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Final updates
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‎examples/annotation_import/text.ipynb‎

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@@ -86,33 +86,19 @@
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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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"execution_count": 36,
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"id": "4d63074b-2379-48af-b9d6-2a66190f03c4",
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"metadata": {
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"id": "4d63074b-2379-48af-b9d6-2a66190f03c4",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "0b4547c9-e534-441d-9bf6-7588ee50c8a2"
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"id": "4d63074b-2379-48af-b9d6-2a66190f03c4"
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},
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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[K |████████████████████████████████| 184 kB 5.2 MB/s \n",
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"\u001b[K |████████████████████████████████| 7.8 MB 45.4 MB/s \n",
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"\u001b[?25h 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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"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": "code",
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"execution_count": null,
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"execution_count": 37,
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"id": "01fca8c9-0680-4a9c-a11e-1b49f31e9121",
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"metadata": {
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"id": "01fca8c9-0680-4a9c-a11e-1b49f31e9121"
@@ -124,7 +110,7 @@
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"from labelbox.schema.queue_mode import QueueMode\n",
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"from labelbox.data.annotation_types import (\n",
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" Label, TextData, Checklist, Radio, ObjectAnnotation, TextEntity,\n",
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" ClassificationAnnotation, ClassificationAnswer, LabelList, Text \n",
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" ClassificationAnnotation, ClassificationAnswer, LabelList, Text, ImageData\n",
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")\n",
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"from labelbox.data.serialization import NDJsonConverter\n",
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"import uuid\n",
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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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"execution_count": 38,
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"id": "86003724-4807-4281-95c1-5284a6f9609f",
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"metadata": {
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"id": "86003724-4807-4281-95c1-5284a6f9609f"
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"id": "Z5ORHD1pZKt1"
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},
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"id": "Z5ORHD1pZKt1",
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"execution_count": null,
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"execution_count": 39,
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"outputs": []
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},
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{
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"id": "mrPJ1Ko0ZMdl"
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},
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"id": "mrPJ1Ko0ZMdl",
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"execution_count": null,
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"execution_count": 40,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"########## Nested Classifications are only supported with NDJSON tools ##########\n",
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"########## Classification - Radio (with subclassifcations) is only suppported with NDJSON tools ##########\n",
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"\n",
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"# NDJSON\n",
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"radio_annotation_ndjson_with_subclass = {\n",
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"id": "vt7QvfV7ZPAF"
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},
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"id": "vt7QvfV7ZPAF",
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"execution_count": null,
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"execution_count": 41,
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"outputs": []
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},
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{
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"id": "45BDh768ZYsH"
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},
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"id": "45BDh768ZYsH",
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"execution_count": null,
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"execution_count": 42,
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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": null,
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"execution_count": 43,
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"id": "f9f9287c-aad7-4914-bc87-1453fb8bce81",
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"metadata": {
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"id": "f9f9287c-aad7-4914-bc87-1453fb8bce81"
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"base_uri": "https://localhost:8080/"
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},
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"id": "Ak-jnABK3OPH",
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"outputId": "ff481cce-af08-4748-e8ac-a959085e07de"
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"outputId": "0f80ba52-adde-4d97-d04a-fcdbe4d1a414"
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},
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"id": "Ak-jnABK3OPH",
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"execution_count": null,
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"execution_count": 44,
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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\": \"2022-12-22 20:59:28+00:00\",\n",
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" \"created_at\": \"2023-01-30 18:06:23+00:00\",\n",
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" \"external_id\": null,\n",
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" \"global_key\": \"TEST-ID-190217471490724126308456269288159181446d\",\n",
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" \"global_key\": \"TEST-ID-321668750509639104503407808903937477958d\",\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-sample-datasets/nlp/lorem-ipsum.txt\",\n",
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" \"uid\": \"clbzkhbxy18ws07ysdumbbgnq\",\n",
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" \"updated_at\": \"2022-12-22 20:59:28+00:00\"\n",
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" \"uid\": \"cldj4gyf60fy207xh3z1y2g1h\",\n",
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" \"updated_at\": \"2023-01-30 18:06:23+00:00\"\n",
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"}>\n",
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"clbzkhbxy18ws07ysdumbbgnq\n"
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"cldj4gyf60fy207xh3z1y2g1h\n"
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]
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}
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]
@@ -382,7 +368,7 @@
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"### Step 2: Create/select an ontology\n",
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"Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name`/`instructions` fields in your annotations to ensure the correct feature schemas are matched.\n",
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"\n",
