topicgpt-python 0.2.7

Official implementation of TopicGPT: A Prompt-based Topic Modeling Framework (NAACL'24)

TopicGPT

This repository contains scripts and prompts for our paper "TopicGPT: Topic Modeling by Prompting Large Language Models" (NAACL'24). Our topicgpt_python package consists of five main functions:

• generate_topic_lvl1 generates high-level and generalizable topics.
• generate_topic_lvl2 generates low-level and specific topics to each high-level topic.
• refine_topics refines the generated topics by merging similar topics and removing irrelevant topics.
• assign_topics assigns the generated topics to the input text, along with a quote that supports the assignment.
• correct_topics corrects the generated topics by reprompting the model so that the final topic assignment is grounded in the topic list.

📣 Updates

• [11/09/24] Python package topicgpt_python is released! You can install it via pip install topicgpt_python. We support OpenAI API, VertexAI, Azure API, Gemini API, and vLLM (requires GPUs for inference). See PyPI.
• [11/18/23] Second-level topic generation code and refinement code are uploaded.
• [11/11/23] Basic pipeline is uploaded. Refinement and second-level topic generation code are coming soon.

📦 Using TopicGPT

Getting Started

1. Make a new Python 3.9+ environment using virtualenv or conda.
2. Install the required packages:

   pip install topicgpt_python
   

• Set your API key:

  # Run in shell
  # Needed only for the OpenAI API deployment
  export OPENAI_API_KEY={your_openai_api_key}
  
  # Needed only for the Vertex AI deployment
  export VERTEX_PROJECT={your_vertex_project}   # e.g. my-project
  export VERTEX_LOCATION={your_vertex_location} # e.g. us-central1
  
  # Needed only for Gemini deployment
  export GEMINI_API_KEY={your_gemini_api_key}
  
  # Needed only for the Azure API deployment
  export AZURE_OPENAI_API_KEY={your_azure_api_key}
  export AZURE_OPENAI_ENDPOINT={your_azure_endpoint}
  

• Refer to https://openai.com/pricing/ for OpenAI API pricing or to https://cloud.google.com/vertex-ai/pricing for Vertex API pricing.

Data

• Prepare your .jsonl data file in the following format:

  {
      "id": "IDs (optional)",
      "text": "Documents",
      "label": "Ground-truth labels (optional)"
  }
  

• Put your data file in data/input. There is also a sample data file data/input/sample.jsonl to debug the code.
• Raw dataset used in the paper (Bills and Wiki): [link].

Pipeline

Check out demo.ipynb for a complete pipeline and more detailed instructions. We advise you to try running on a subset with cheaper (or open-source) models first before scaling up to the entire dataset.

0. (Optional) Define I/O paths in config.yml and load using:

   import yaml
   
   with open("config.yml", "r") as f:
       config = yaml.safe_load(f)
   

1. Load the package:

   from topicgpt_python import *
   

2. Generate high-level topics:

   generate_topic_lvl1(api, model, data, prompt_file, seed_file, out_file, topic_file, verbose)
   

3. Generate low-level topics (optional)

   generate_topic_lvl2(api, model, seed_file, data, prompt_file, out_file, topic_file, verbose)
   

4. Refine the generated topics by merging near duplicates and removing topics with low frequency (optional):

   refine_topics(api, model, prompt_file, generation_file, topic_file, out_file, updated_file, verbose, remove, mapping_file)
   

5. Assign and correct the topics, usually with a weaker model if using paid APIs to save cost:

   assign_topics(
   api, model, data, prompt_file, out_file, topic_file, verbose
   )
   

   correct_topics(
       api, model, data_path, prompt_path, topic_path, output_path, verbose
   ) 
   

6. Check out the data/output folder for sample outputs.

7. We also offer metric calculation functions in topicgpt_python.metrics to evaluate the alignment between the generated topics and the ground-truth labels (Adjusted Rand Index, Harmonic Purity, and Normalized Mutual Information).

📜 Citation

@misc{pham2023topicgpt,
      title={TopicGPT: A Prompt-based Topic Modeling Framework}, 
      author={Chau Minh Pham and Alexander Hoyle and Simeng Sun and Mohit Iyyer},
      year={2023},
      eprint={2311.01449},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}