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459743e
Merge remote-tracking branch 'upstream/main' into Upstage
LEE1026icarus 35912dd
Merge remote-tracking branch 'upstream/main' into eval
LEE1026icarus e976992
[Title] 03-HF-Upload
LEE1026icarus 4edaf7e
Merge remote-tracking branch 'upstream/main' into eval
LEE1026icarus c89fefe
add PDF
LEE1026icarus 89f3b84
[Title] 03-HF-Upload
LEE1026icarus bddc0ac
Merge remote-tracking branch 'upstream/main' into eval
LEE1026icarus 4f6f527
add the csv file and revise the py file.
LEE1026icarus 6646689
Update 16-Evaluations/03-HF-Upload.ipynb
LEE1026icarus 18d9939
Update 16-Evaluations/03-HF-Upload.ipynb
LEE1026icarus 4230e6a
Update 16-Evaluations/03-HF-Upload.ipynb
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,371 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# HF-Upload\n", | ||
| "\n", | ||
| "- Author: [Sun Hyoung Lee](https://github.com/LEE1026icarus)\n", | ||
| "- Design: \n", | ||
| "- Peer Review : \n", | ||
| "- Proofread:\n", | ||
| "- This is a part of [LangChain Open Tutorial](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial)\n", | ||
| "\n", | ||
| "[](https://colab.research.google.com/github/LangChain-OpenTutorial/LangChain-OpenTutorial/blob/main/08-Embeeding/04-UpstageEmbeddings.ipynb) [](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial/blob/main/08-Embeeding/04-UpstageEmbeddings.ipynb)\n", | ||
| "\n", | ||
| "## Overview\n", | ||
| "\n", | ||
| "The process involves loading a local CSV file, converting it to a HuggingFace Dataset format, and uploading it to the Hugging Face Hub as a private dataset. This process allows for easy sharing and access of the dataset through the HuggingFace infrastructure.\n", | ||
| "\n", | ||
| "### Table of Contents\n", | ||
| "\n", | ||
| "- [Overview](#overview)\n", | ||
| "- [Environement Setup](#environment-setup)\n", | ||
| "- [Upload Generated Dataset](#upload-generated-dataset)\n", | ||
| "- [Upload to HuggingFace Dataset](#upload-to-huggingface-dataset)\n", | ||
| "\n", | ||
| "\n", | ||
| "### References\n", | ||
| "- [Huggingface / Share a dataset to the Hub](https://huggingface.co/docs/datasets/upload_dataset)\n", | ||
| "---\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Environment Setup\n", | ||
| "\n", | ||
| "Set up the environment. You may refer to [Environment Setup](https://wikidocs.net/257836) for more details.\n", | ||
| "\n", | ||
| " **[Note]** \n", | ||
| "- `langchain-opentutorial` is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials. \n", | ||
| "- You can checkout the [`langchain-opentutorial`](https://github.com/LangChain-OpenTutorial/langchain-opentutorial-pypi) for more details.\n", | ||
| "\n", | ||
| "### API Key Configuration\n", | ||
| "To use `HuggingFace Dataset` , you need to [obtain a HuggingFace write token](https://huggingface.co/settings/tokens).\n", | ||
| "\n", | ||
| "Once you have your API key, set it as the value for the variable `HUGGINGFACEHUB_API_TOKEN` ." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%%capture --no-stderr\n", | ||
| "%pip install langchain-opentutorial" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Install required packages\n", | ||
| "from langchain_opentutorial import package\n", | ||
| "\n", | ||
| "package.install(\n", | ||
| " [\"datasets\"],\n", | ||
| " verbose=False,\n", | ||
| " upgrade=False,\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "You can set API keys in a `.env` file or set them manually.\n", | ||
| "\n", | ||
| "[Note] If you’re not using the `.env` file, no worries! Just enter the keys directly in the cell below, and you’re good to go.\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from dotenv import load_dotenv\n", | ||
| "from langchain_opentutorial import set_env\n", | ||
