ferret

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!")
evaluations = bench.evaluate_explanations(explanations)

print(evaluations)

Features

ferret builds on top of the transformers library. The library supports explanations using:

Gradients
Integrated Gradinets
Gradient x Input word embeddings
SHAP
LIME

and evaluate explanations via:

Faithfulness measures.

AOPC Comprehensiveness
AOPC Sufficiency
Kendall’s tau correlation with leave-one-feature out

Plausibility measures.

AUPRC soft score plausibility
Token f1 hard score plausibility
Token IOU hard score plausibility

TODOs

Possibility to run on select device (“cpu”, “cuda”)
Sample-And-Occlusion explanations
Discretized Integrated Gradients: https://arxiv.org/abs/2108.13654

Visualization

bench = Benchmark(...)

explanations = ...
bench.show_table(explanations)

evaluations = bench.evaluate_explanations(explanations)
bench.show_evaluation_table(evaluations)

Datasets evaluations

bench = Benchmark(...)

hatexdata = bench.load_dataset("hatexplain")
dataset_explanations = bench.generate_dataset_explanations(hatexdata)
dataset_evaluations = bench.evaluate_dataset_explanations(dataset_explanations)
bench.show_dataset_evaluation_table(dataset_evaluations)

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

Logo and graphical assets made by Luca Attanasio.