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Merge pull request #378 from johnnygreco/johnny/remove-ea-notices
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‎nemo/NeMo-Data-Designer/managed-service-tutorials/getting-started/1-the-basics.ipynb‎

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"source": [
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"# 🎨 NeMo Data Designer 101: The Basics\n",
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"\n",
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"> ⚠️ **Warning**: NeMo Data Designer is current in Early Release and is not recommended for production use.\n",
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"\n",
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"<br>\n",
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"\n",
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"In this notebook, we will demonstrate the basics of Data Designer by generating a simple product review dataset."
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"In this notebook, we will demonstrate the basics of Data Designer by generating a simple product review dataset.\n"
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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": 1,
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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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" SubcategorySamplerParams,\n",
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" UniformSamplerParams,\n",
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" ModelConfig,\n",
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" InferenceParameters\n",
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" InferenceParameters,\n",
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")\n"
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]
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},
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"- In this notebook, we connect to the [managed service of data designer](https://build.nvidia.com/nemo/data-designer). Alternatively, you can connect to your own instance of data designer by following the deployment instructions [here](https://docs.nvidia.com/nemo/microservices/latest/set-up/deploy-as-microservices/data-designer/docker-compose.html).\n",
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"- If you have an instance of data designer running locally, you can connect to it as follows\n",
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"\n",
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" ```python\n",
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" data_designer_client = DataDesignerClient(client=NeMoMicroservices(base_url=\"http://localhost:8080\"))\n",
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" ```\n"
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" ```python\n",
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" data_designer_client = DataDesignerClient(client=NeMoMicroservices(base_url=\"http://localhost:8080\"))\n",
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" ```\n"
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]
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},
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{
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"source": [
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"data_designer_client = NeMoDataDesignerClient(\n",
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" base_url=\"https://ai.api.nvidia.com/v1/nemo/dd\",\n",
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" default_headers={\"Authorization\": f\"Bearer {api_key}\"} # auto-generated API KEY\n",
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" default_headers={\"Authorization\": f\"Bearer {api_key}\"}, # auto-generated API KEY\n",
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")\n"
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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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"**Note**: \n",
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"**Note**:\n",
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"The NeMo Data Designer Managed service has models available for you to use as well. You can use these models by referencing the appropriate model_alias for them.\n",
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"\n",
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"Please visit https://build.nvidia.com/nemo/data-designer to see the full list of models and their model aliases."
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"Please visit https://build.nvidia.com/nemo/data-designer to see the full list of models and their model aliases.\n"
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]
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},
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{
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" max_tokens=1024,\n",
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" temperature=0.6,\n",
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" top_p=0.95,\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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")\n"

‎nemo/NeMo-Data-Designer/managed-service-tutorials/getting-started/2-structured-outputs-and-jinja-expressions.ipynb‎

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"source": [
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"# 🎨 NeMo Data Designer 101: Structured Outputs and Jinja Expressions\n",
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"\n",
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"> ⚠️ **Warning**: NeMo Data Designer is current in Early Release and is not recommended for production use.\n",
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">\n",
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"> **Note**: In order to run this notebook, you must have the NeMo Data Designer microservice deployed locally via docker compose. See the [deployment guide](http://docs.nvidia.com/nemo/microservices/latest/set-up/deploy-as-microservices/data-designer/docker-compose.html) for more details.\n",
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"\n",
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"<br>\n",
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"\n",
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"In this notebook, we will continue our exploration of Data Designer, demonstrating more advanced data generation using structured outputs and Jinja expressions.\n",
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"\n",
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"If this is your first time using Data Designer, we recommend starting with the [first notebook](./1-the-basics.ipynb) in this 101 series."
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"If this is your first time using Data Designer, we recommend starting with the [first notebook](./1-the-basics.ipynb) in this 101 series.\n"
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]
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},
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{
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"- In this notebook, we connect to the [managed service of data designer](https://build.nvidia.com/nemo/data-designer). Alternatively, you can connect to your own instance of data designer by following the deployment instructions [here](https://docs.nvidia.com/nemo/microservices/latest/set-up/deploy-as-microservices/data-designer/docker-compose.html).\n",
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"- If you have an instance of data designer running locally, you can connect to it as follows\n",
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"\n",
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" ```python\n",
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" data_designer_client = DataDesignerClient(client=NeMoMicroservices(base_url=\"http://localhost:8080\"))\n",
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" ```\n"
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" ```python\n",
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" data_designer_client = DataDesignerClient(client=NeMoMicroservices(base_url=\"http://localhost:8080\"))\n",
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" ```\n"
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"data_designer_client = NeMoDataDesignerClient(\n",
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" base_url=\"https://ai.api.nvidia.com/v1/nemo/dd\",\n",
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" default_headers={\"Authorization\": f\"Bearer {api_key}\"} # auto-generated API KEY\n",
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" default_headers={\"Authorization\": f\"Bearer {api_key}\"}, # auto-generated API KEY\n",
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"cell_type": "markdown",
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"metadata": {},
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"**Note**: \n",
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"**Note**:\n",
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"The NeMo Data Designer Managed service has models available for you to use as well. You can use these models by referencing the appropriate model_alias for them.\n",
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"\n",
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"Please visit https://build.nvidia.com/nemo/data-designer to see the full list of models and their model aliases."
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"Please visit https://build.nvidia.com/nemo/data-designer to see the full list of models and their model aliases.\n"
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{
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" sampler_type=SamplerType.CATEGORY,\n",
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" params=CategorySamplerParams(\n",
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" values=[\"rambling\", \"brief\", \"detailed\", \"structured with bullet points\"],\n",
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" weights=[1, 2, 2, 1]\n",
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" weights=[1, 2, 2, 1],\n",
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" ),\n",
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" conditional_params={\n",
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" \"target_age_range == '18-25'\": CategorySamplerParams(values=[\"rambling\"]),\n",
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" }\n",
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" },\n",
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"- [Seeding synthetic data generation with an external dataset](./3-seeding-with-a-dataset.ipynb)\n",
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"- [Using Custom Model Configs](./4-custom-model-configs.ipynb)\n",
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"- [Using Custom Model Configs](./4-custom-model-configs.ipynb)\n"
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}
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‎nemo/NeMo-Data-Designer/managed-service-tutorials/getting-started/3-seeding-with-a-dataset.ipynb‎

