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Update src\llamafactory\train\sft\metric.py #4877

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Update src\llamafactory\train\sft\metric.py #4877

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20 changes: 16 additions & 4 deletions src/llamafactory/train/sft/metric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@
import numpy as np
import torch
from transformers.utils import is_jieba_available, is_nltk_available
import re

from ...extras.constants import IGNORE_INDEX
from ...extras.misc import numpify
Expand Down Expand Up @@ -101,6 +102,9 @@ def __post_init__(self):
self._dump()

def __call__(self, eval_preds: "EvalPrediction", compute_result: bool = True) -> Optional[Dict[str, float]]:
# Check whether Chinese characters exist
is_chinese = lambda x='ddd':sum([1 if u'\u4e00' <= i <= u'\u9fff' else 0 for i in x])>0

preds, labels = numpify(eval_preds.predictions), numpify(eval_preds.label_ids)

preds = np.where(preds != IGNORE_INDEX, preds, self.tokenizer.pad_token_id)
Expand All @@ -110,9 +114,17 @@ def __call__(self, eval_preds: "EvalPrediction", compute_result: bool = 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 is_chinese(label):# Chinese, Remove special characters and space
pred = re.sub('[^\w]','',pred)
label = re.sub('[^\w]','',label)
hypothesis = list(jieba.cut(pred))
reference = list(jieba.cut(label))
else: # English, Remove special characters
pred = re.sub('[^\w ]','',pred)
label = re.sub('[^\w ]','',label)
hypothesis = pred.split()
reference = label.split()

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:
Expand All @@ -123,7 +135,7 @@ def __call__(self, eval_preds: "EvalPrediction", compute_result: bool = True) ->
for k, v in result.items():
self.score_dict[k].append(round(v["f"] * 100, 4))

bleu_score = sentence_bleu([list(label)], list(pred), smoothing_function=SmoothingFunction().method3)
bleu_score = sentence_bleu([reference], hypothesis, smoothing_function=SmoothingFunction().method3)
self.score_dict["bleu-4"].append(round(bleu_score * 100, 4))

if compute_result:
Expand Down