metrics/bart_score.py

import os
import numpy as np
from BARTScore.bart_score import BARTScorer


def get_scorers():
    assert os.path.isfile(
        os.path.join("BARTScore", "bart.pth")
    ), "You must download `bart.pth` to use BARTScore.\nUse `gdown --id 1_7JfF7KOInb7ZrxKHIigTMR4ChVET01m --output bart.pth`"

    scorers = {}

    scorers["vanilla"] = BARTScorer(device="cuda:0", checkpoint="facebook/bart-large")

    scorers["cnn"] = BARTScorer(device="cuda:0", checkpoint="facebook/bart-large-cnn")

    # for the parabank model, first init a bart model, then load the local para model from BARTScore/bart.pth
    # see the documentation from https://github.com/neulab/BARTScore for reference
    scorers["para"] = BARTScorer(device="cuda:0", checkpoint="facebook/bart-large-cnn")
    scorers["para"].load(path="BARTScore/bart.pth")

    return scorers


def compute_bart_score_for_scorer(predictions, references, scorer_name, scorer):
    precisions = np.array(scorer.score(references, predictions, batch_size=4))
    recalls = np.array(scorer.score(predictions, references, batch_size=4))
    f_scores = 0.5 * (precisions + recalls)

    return [
        {
            f"{scorer_name}_f_score": f_scores[i],
            f"{scorer_name}_precision": precisions[i],
            f"{scorer_name}_recall": recalls[i],
        }
        for i in range(len(predictions))
    ]


def compute_bart_score(predictions, references, scorers):
    result = [{} for _ in range(len(predictions))]
    for scorer_name, scorer in scorers.items():
        scorer_result = compute_bart_score_for_scorer(predictions, references, scorer_name, scorer)
        for i, element in enumerate(scorer_result):
            result[i].update(element)
    return result