aopc 0.1.0

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Aopc

The Aopc package provides a framework for evaluating model faithfulness using the Area Over the Perturbation Curve (AOPC) metric. It supports Hugging Face models and datasets, specifically tailored for sequence label classification tasks.

Installation

Install the package via pip:

pip install aopc

Key Features

• Support for Hugging Face models and datasets: Utilize pre-trained models and standard datasets seamlessly.
• AOPC Evaluation: Calculate AOPC metrics for attributions.
• Beam Size Suggestion: Automatically estimate optimal beam sizes for normalized AOPC using our approximation method.

Quick Start

Initialize the Aopc Class

Start by configuring Aopc with a Hugging Face model, such as prajjwal1/bert-tiny:

from aopc import Aopc

aopc = Aopc(model_id="prajjwal1/bert-tiny")

Evaluate Dataset

Load your dataset with Hugging Face's datasets library and evaluate it with Aopc:

Note: If the dataset has not been tokenized Aopc will take care of it.

import datasets

# Load dataset
dset = datasets.load_dataset("stanfordnlp/imdb")

# Evaluate dataset without normalization
new_dset = aopc.evaluate(dset)

Note: Aopc.evaluate() allow either a dictionary, datasets.Dataset or datasets.DatasetDict as input.

Normalized AOPC with Exact Bounds

Estimate

new_dset = aopc.evaluate(dset, normalization="exact")

Normalized AOPC with Approximated Bounds

Calculate the suggested beam size for normalized AOPC approximation:

# Estimate Beam Size
beam_size = aopc.get_suggested_beam_size(dset)

# Approximate normalization
new_dset = aopc.evaluate_dset(dset, normalization="approx", beam_size=beam_size)

License

This project is licensed under the MIT License.