| "\n", | ||
| "# Attempt to load environment variables from a .env file; if unsuccessful, set them manually.\n", | ||
| "if not load_dotenv(override=True):\n", | ||
| " set_env(\n", | ||
| " {\n", | ||
| " \"LANGCHAIN_API_KEY\": \"\",\n", | ||
| " \"LANGCHAIN_TRACING_V2\": \"true\",\n", | ||
| " \"LANGCHAIN_ENDPOINT\": \"https://api.smith.langchain.com\",\n", | ||
| " \"LANGCHAIN_PROJECT\": \"\", # set the project name same as the title\n", | ||
| " \"HUGGINGFACEHUB_API_TOKEN\": \"\",\n", | ||
| " }\n", | ||
| " )\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 45, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "True" | ||
| ] | ||
| }, | ||
| "execution_count": 45, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from dotenv import load_dotenv\n", | ||
| "\n", | ||
| "load_dotenv(override=True)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Upload Generated Dataset\n", | ||
| "Import the pandas library for data upload" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 47, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/html": [ | ||
| "<div>\n", | ||
| "<style scoped>\n", | ||
| " .dataframe tbody tr th:only-of-type {\n", | ||
| " vertical-align: middle;\n", | ||
| " }\n", | ||
| "\n", | ||
| " .dataframe tbody tr th {\n", | ||
| " vertical-align: top;\n", | ||
| " }\n", | ||
| "\n", | ||
| " .dataframe thead th {\n", | ||
| " text-align: right;\n", | ||
| " }\n", | ||
| "</style>\n", | ||
| "<table border=\"1\" class=\"dataframe\">\n", | ||
| " <thead>\n", | ||
| " <tr style=\"text-align: right;\">\n", | ||
| " <th></th>\n", | ||
| " <th>user_input</th>\n", | ||
| " <th>reference_contexts</th>\n", | ||
| " <th>reference</th>\n", | ||
| " <th>synthesizer_name</th>\n", | ||
| " </tr>\n", | ||
| " </thead>\n", | ||
| " <tbody>\n", | ||
| " <tr>\n", | ||
| " <th>0</th>\n", | ||
| " <td>Wht is an API?</td>\n", | ||
| " <td>[\"Agents\\nThis combination of reasoning,\\nlogi...</td>\n", | ||
| " <td>An API can be used by a model to make various ...</td>\n", | ||
| " <td>single_hop_specifc_query_synthesizer</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>1</th>\n", | ||
| " <td>What are the three essential components in an ...</td>\n", | ||
| " <td>['Agents\\nWhat is an agent?\\nIn its most funda...</td>\n", | ||
| " <td>The three essential components in an agent's c...</td>\n", | ||
| " <td>single_hop_specifc_query_synthesizer</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>2</th>\n", | ||
| " <td>What Chain-of-Thought do in agent model, how i...</td>\n", | ||
| " <td>['Agents\\nFigure 1. General agent architecture...</td>\n", | ||
| " <td>Chain-of-Thought is a reasoning and logic fram...</td>\n", | ||
| " <td>single_hop_specifc_query_synthesizer</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>3</th>\n", | ||
| " <td>Waht is the DELETE method used for?</td>\n", | ||
| " <td>['Agents\\nThe tools\\nFoundational models, desp...</td>\n", | ||
| " <td>The DELETE method is a common web API method t...</td>\n", | ||
| " <td>single_hop_specifc_query_synthesizer</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>4</th>\n", | ||
| " <td>How do foundational components contribute to t...</td>\n", | ||
| " <td>['<1-hop>\\n\\nAgents\\ncombining specialized age...</td>\n", | ||
| " <td>Foundational components contribute to the cogn...</td>\n", | ||
| " <td>NewMultiHopQuery</td>\n", | ||
| " </tr>\n", | ||
| " </tbody>\n", | ||
| "</table>\n", | ||
| "</div>" | ||
| ], | ||
| "text/plain": [ | ||
| " user_input \\\n", | ||
| "0 Wht is an API? \n", | ||
| "1 What are the three essential components in an ... \n", | ||
| "2 What Chain-of-Thought do in agent model, how i... \n", | ||
| "3 Waht is the DELETE method used for? \n", | ||
| "4 How do foundational components contribute to t... \n", | ||
