Benchmarking topic models for a paper
Project description
topic-benchmark
Command Line Interface for benchmarking topic models.
The package contains catalogue
registries for all models, datasets and metrics for model evaluation,
along with scripts for producing tables and figures for the S3 paper.
Usage:
Installation
You can install the package from PyPI.
pip install topic-benchmark
Commands
run
Run the benchmark using a given embedding model. Runs can be resumed if they get obruptly stopped from the results file.
python3 -m topic_benchmark run -e "embedding_model_name"
argument | description | type | default |
---|---|---|---|
--encoder_model (-e) | The encoder model to use for the benchmark. | str |
"all-MiniLM-L6-v2" |
--out_file (-o) | The output path of the benchmark results. By default it will be under results/{encoder_model}.jsonl |
str |
None |
table
Creates a latex table of the results of the benchmark. (Main table in the paper)
python3 -m topic_benchmark table -o results.tex
argument | description | type | default |
---|---|---|---|
results_folder | The folder where all result files are located. | str |
"results/" |
--out_file (-o) | The output path of the benchmark results. By default, results will be printed to stdout. | str |
None |
figures
Creates all figures in the paper as .png
files.
python3 -m topic_benchmark figures
argument | description | type | default |
---|---|---|---|
results_folder | The folder where all result files are located. | str |
"results/" |
--out_file (-o) | Directory where the figures should be placed. | str |
"figures/" |
--show_figures (-s) | Indicates whether the figures should be displayed in a browser tab or not. | bool |
False |
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