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from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, Sequence, Tuple, Union
import numpy as np
from ...extras.constants import IGNORE_INDEX
from ...extras.packages import is_jieba_available, is_nltk_available, is_rouge_available
if TYPE_CHECKING:
from transformers.tokenization_utils import PreTrainedTokenizer
if is_jieba_available():
import jieba
if is_nltk_available():
from nltk.translate.bleu_score import SmoothingFunction, sentence_bleu
if is_rouge_available():
from rouge_chinese import Rouge
@dataclass
class ComputeMetrics:
r"""
Wraps the tokenizer into metric functions, used in Seq2SeqPeftTrainer.
"""
tokenizer: "PreTrainedTokenizer"
def __call__(self, eval_preds: Sequence[Union[np.ndarray, Tuple[np.ndarray]]]) -> Dict[str, float]:
r"""
Uses the model predictions to compute metrics.
"""
preds, labels = eval_preds
score_dict = {"rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []}
preds = np.where(preds != IGNORE_INDEX, preds, self.tokenizer.pad_token_id)
labels = np.where(labels != IGNORE_INDEX, labels, self.tokenizer.pad_token_id)
decoded_preds = self.tokenizer.batch_decode(preds, skip_special_tokens=True)
decoded_labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True)
for pred, label in zip(decoded_preds, decoded_labels):
hypothesis = list(jieba.cut(pred))
reference = list(jieba.cut(label))
if len(" ".join(hypothesis).split()) == 0 or len(" ".join(reference).split()) == 0:
result = {"rouge-1": {"f": 0.0}, "rouge-2": {"f": 0.0}, "rouge-l": {"f": 0.0}}
else:
rouge = Rouge()
scores = rouge.get_scores(" ".join(hypothesis), " ".join(reference))
result = scores[0]
for k, v in result.items():
score_dict[k].append(round(v["f"] * 100, 4))
bleu_score = sentence_bleu([list(label)], list(pred), smoothing_function=SmoothingFunction().method3)
score_dict["bleu-4"].append(round(bleu_score * 100, 4))
return {k: float(np.mean(v)) for k, v in score_dict.items()}
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