| "\n", | ||
| " reference_contexts \\\n", | ||
| "0 [\"Agents\\nThis combination of reasoning,\\nlogi... \n", | ||
| "1 ['Agents\\nWhat is an agent?\\nIn its most funda... \n", | ||
| "2 ['Agents\\nFigure 1. General agent architecture... \n", | ||
| "3 ['Agents\\nThe tools\\nFoundational models, desp... \n", | ||
| "4 ['<1-hop>\\n\\nAgents\\ncombining specialized age... \n", | ||
| "\n", | ||
| " reference \\\n", | ||
| "0 An API can be used by a model to make various ... \n", | ||
| "1 The three essential components in an agent's c... \n", | ||
| "2 Chain-of-Thought is a reasoning and logic fram... \n", | ||
| "3 The DELETE method is a common web API method t... \n", | ||
| "4 Foundational components contribute to the cogn... \n", | ||
| "\n", | ||
| " synthesizer_name \n", | ||
| "0 single_hop_specifc_query_synthesizer \n", | ||
| "1 single_hop_specifc_query_synthesizer \n", | ||
| "2 single_hop_specifc_query_synthesizer \n", | ||
| "3 single_hop_specifc_query_synthesizer \n", | ||
| "4 NewMultiHopQuery " | ||
| ] | ||
| }, | ||
| "execution_count": 47, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import pandas as pd\n", | ||
| "\n", | ||
| "df = pd.read_csv(\"data/ragas_synthetic_dataset.csv\")\n", | ||
| "df.head()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Upload to HuggingFace Dataset\n", | ||
| "Convert a Pandas DataFrame to a Hugging Face Dataset and proceed with the upload." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 48, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Dataset({\n", | ||
| " features: ['user_input', 'reference_contexts', 'reference', 'synthesizer_name'],\n", | ||
| " num_rows: 10\n", | ||
| "})\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from datasets import Dataset\n", | ||
| "\n", | ||
| "# Convert pandas DataFrame to Hugging Face Dataset\n", | ||
| "dataset = Dataset.from_pandas(df)\n", | ||
| "\n", | ||
| "# Check the dataset\n", | ||
| "print(dataset)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "application/vnd.jupyter.widget-view+json": { | ||
| "model_id": "902d30cb54d64f1fb7115250c01672d9", | ||
| "version_major": 2, | ||
| "version_minor": 0 | ||
| }, | ||
| "text/plain": [ | ||
| "Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:00<?, ?it/s]" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| }, | ||
| { | ||
| "data": { | ||
| "application/vnd.jupyter.widget-view+json": { | ||
| "model_id": "2c60415789974ce88c9c95535dfefa4a", | ||
| "version_major": 2, | ||
| "version_minor": 0 | ||
| }, | ||
| "text/plain": [ | ||
| "Creating parquet from Arrow format: 0%| | 0/1 [00:00<?, ?ba/s]" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from datasets import Dataset\n", | ||
| "import os\n", | ||
| "\n", | ||
| "# Convert pandas DataFrame to Hugging Face Dataset\n", | ||
| "dataset = Dataset.from_pandas(df)\n", | ||
| "\n", | ||
| "# Set dataset name (change to your desired name)\n", | ||
| "hf_username = \"icarus1026\" # Your Hugging Face Username(ID)\n", | ||
| "dataset_name = f\"{hf_username}/rag-synthetic-dataset\"\n", | ||
| "\n", | ||
| "# Upload dataset\n", | ||
| "dataset.push_to_hub(\n", | ||
| " dataset_name,\n", | ||
| " private=True, # Set private=False for a public dataset\n", | ||
| " split=\"test_v1\", # Enter dataset split name\n", | ||
| " token=os.getenv(\"HUGGINGFACEHUB_API_TOKEN\"),\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "[Note] The Dataset Viewer may take some time to display.\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "langchain-kr-bpXWMSjn-py3.11", | ||
| "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": "3.11.9" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } | ||
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data/ragas_synthetic_dataset.csv파일이 존재하지 않습니다.검토 부탁드립니다.