fastopic 0.0.3

FASTopic

FASTopic

FASTopic is a fast, adaptive, stable, and transferable topic modeling package. It leverages pretrained Transformers to produce document embeddings, and discovers latent topics through the optimal transport between document, topic, and word embeddings. This brings about a neat and efficient topic modeling paradigm, different from traditional probabilistic, VAE-based, and clustering-based models.

Installation

Install FASTopic with pip:

pip install fastopic

Otherwise, install FASTopic from the source:

git clone https://github.com/bobxwu/FASTopic.git
cd FASTopic && python setup.py install

Quick Start

Discover topics from 20newsgroups.

from fastopic import FASTopic
from sklearn.datasets import fetch_20newsgroups
from topmost.preprocessing import Preprocessing

docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']

preprocessing = Preprocessing(vocab_size=10000, stopwords='English')

model = FASTopic(num_topics=50, preprocessing)
topic_top_words, doc_topic_dist = model.fit_transform(docs)

topic_top_words is a list of the top words in discovered topics. doc_topic_dist is the topic distributions of documents (doc-topic distributions), a numpy array with shape $N \times K$ (number of documents $N$ and number of topics $K$).

Usage

1. Try FASTopic on your dataset

from fastopic import FASTopic
from topmost.preprocessing import Preprocessing

# Prepare your dataset.
your_dataset = [
    'doc 1',
    'doc 2', # ...
]

# Preprocess the dataset. This step tokenizes docs, removes stopwords, and sets max vocabulary size, etc..
# Pass your tokenizer as:
#   preprocessing = Preprocessing(vocab_size=your_vocab_size, tokenizer=your_tokenizer, stopwords=your_stopwords_set)
preprocessing = Preprocessing(stopwords='English')

model = FASTopic(num_topics=50, preprocessing)
topic_top_words, doc_topic_dist = model.fit_transform(docs)

2. Topic activity over time

After training, we can compute the activity of each topic at each time slice.

topic_activity = model.topic_activity_over_time(time_slices)

Citation

If you want to use our package, please cite as

@article{wu2024fastopic,
    title={FASTopic: A Fast, Adaptive, Stable, and Transferable Topic Modeling Paradigm},
    author={Wu, Xiaobao and Nguyen, Thong and Zhang, Delvin Ce and Wang, William Yang and Luu, Anh Tuan},
    journal={arXiv preprint arXiv:2405.17978},
    year={2024}
}