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| { | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0, | ||
| "metadata": { | ||
| "colab": { | ||
| "provenance": [], | ||
| "authorship_tag": "ABX9TyNAhFQUbk8KKp8mUyRVbN8K", | ||
| "include_colab_link": true | ||
| }, | ||
| "kernelspec": { | ||
| "name": "python3", | ||
| "display_name": "Python 3" | ||
| }, | ||
| "language_info": { | ||
| "name": "python" | ||
| } | ||
| }, | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "id": "view-in-github", | ||
| "colab_type": "text" | ||
| }, | ||
| "source": [ | ||
| "<a href=\"https://colab.research.google.com/github/ieg-dhr/NLP-Course4Humanities_2024/blob/main/Large_Language_Models_Article_Separation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "#Large Language Models and Article Extraction\n", | ||
| "\n", | ||
| "\n", | ||
| "Created by Sarah Oberbichler [](https://orcid.org/0000-0002-1031-2759)\n", | ||
| "\n", | ||
| "###Using LLMs via APIs\n", | ||
| "\n", | ||
| "For this course, we utilize the NVIDIA API, which provides up to 4,000 free credits to access the open-source model llama-3.1-nemotron-70b-instruct via NVIDIA's GPU infrastructure. When using larger models outside of chatbot applications, they demand significant computational resources.\n", | ||
| "While APIs offer a solution for accessing models and GPU power through third parties where no local computer power is available, they typically:\n", | ||
| "\n", | ||
| "* Require payment beyond free trial credits\n", | ||
| "* Should not be used with sensitive data\n", | ||
| "* Should not be used with copyright restricted data\n", | ||
| "\n", | ||
| "\n", | ||
| "### Using LLMs via APIs for the Analysis of Historical Newspapers\n", | ||
| "Historical newspapers published before 1940 are generally free from copyright protection and, when accessed through public newspaper platforms, are not classified as sensitive data. However, important considerations include:\n", | ||
| "\n", | ||
| "* Library licensing agreements may restrict usage\n", | ||
| "* Cultural heritage institutions might have specific terms of use\n", | ||
| "* Access and processing policies may vary by institution\n", | ||
| "\n", | ||
| "When using API's provided by third parties, make sure to check the licensing agreements of the data provider (e.g. library). For example, newspapers makred with **Public Domain Mark 1.0 Universell** don't have any restrictions." | ||
| ], | ||
| "metadata": { | ||
| "id": "LX69IhPEGqhv" | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "#Setting up the Large Language Model\n", | ||
| "\n", | ||
| "In order to use the large language model via API, you need to get an API key: https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-instruct. Add your private key to you Colab Notebook under *Secrets* as NVIDIA_TOKEN. Run the next cell and see if everything worked as intended." | ||
| ], | ||
| "metadata": { | ||
| "id": "vzUjqdnmXvg1" | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "import pandas as pd\n", | ||
| "from openai import OpenAI\n", | ||
| "\n", | ||
| "# Initialize OpenAI client with NVIDIA API settings\n", | ||
| "client = OpenAI(\n", | ||
| " base_url=\"https://integrate.api.nvidia.com/v1\",\n", | ||
| " api_key = userdata.get('NVIDIA_TOKEN')\n", | ||
| ")\n", | ||
| "\n", | ||
| "# Process the DataFrame\n", | ||
| "all_articles = []\n", | ||
| "for index, row in df.iterrows():\n", | ||
| " try:\n", | ||
| " # Make API call\n", | ||
| " completion = client.chat.completions.create(\n", | ||
| " model=\"nvidia/llama-3.1-nemotron-70b-instruct\",\n", | ||
| " messages=[\n", | ||
| " {\n", | ||