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"For example, when we create the checklist annotation [above](https://colab.research.google.com/drive/1mXfgydXxVR2OMAjJo1nj2EU9QQyVhW90#scrollTo=45BDh768ZYsH&line=1&uniqifier=1), we provided the `name` as `checklist_question`. Now, when we setup our ontology, we must ensure that the name of my classification tool is also `checklist_question`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
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"For example, when we create the checklist annotation above, we provided the `name` as `checklist_question`. Now, when we setup our ontology, we must ensure that the name of my classification tool is also `checklist_question`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
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"\n",
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"[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
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],
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" ]\n",
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")\n",
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"\n",
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"ontology = client.create_ontology(\"Ontology Text Annotations\", ontology_builder.asdict(), media_type=MediaType.Text)\n"
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"ontology = client.create_ontology(\"Ontology Text Annotations\", ontology_builder.asdict())\n"
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],
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"metadata": {
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"id": "_wWxYFoDTsar"
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},
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"id": "_wWxYFoDTsar",
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"execution_count": null,
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"execution_count": 45,
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"outputs": []
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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": 46,
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"id": "044e9194-d21d-403e-b64c-047c1063b0fe",
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"metadata": {
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"id": "044e9194-d21d-403e-b64c-047c1063b0fe"
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "153857ae-455d-4c7c-fb36-40fc16dc0819"
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"outputId": "6eb66aad-477d-490e-ea91-53de995f1f65"
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},
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"id": "DbwheC8RzI-0",
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"execution_count": null,
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"execution_count": 47,
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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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"Batch: <Batch {\n",
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" \"consensus_settings_json\": \"{\\\"numberOfLabels\\\":1,\\\"coveragePercentage\\\":0}\",\n",
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" \"created_at\": \"2022-12-22 20:59:51+00:00\",\n",
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" \"created_at\": \"2023-01-30 18:06:28+00:00\",\n",
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" \"name\": \"first-batch-text-demo\",\n",
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" \"size\": 1,\n",
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" \"uid\": \"94297660-823b-11ed-8039-c1b641c04d88\",\n",
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" \"updated_at\": \"2022-12-22 20:59:51+00:00\"\n",
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" \"uid\": \"d14be9f0-a0c8-11ed-a7e5-f9da6146996d\",\n",
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" \"updated_at\": \"2023-01-30 18:06:28+00:00\"\n",
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"}>\n"
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]
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}
@@ -562,7 +548,7 @@
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{
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"cell_type": "markdown",
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"source": [
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"#### Python Annotations"
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"#### Python annotations"
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],
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"metadata": {
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"id": "7IgSl5SxcO4R"
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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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"execution_count": 48,
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"id": "53aaf87b-114f-4b56-a417-8c7cddc1f532",
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"metadata": {
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"id": "53aaf87b-114f-4b56-a417-8c7cddc1f532"
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"source": [
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"# Create a Label\n",
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"label = Label(\n",
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" data=TextData(\n",
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" data=ImageData(\n",
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" uid=data_row.uid),\n",
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" annotations = [\n",
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" named_entitity_annotation, \n",
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" radio_annotation, \n",
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" checklist_annotation, \n",
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" text_annotation \n"
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" text_annotation\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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"# Create urls to mask data for upload\n",
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"def signing_function(obj_bytes: bytes) -> str:\n",
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" url = client.upload_data(content=obj_bytes, sign=True)\n",
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" return url \n",
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"\n",
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"label.add_url_to_masks(signing_function)\n",
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"\n",
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"# Convert our label from a Labelbox class object to the underlying NDJSON format required for upload \n",
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"label_ndjson = list(NDJsonConverter.serialize([label]))"
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]
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},
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{
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"id": "c66672c7-33e8-4d5d-b79d-2ac76d830341",
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"metadata": {
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"id": "c66672c7-33e8-4d5d-b79d-2ac76d830341"
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"base_uri": "https://localhost:8080/"
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},
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"id": "izli4zhEfi75",
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"outputId": "b1cdc2d0-9541-4df2-d922-be0a7ce80499"
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"outputId": "4eb1b790-4e60-4cc6-9224-de6a9ed339bf"
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"id": "e8d4e99b-ad7e-48b9-8073-afb764d7c5b4",
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"metadata": {
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"id": "e8d4e99b-ad7e-48b9-8073-afb764d7c5b4",
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"colab": {
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"base_uri": "https://localhost:8080/"
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"outputId": "be9df775-f18c-44ed-e02e-82b3c2a3691f"
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"outputId": "1e52b23b-cefc-439b-f374-2e8b886280f4"
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"outputs": [
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{
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"id": "ubbraAmo2_fe"
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"execution_count": 52,
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"outputs": []
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