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{
"cells": [
{
"cell_type": "markdown",
"id": "40a27d3c-4e5c-4b96-b290-4c49d4fd7219",
"metadata": {},
"source": [
"## HuggingFace Tools\n",
"\n",
"[Huggingface Tools](https://huggingface.co/docs/transformers/v4.29.0/en/custom_tools) supporting text I/O can be\n",
"loaded directly using the `load_huggingface_tool` function."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1055b75-362c-452a-b40d-c9a359706a3a",
"metadata": {},
"outputs": [],
"source": [
"# Requires transformers>=4.29.0 and huggingface_hub>=0.14.1\n",
"!pip install --uprade transformers huggingface_hub > /dev/null"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f964bb45-fba3-4919-b022-70a602ed4354",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"model_download_counter: This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. It takes the name of the category (such as text-classification, depth-estimation, etc), and returns the name of the checkpoint\n"
]
}
],
"source": [
"from langchain.agents import load_huggingface_tool\n",
"\n",
"tool = load_huggingface_tool(\"lysandre/hf-model-downloads\")\n",
"\n",
"print(f\"{tool.name}: {tool.description}\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "641d9d79-95bb-469d-b40a-50f37375de7f",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'facebook/bart-large-mnli'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tool.run(\"text-classification\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88724222-7c10-4aff-8713-751911dc8b63",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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