gec_metrics.metrics.bertscore module

class gec_metrics.metrics.bertscore.BertScore(config: Config = None)

Bases: MetricBaseForSourceFree

class Config(model_type: str = 'bert-base-uncased', num_layers: int = None, batch_size: int = 64, nthreads: int = 4, all_layers: bool = False, idf: bool = False, idf_sents: list[str] = None, lang: str = 'en', rescale_with_baseline: bool = True, baseline_path: str = None, use_fast_tokenizer: bool = False, score_type: str = 'f')

Bases: Config

BERTScore configuration.

• model_type (str): Embedding model.

• num_layers (int): The layer of representation to use.

  If None, the pre-difined one is used. (See bert_score.utils.model2layers.)

• nthreads (int): Number of threads.

• idf (bool): Whether to use idf or not.

• idf_sents (list[str]): Sentences to compute idf weights.

• rescale_with_baselines (bool): Whether to rescale scores.

• baseline_path (str): Path to .tsv file.

  If None, the pre-defined one is used. (See bert_score.rescale_baseline.*.tsv)

• use_fast_tokenizer (bool): Whether to use fast tokenizer.

• score_type (str): “p” (precision) or “r” (recall) or “f” (F1) score.

all_layers: bool = False

baseline_path: str = None

batch_size: int = 64

idf: bool = False

idf_sents: list[str] = None

lang: str = 'en'

model_type: str = 'bert-base-uncased'

nthreads: int = 4

num_layers: int = None

rescale_with_baseline: bool = True

score_type: str = 'f'

use_fast_tokenizer: bool = False

score_sentence(hypotheses: list[str], references: list[list[str]]) → list[float]

Calculate sentence-level scores.

Parameters:

• hypotheses (list[str]) – Corrected sentences. The shape is (num_sentences, )

• references (list[list[str]]) – Reference sentences. The shape is (num_references, num_sentences).

Returns:

The sentence-level scores.

Return type:

list[float]