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Ontology updates (instructions --> name)
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‎examples/prediction_upload/text_predictions.ipynb‎

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@@ -88,10 +88,25 @@
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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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"outputId": "f82cdc12-a572-4d42-fe74-3f65376f82fb",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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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[32m187.7/187.7 KB\u001b[0m \u001b[31m6.3 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[31m28.1 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",
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"metadata": {
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"id": "NIq-6M9kHKSs"
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},
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{
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"metadata": {
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"id": "z7ZLKLYLHP__"
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{
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"metadata": {
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{
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"metadata": {
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"metadata": {
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"id": "ie2Zni_d6MI8"
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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": "8c427e26-591a-4a90-edba-9d28dd686f3e"
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"outputId": "9f63b64d-c38a-4b41-e4ba-36ef7a59a2ea"
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},
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"execution_count": 173,
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"execution_count": 9,
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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\": \"2023-02-07 20:23:02+00:00\",\n",
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" \"created_at\": \"2023-02-10 14:47:36+00:00\",\n",
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" \"external_id\": null,\n",
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" \"global_key\": \"0816b92a-4c5a-4ac6-9bb7-2401852782f1\",\n",
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" \"global_key\": \"9ed73489-3b36-49c0-884d-eeb6af3ce1a5\",\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\": \"clduovifd2faa07xu9aq3byqt\",\n",
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" \"updated_at\": \"2023-02-07 20:23:02+00:00\"\n",
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" \"uid\": \"cldyn7owq15v907wzbhht7fv7\",\n",
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" \"updated_at\": \"2023-02-10 14:47:36+00:00\"\n",
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"}>\n"
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]
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}
@@ -365,18 +380,18 @@
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" classifications=[ # List of Classification objects\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.RADIO,\n",
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" instructions=\"radio_question\", \n",
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" name=\"radio_question\", \n",
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" options=[lb.Option(value=\"first_radio_answer\")]\n",
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" ),\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.RADIO,\n",
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" instructions=\"nested_radio_question\", \n",
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" name=\"nested_radio_question\", \n",
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" options=[\n",
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" lb.Option(value=\"first_radio_answer\",\n",
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" options=[\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.RADIO,\n",
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" instructions=\"sub_radio_question\",\n",
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" name=\"sub_radio_question\",\n",
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" options=[\n",
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" lb.Option(value=\"first_sub_radio_answer\")\n",
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" ]\n",
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" ),\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.CHECKLIST,\n",
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" instructions=\"checklist_question\", \n",
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" name=\"checklist_question\", \n",
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" options=[\n",
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" lb.Option(value=\"first_checklist_answer\"),\n",
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" lb.Option(value=\"second_checklist_answer\"),\n",
@@ -396,17 +411,17 @@
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" ), \n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.TEXT,\n",
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" instructions=\"free_text\"\n",
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" name=\"free_text\"\n",
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" ),\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.CHECKLIST,\n",
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" instructions=\"nested_checklist_question\",\n",
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" name=\"nested_checklist_question\",\n",
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" options=[\n",
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" lb.Option(\"first_checklist_answer\",\n",
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" options=[\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.CHECKLIST,\n",
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" instructions=\"sub_checklist_question\", \n",
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" name=\"sub_checklist_question\", \n",
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" options=[lb.Option(\"first_sub_checklist_answer\")]\n",
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" )\n",
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" ]\n",
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"metadata": {
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"id": "Kt4XWWqgIiWk"
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},
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"execution_count": 174,
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"outputs": []
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"metadata": {
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"id": "8n-AvzdiOR6d"
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{
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"colab": {
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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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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"outputId": "6708c469-4544-4a00-e866-8fd446944554",
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"outputId": "fde12aaf-9ad1-4071-c3da-537a1867d72c",
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"pycharm": {
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"is_executing": true
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"outputs": [
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"data": {
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"text/plain": [
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"<Batch ID: 42d72d20-a725-11ed-8344-ad4c26b44c8e>"
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"<Batch ID: e5eb4680-a951-11ed-9e69-9fa57a6a78c6>"
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"colab": {
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