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https://github.com/chtmp223/topicGPT
Code & Prompts for TopicGPT: A Prompt-Based Framework for Topic Modeling
https://github.com/chtmp223/topicGPT
python topic-modeling
Last synced: about 1 month ago
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Code & Prompts for TopicGPT: A Prompt-Based Framework for Topic Modeling
- Host: GitHub
- URL: https://github.com/chtmp223/topicGPT
- Owner: chtmp223
- Created: 2023-11-02T20:43:16.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-08T14:28:24.000Z (about 1 month ago)
- Last Synced: 2024-11-09T05:13:49.358Z (about 1 month ago)
- Topics: python, topic-modeling
- Language: Python
- Homepage:
- Size: 766 KB
- Stars: 217
- Watchers: 5
- Forks: 35
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- StarryDivineSky - chtmp223/topicGPT
README
# TopicGPT
This repository contains scripts and prompts for our paper ["TopicGPT: Topic Modeling by Prompting Large Language Models"](https://arxiv.org/abs/2311.01449).
## Updates
- [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.
## Setup
- Install the requirements: `pip install -r requirements.txt`
- Set your OpenAI key according to [this article](https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety).
- Refer to https://openai.com/pricing/ for OpenAI API pricing or to https://blog.perplexity.ai/blog/introducing-pplx-api for Perplexity API pricing.
## Data
- Prepare your `.jsonl` data file in the following format:
```
{
"id": "Optional IDs",
"text": "Documents",
"label": "Optional ground-truth labels"
}
```
- Put the data file in `data/input`. There is also a sample data file `data/input/sample.jsonl` to debug the code.
- If you want to sample a subset of the data for topic generation, run `python script/data.py --data --num_samples 1000 --output `. This will sample 1000 documents from the data file and save it to ``. You can also specify `--num_samples` to sample a different number of documents, see the paper for more detail.
- Raw dataset: [[link]](https://drive.google.com/drive/folders/1rCTR5ZQQ7bZQoewFA8eqV6glP6zhY31e?usp=sharing).
## Pipeline
- You can either run `script/run.sh` to run the entire pipeline or run each step individually. See the notebook in `script/example.ipynb` for a step-by-step guide.
- Topic generation: Modify the prompts according to the templates in `templates/generation_1.txt` and `templates/seed_1.md`. Then, to run topic generation, do:
```
python3 script/generation_1.py --deployment_name gpt-4 \
--max_tokens 300 --temperature 0.0 --top_p 0.0 \
--data data/input/sample.jsonl \
--prompt_file prompt/generation_1.txt \
--seed_file prompt/seed_1.md \
--out_file data/output/generation_1.jsonl \
--topic_file data/output/generation_1.md \
--verbose True
```
- Topic refinement: If you want to refine the topics, modify the prompts according to the templates in `templates/refinement.txt`. Then, to run topic refinement, do:
```
python3 refinement.py --deployment_name gpt-4 \
--max_tokens 500 --temperature 0.0 --top_p 0.0 \
--prompt_file prompt/refinement.txt \
--generation_file data/output/generation_1.jsonl \
--topic_file data/output/generation_1.md \
--out_file data/output/refinement.md \
--verbose True \
--updated_file data/output/refinement.jsonl \
--mapping_file data/output/refinement_mapping.txt \
--refined_again False \
--remove False
```
- Topic assignment: Modify the prompts according to the templates in `templates/assignment.txt`. Then, to run topic assignment, do:
```
python3 script/assignment.py --deployment_name gpt-3.5-turbo \
--max_tokens 300 --temperature 0.0 --top_p 0.0 \
--data data/input/sample.jsonl \
--prompt_file prompt/assignment.txt \
--topic_file data/output/generation_1.md \
--out_file data/output/assignment.jsonl \
--verbose True
```
- Topic correction: If the assignment contains errors or hallucinated topics, modify the prompts according to the templates in `templates/correction.txt` (note that this prompt is very similar to the assignment prompt, only adding a `{Message}` field towards the end of the prompt). Then, to run topic correction, do:
```
python3 script/correction.py --deployment_name gpt-3.5-turbo \
--max_tokens 300 --temperature 0.0 --top_p 0.0 \
--data data/output/assignment.jsonl \
--prompt_file prompt/correction.txt \
--topic_file data/output/generation_1.md \
--out_file data/output/assignment_corrected.jsonl \
--verbose True
```
- Second-level topic generation: If you want to generate second-level topics, modify the prompts according to the templates in `templates/generation_2.txt`. Then, to run second-level topic generation, do:
```
python3 script/generation_2.py --deployment_name gpt-4 \
--max_tokens 300 --temperature 0.0 --top_p 0.0 \
--data data/output/generation_1.jsonl \
--seed_file data/output/generation_1.md \
--prompt_file prompt/generation_2.txt \
--out_file data/output/generation_2.jsonl \
--topic_file data/output/generation_2.md \
--verbose True
```
## Cite
```
@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}
}
```