Benchmarking topic models for a paper
Project description
topic-benchmark
Just Benchmarking Topic Models :)
Todo:
- Run benchmark with these models and upload the results:
- all-MiniLM-L6-v2 ⌛
- all-mpnet-base-v2 ⌛
- sentence-transformers/average_word_embeddings_glove.6B.300d ⌛
- intfloat/e5-large-v2 (OR intfloat/multilingual-e5-large-instruct, to my knowledge, they are the same size, but this one performs way better on MTEB)
- Implement pretty printing and formatting to Latex and MD tables for results.
- (Maybe) Implement speed tracking.
Usage:
pip install topic-benchmark
python3 -m topic_benchmark run -e "embedding_model_name"
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
topic_benchmark-0.1.4.tar.gz
(7.5 kB
view hashes)
Built Distribution
Close
Hashes for topic_benchmark-0.1.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 658ba5579a473948de9ea3a440fb526265c74ed6f75ff4af8671299e701963b3 |
|
MD5 | ca54d18726c3edc5350751629af65693 |
|
BLAKE2b-256 | a214f96b9a48483286b36da5478ea4ee42958638191197d74e4d5f7d241b0853 |