ferret-xai 0.3.4

Features

ferret offers a painless integration with Hugging Face models and naming conventions. If you are already using the transformers library, you immediately get access to our Explanation and Evaluation API.

Visualization

The Benchmark class exposes easy-to-use table visualization methods (e.g., within Jupyter Notebooks)

bench = Benchmark(model, tokenizer)

# Pretty-print feature attribution scores by all supported explainers
explanations = bench.explain("You look stunning!")
bench.show_table(explanations)

# Pretty-print all the supported evaluation metrics
evaluations = bench.evaluate_explanations(explanations)
bench.show_evaluation_table(evaluations)

Dataset Evaluations

The Benchmark class has a handy method to compute and average our evaluation metrics across multiple samples from a dataset.

import numpy as np
bench = Benchmark(model, tokenizer)

# Compute and average evaluation scores one of the supported dataset
samples = np.arange(20)
hatexdata = bench.load_dataset("hatexplain")
sample_evaluations =  bench.evaluate_samples(hatexdata, samples)

# Pretty-print the results
bench.show_samples_evaluation_table(sample_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.