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