| " 'role': 'system',\n", | ||
| " 'content': \"\"\" System Instructions: \"\"\"\n", | ||
| " },\n", | ||
| " {\n", | ||
| " 'role': 'user',\n", | ||
| " 'content': f\"\"\"# Task Instructions:\n", | ||
| "Text to analyze:\n", | ||
| "{row['plainpagefulltext']}\"\"\"\n", | ||
| " }\n", | ||
| " ],\n", | ||
| " temperature=0.0,\n", | ||
| " max_tokens=20000\n", | ||
| " )\n", | ||
| "\n", | ||
| " content = completion.choices[0].message.content\n", | ||
| "\n", | ||
| " # Process articles\n", | ||
| " if content and \"Keine Artikel mit dem angegebenen Thema gefunden.\" not in content:\n", | ||
| " new_row = row.to_dict()\n", | ||
| " new_row['extracted_article'] = content.strip()\n", | ||
| " all_articles.append(new_row)\n", | ||
| "\n", | ||
| " except Exception as e:\n", | ||
| " print(f\"Error processing row {index}: {str(e)}\")\n", | ||
| " continue\n", | ||
| "\n", | ||
| "# Create final DataFrame\n", | ||
| "result_df = pd.DataFrame(all_articles)\n", | ||
| "\n", | ||
| "# Save to Excel\n", | ||
| "result_df.to_excel('test_1.xlsx', index=False)\n", | ||
| "\n", | ||
| "# Display results\n", | ||
| "print(result_df.head())" | ||
| ], | ||
| "metadata": { | ||
| "id": "-6S_983uGveC" | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "#Now try the code with the provided prompts\n", | ||
| "\n", | ||
| "How well did your prompt perform in comparison to this prompt?" | ||
| ], | ||
| "metadata": { | ||
| "id": "DEXdZNstH5ba" | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "import pandas as pd\n", | ||
| "from typing import List, Dict\n", | ||
| "from openai import OpenAI\n", | ||
| "\n", | ||
| "# Initialize OpenAI client with NVIDIA API settings\n", | ||
| "client = OpenAI(\n", | ||
| " base_url=\"https://integrate.api.nvidia.com/v1\",\n", | ||
| " api_key = userdata.get('NVIDIA_TOKEN')\n", | ||
| ")\n", | ||
| "\n", | ||
| "def analyze_dataframe(df: pd.DataFrame, text_column: str) -> pd.DataFrame:\n", | ||
| " def analyze_text(text: str) -> List[Dict[str, str]]:\n", | ||
| " system_prompt = f\"\"\"\n", | ||
| "# System Instructions\n", | ||
| "You are an expert text analyst and information retrieval specialist and hate summarization as well as enumerations. Use {examples} for structuring your answer.\n", | ||
| "Your task is to carefully analyze given texts and extract complete articles that contain specific themes. You never change original texts.\n", | ||
| "\n", | ||
| "Classify as relevant if the text contains:\n", | ||
| "- Primary earthquake terminology from the 19th and 20th century\n", | ||
| "- Official earthquake reports\n", | ||
| "- geology and seismology\n", | ||
| "- Impact descriptions\n", | ||
| "- Solution description\n", | ||
| "- Technical description\n", | ||
| "- Aid\n", | ||
| "- Honorations\n", | ||
| "- Political discussion and opinions on earthquake\n", | ||
| "- Stories from victims and refugees\n", | ||
| "- reportings on refugees and victims\n", | ||
| "- Live of victims\n", | ||
| "- historical references\n", | ||
| "- comparisons\n", | ||
| "\n", | ||
| "Your output should consist of the extracted articles and the verification\n", | ||
| "\n", | ||
| "Maintain a neutral, objective stance throughout the analysis. Focus on accuracy and completeness in your extractions\n", | ||
| "\"\"\"\n", | ||
| " user_prompt = f\"\"\"\n", | ||
| "# Task Instructions\n", | ||
| "Bitte führe die folgenden Schritte aus:\n", | ||
| "1. Lese jeden Text aufmerksam durch. Behandle jeden Text als eigene Einheit, ohne auf andere Texte zu referieren\n", | ||
| "2. Identifiziere alle Artikel zum Thema Erdbeben und Erstoß\n", | ||
| "3. Für jedes Vorkommen des Themas:\n", | ||
