|
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()} |
|
|
