Upload csv_to_jsonl.ipynb with huggingface_hub
Browse files- csv_to_jsonl.ipynb +149 -0
csv_to_jsonl.ipynb
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{
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"cells": [
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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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"# Combine .csv files into one .jsonl file"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'combined_data.jsonl'"
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]
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},
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"execution_count": 1,
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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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"from pathlib import Path\n",
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"import pandas as pd\n",
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"import json\n",
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"\n",
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"# Define the path for the uploaded files\n",
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"files = [\n",
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" \"data/ai-tutor-csv-files/activeloop.csv\",\n",
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" \"data/ai-tutor-csv-files/advanced_rag_course.csv\",\n",
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" \"data/ai-tutor-csv-files/filtered_tai_v2.csv\",\n",
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" \"data/ai-tutor-csv-files/hf_transformers.csv\",\n",
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" \"data/ai-tutor-csv-files/langchain_course.csv\",\n",
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" \"data/ai-tutor-csv-files/langchain_docs.csv\",\n",
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" \"data/ai-tutor-csv-files/llm_course.csv\",\n",
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" \"data/ai-tutor-csv-files/openai.csv\",\n",
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" \"data/ai-tutor-csv-files/wiki.csv\"\n",
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"]\n",
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"\n",
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"# Function to load and clean CSV data\n",
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"def load_and_clean_csv(file_path):\n",
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" # Attempt to load the CSV file\n",
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" df = pd.read_csv(file_path)\n",
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" \n",
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" # Check if the first column is unnamed and drop it if so\n",
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" if 'Unnamed: 0' in df.columns or df.columns[0] == '':\n",
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" df = df.drop(df.columns[0], axis=1)\n",
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" \n",
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" # Reorder columns based on expected headers\n",
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" expected_headers = ['title', 'url', 'content', 'source']\n",
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" df = df[expected_headers]\n",
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" \n",
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" return df\n",
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"\n",
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"# Load, clean, and combine all CSV files\n",
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"combined_df = pd.concat([load_and_clean_csv(file) for file in files], ignore_index=True)\n",
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"\n",
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"# Convert to JSON - list of JSON objects\n",
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"combined_json_list = combined_df.to_dict(orient=\"records\")\n",
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"\n",
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"# Save the combined JSON list to a new JSONL file, with each line representing a JSON object\n",
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"output_file_jsonl = \"combined_data.jsonl\"\n",
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"with open(output_file_jsonl, \"w\") as file:\n",
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" for record in combined_json_list:\n",
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" file.write(json.dumps(record) + '\\n')\n",
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"\n",
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"output_file_jsonl"
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]
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},
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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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"# Merge chunks together if they have the same title and url\n",
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"Also prepend the titles into the content for the sources that did not have that."
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# import json\n",
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"\n",
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"# def remove_title_from_content_simple(jsonl_file_path, output_file_path):\n",
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"# with open(jsonl_file_path, 'r') as file, open(output_file_path, 'w') as output_file:\n",
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"# for line in file:\n",
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"# # Parse the JSON line\n",
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"# data = json.loads(line)\n",
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"\n",
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"# content = str(data['content'])\n",
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"# title = str(data['title'])\n",
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" \n",
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"# # Replace the title in the content with an empty string\n",
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"# # This removes the exact match of the title from the content\n",
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"# data['content'] = content.replace(title, '', 1)\n",
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" \n",
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"# # Write the updated data to the output file\n",
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"# json.dump(data, output_file)\n",
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"# output_file.write('\\n') # Add newline to separate JSON objects\n",
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"\n",
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"# # Example usage\n",
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"# jsonl_file_path = 'combined_data.jsonl'\n",
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"# output_file_path = 'output.jsonl'\n",
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"# remove_title_from_content_simple(jsonl_file_path, output_file_path)\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": null,
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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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"cell_type": "code",
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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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "env",
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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": "3.11.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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