"outputs": []
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m185.5/185.5 KB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Building wheel for pygeotile (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
@@ -112,7 +127,7 @@
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"metadata": {
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"id": "NIq-6M9kHKSs"
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},
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"execution_count": 50,
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"execution_count": 2,
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"outputs": []
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},
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{
@@ -128,13 +143,13 @@
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{
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"cell_type": "code",
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"source": [
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"API_KEY = None\n",
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"API_KEY = None\n",
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"client = Client(API_KEY)"
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],
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"metadata": {
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"id": "z7ZLKLYLHP__"
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},
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"execution_count": 51,
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"execution_count": 3,
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"outputs": []
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{
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"metadata": {
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"id": "FJhCAqeR-cUJ"
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},
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"execution_count": 4,
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"outputs": []
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{
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"metadata": {
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"id": "D_luKHzuY-b3"
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},
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"execution_count": 53,
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"execution_count": 5,
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"outputs": []
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{
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"metadata": {
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"id": "rjHUJUbGZXgY"
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},
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"execution_count": 54,
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"execution_count": 6,
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"outputs": []
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},
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{
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"metadata": {
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"id": "dMpF0LfZZZpg"
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},
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"execution_count": 55,
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"execution_count": 7,
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"outputs": []
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},
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{
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "537e3d11-3809-46fd-af14-4821ae7b89b0"
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"outputId": "e7c7dd14-293e-42e7-d93e-faebdbbab036"
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},
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"execution_count": 56,
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"execution_count": 8,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"<DataRow {\n",
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" \"created_at\": \"2023-01-24 20:57:13+00:00\",\n",
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" \"created_at\": \"2023-01-24 22:36:16+00:00\",\n",
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" \"external_id\": null,\n",
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" \"global_key\": \"eb359c83-89e5-42e3-8fc5-dd2a5f22fb1e\",\n",
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" \"global_key\": \"c23d513c-57f1-48ad-a233-53ff99a795c4\",\n",
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" \"media_attributes\": {},\n",
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" \"metadata\": [],\n",
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" \"metadata_fields\": [],\n",
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" \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_2.html\",\n",
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" \"uid\": \"cldapxjfn0gns07xkfn9p4ct8\",\n",
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" \"updated_at\": \"2023-01-24 20:57:13+00:00\"\n",
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" \"uid\": \"cldatgxbz33qk07y0cknjgnsf\",\n",
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" \"updated_at\": \"2023-01-24 22:36:16+00:00\"\n",
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"}>\n"
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]
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}
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"metadata": {
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"id": "Kt4XWWqgIiWk"
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},
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"execution_count": 57,
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"execution_count": 9,
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"outputs": []
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{
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"metadata": {
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"id": "8n-AvzdiOR6d"
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},
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"outputs": []
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{
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "ebb721f4-b795-4511-bdd6-aac4f92a4e71"
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"outputId": "630f3187-5377-4607-af50-5c8250fd431a"
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},
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"execution_count": 60,
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"execution_count": 11,
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"outputs": [
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"output_type": "execute_result",
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]
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},
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"metadata": {},
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"execution_count": 60
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"execution_count": 11
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}
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]
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},
@@ -474,7 +489,7 @@
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"source": [
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"## Step 5. Create the predictions payload\n",
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"\n",
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"Create the annotations payload using the snippets of [code here](https://https://docs.labelbox.com/reference/import-image-annotations#supported-annotations).\n",
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"Create the annotations payload using the snippets of code in the **Supported Predictions** section.\n",
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"\n",
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"Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
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"\n",
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"metadata": {
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"id": "zv2OLTXKSGWv"
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},
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"execution_count": 61,
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"execution_count": 12,
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"outputs": []
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},
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{
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"metadata": {
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"id": "F-Y7sSyAV3tn"
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},
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"execution_count": 13,
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"outputs": []
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},
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{
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "81984276-a391-4e34-f9cd-3974792ec2bb"
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"outputId": "6b6841ae-cca5-4813-d1ac-1e964ae6c104"
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},
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"execution_count": 63,
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"execution_count": 14,
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"outputs": [
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{
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"output_type": "stream",
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"metadata": {
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"id": "jEtoDiDrPFvI"
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},
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"execution_count": 64,
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"execution_count": 15,
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"outputs": []
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{
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "dad5bd19-32ce-45e1-cfd5-b925e37037f2"
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"outputId": "ff468996-5876-4001-99e8-73b597bf5083"
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},
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"execution_count": 65,
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"execution_count": 16,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<Batch ID: d22f70b0-9c29-11ed-9ce8-292230ae59f9>"
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"<Batch ID: 8e97f0d0-9c37-11ed-8a1b-5b30e16bddf7>"
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]
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},
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"metadata": {},
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"execution_count": 65
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"execution_count": 16
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}
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},
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"###### Annotations ###### \n",
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"\n",
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"\n",
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"radio_prediction_ndjson = {\n",
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"radio_annotation_ndjson = {\n",
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" 'name': 'radio_question',\n",
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" 'answer': {'name': 'first_radio_answer'}\n",
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"}\n",
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"\n",
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"\n",
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"nested_radio_prediction_ndjson = {\n",
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"nested_radio_annotation_ndjson = {\n",
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" 'name': 'radio_question_sub',\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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"\n",
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"nested_checklist_prediction_ndjson = {\n",
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"nested_checklist_annotation_ndjson = {\n",
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" \"name\": \"nested_checklist_question\",\n",
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" \"answer\": [{\n",
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" \"name\": \"first_checklist_answer\", \n",
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" }]\n",
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"\n",
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"checklist_prediction_ndjson = {\n",
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"checklist_annotation_ndjson = {\n",
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" 'name': 'checklist_question',\n",
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" 'answer': [\n",
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" {'name': 'first_checklist_answer', }\n",
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" ]\n",
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"}\n",
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"\n",
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"\n",
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"text_prediction_ndjson = {\n",
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"text_annotation_ndjson = {\n",
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" 'name': 'free_text',\n",
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" 'answer': 'sample text'\n",
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"}\n"
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],
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"metadata": {
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"id": "A8_HVvu9Uvfl"
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"execution_count": 17,
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"outputs": []
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"source": [
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"ndjson_annotation = []\n",
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"for annot in [\n",
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" radio_prediction_ndjson,\n",
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" nested_radio_prediction_ndjson,\n",
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" nested_checklist_prediction_ndjson ,\n",
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" checklist_prediction_ndjson,\n",
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" text_prediction_ndjson\n",
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" radio_annotation_ndjson,\n",
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" nested_radio_annotation_ndjson,\n",
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" nested_checklist_annotation_ndjson ,\n",
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" checklist_annotation_ndjson,\n",
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" text_annotation_ndjson\n",
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" ]:\n",
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" annot.update({\n",
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" 'uuid': str(uuid.uuid4()),\n",
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"metadata": {
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"id": "9gD_alThQA3G"
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"base_uri": "https://localhost:8080/"
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"outputId": "495b5db6-14b4-4901-8000-7d831d85968c"
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"outputId": "f3272425-c1de-4dff-d2fe-d8c238d084bb"
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"outputId": "9234850e-fc29-427e-e75e-d16237b3ef08"
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"outputs": []
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