A python package for NLP explainability
Project description
A python package for benchmarking interpretability techniques.
Free software: MIT license
Documentation: https://ferret.readthedocs.io.
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from ferret import Benchmark
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased")
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
bench = Benchmark(model, tokenizer)
explanations = bench.explain("You look stunning!")
bench.show_table(explanations)Features
ferret builds on top of the transformers library. The library supports explanations using:
Gradients
Integrated Gradinets
Gradient x Input word embeddings
SHAP
LIME
TODOs
Possibility to run on select device (“cpu”, “cuda”)
Sample-And-Occlusion explanations
Discretized Integrated Gradients: https://arxiv.org/abs/2108.13654
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
Cookiecutter: https://github.com/audreyr/cookiecutter
audreyr/cookiecutter-pypackage: https://github.com/audreyr/cookiecutter-pypackage
History
0.1.0 (2022-05-30)
First release on PyPI.
Release history Release notifications | RSS feed
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