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"# 🎨 NeMo Data Designer 101: Seeding Synthetic Data Generation with an External Dataset\n",
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"\n",
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"> ⚠️ **Warning**: NeMo Data Designer is current in Early Release and is not recommended for production use.\n",
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">\n",
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"> **Note**: In order to run this notebook, you must have the NeMo Data Designer microservice deployed locally via docker compose. See the [deployment guide](http://docs.nvidia.com/nemo/microservices/latest/set-up/deploy-as-microservices/data-designer/docker-compose.html) for more details.\n",
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"\n",
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"<br>\n",
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"\n",
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"In this notebook, we will demonstrate how to seed synthetic data generation in Data Designer with an external dataset.\n",
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"\n",
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"If this is your first time using Data Designer, we recommend starting with the [first notebook](./1-the-basics.ipynb) in this 101 series."
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"If this is your first time using Data Designer, we recommend starting with the [first notebook](./1-the-basics.ipynb) in this 101 series.\n"
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]
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},
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{
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"- In this notebook, we connect to the [managed service of data designer](https://build.nvidia.com/nemo/data-designer). Alternatively, you can connect to your own instance of data designer by following the deployment instructions [here](https://docs.nvidia.com/nemo/microservices/latest/set-up/deploy-as-microservices/data-designer/docker-compose.html).\n",
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"- If you have an instance of data designer running locally, you can connect to it as follows\n",
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"\n",
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" ```python\n",
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" data_designer_client = DataDesignerClient(client=NeMoMicroservices(base_url=\"http://localhost:8080\"))\n",
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" ```\n"
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" ```python\n",
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" data_designer_client = DataDesignerClient(client=NeMoMicroservices(base_url=\"http://localhost:8080\"))\n",
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" ```\n"
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]
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{
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"data_designer_client = NeMoDataDesignerClient(\n",
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" base_url=\"https://ai.api.nvidia.com/v1/nemo/dd\",\n",
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" default_headers={\"Authorization\": f\"Bearer {api_key}\"} # auto-generated API KEY\n",
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" default_headers={\"Authorization\": f\"Bearer {api_key}\"}, # auto-generated API KEY\n",
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")\n"
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"**Note**: \n",
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"**Note**:\n",
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"The NeMo Data Designer Managed service has models available for you to use as well. You can use these models by referencing the appropriate model_alias for them.\n",
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"\n",
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"Please visit https://build.nvidia.com/nemo/data-designer to see the full list of models and their model aliases."
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"Please visit https://build.nvidia.com/nemo/data-designer to see the full list of models and their model aliases.\n"
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]
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"- In this dataset, the `input_text` represents the `patient_summary` and the `output_text` represents the `diagnosis`\n",
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"**Note**: At this time, we only support using a single file as the seed. If you have multiple files you would like to use as seeds, it is recommended you consolidated these into a single file. \n"
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"**Note**: At this time, we only support using a single file as the seed. If you have multiple files you would like to use as seeds, it is recommended you consolidated these into a single file.\n"
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" dataset=\"gretelai/symptom_to_diagnosis/train.jsonl\",\n",
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" datastore_settings={\"endpoint\": \"https://huggingface.co\"}\n",
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" datastore_settings={\"endpoint\": \"https://huggingface.co\"},\n",
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" ),\n",
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" sampling_strategy=\"shuffle\",\n",
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")\n"

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