| " a. Bestimme den Anfang des Artikels, in dem das Thema vorkommen.\n", | ||
| " b. Kontrolliere Satz für Satz, ob diese zusammengehören, Ende den Artikel, wenn die Sätze nicht mehr zusammengehören.\n", | ||
| " c. Markiere den vollständigen Artikel von Anfang bis Ende.\n", | ||
| " d. Wenn der Artikel zu lang für eine Antwort ist, antworte mit Ja auf \"article too long, human addition needed\":\n", | ||
| " e. Berücksichtige auch sehr kurze und sehr lange Artikel\n", | ||
| "4. Überprüfe jeden markierten Artikel:\n", | ||
| " a. Stelle sicher, dass er eine Einheit bildet, auch wenn es nicht mehr um Erdbeben geht.\n", | ||
| " b. Vergewissere dich, dass er eines der genannten Themen enthält.\n", | ||
| " c. Prüfe, ob der extrahierte Text tatsächlich im Dokument ist\n", | ||
| "5. Extrahiere jeden überprüften Artikel als Originaltext, der nichts als den originalen Text enthält\n", | ||
| "6. Korrigiere OCR-Fehler\n", | ||
| "7. Wenn keine Artikel gefunden wurden, gib \"Keine Artikel mit dem angegebenen Thema gefunden.\" aus.\n", | ||
| "\n", | ||
| "Führe nun diese Schritte für den folgenden Text aus:\n", | ||
| "{text}\n", | ||
| "\"\"\"\n", | ||
| " try:\n", | ||
| " messages = [\n", | ||
| " {\n", | ||
| " 'role': 'system',\n", | ||
| " 'content': system_prompt\n", | ||
| " },\n", | ||
| " {\n", | ||
| " 'role': 'user',\n", | ||
| " 'content': user_prompt\n", | ||
| " }\n", | ||
| " ]\n", | ||
| "\n", | ||
| " completion = client.chat.completions.create(\n", | ||
| " model=\"nvidia/llama-3.1-nemotron-70b-instruct\",\n", | ||
| " messages=messages,\n", | ||
| " temperature=0.0,\n", | ||
| " max_tokens=20000\n", | ||
| " )\n", | ||
| "\n", | ||
| " content = completion.choices[0].message.content\n", | ||
| "\n", | ||
| " # Split the content into individual articles\n", | ||
| " articles = []\n", | ||
| " if \"Keine Artikel mit dem angegebenen Thema gefunden.\" in content:\n", | ||
| " return []\n", | ||
| "\n", | ||
| " # Split by \"**END OF ARTICLE**\" if present, otherwise treat as single article\n", | ||
| " if \"**END OF ARTICLE**\" in content:\n", | ||
| " parts = content.split(\"**END OF ARTICLE**\")\n", | ||
| " articles = [{\"article\": part.strip()} for part in parts if part.strip()]\n", | ||
| " else:\n", | ||
| " articles = [{\"article\": content.strip()}]\n", | ||
| "\n", | ||
| " return articles\n", | ||
| "\n", | ||
| " except Exception as e:\n", | ||
| " print(f\"Error in AI processing: {str(e)}\")\n", | ||
| " return []\n", | ||
| "\n", | ||
| " # Apply the analysis to each row in the DataFrame\n", | ||
| " all_articles = []\n", | ||
| " for index, row in df.iterrows():\n", | ||
| " articles = analyze_text(row[text_column])\n", | ||
| " for i, article in enumerate(articles, 1):\n", | ||
| " new_row = row.to_dict()\n", | ||
| " new_row['extracted_article'] = article['article']\n", | ||
| " new_row['article_part'] = i\n", | ||
| " new_row['total_parts'] = len(articles)\n", | ||
| " all_articles.append(new_row)\n", | ||
| "\n", | ||
| " # Create a new DataFrame with individual rows for each article\n", | ||
| " result_df = pd.DataFrame(all_articles)\n", | ||
| "\n", | ||
| " return result_df\n", | ||
| "\n", | ||
| "# Usage example\n", | ||
| "text_column = 'plainpagefulltext'\n", | ||
| "result_df = analyze_dataframe(df, text_column)\n", | ||
| "\n", | ||
| "# Save the results to an Excel file\n", | ||
| "result_df.to_excel('test_2.xlsx', index=False)\n", | ||
| "\n", | ||
| "# Display the first few rows of the result\n", | ||
| "print(result_df.head())" | ||
| ], | ||
| "metadata": { | ||
| "id": "G6GHbkcUb0hR" | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| } | ||
| ] | ||
| } |