mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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7d4dc25c23
* enable manual bleu&rouge eval by adding `scripts/eval_bleu_rouge.py` * added libraries check * update: 使用datasets库的多进程加速处理 * update: - 使用 fire.Fire - 修改代码格式 * Update eval_bleu_rouge.py: correctly uses fire Deleted the code of using sys.argv * Update eval_bleu_rouge.py --------- Co-authored-by: SnowFox4004 <manba@out> Co-authored-by: hoshi-hiyouga <hiyouga@buaa.edu.cn>
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
import json
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import logging
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import time
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import fire
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from datasets import load_dataset
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try:
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import jieba
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from nltk.translate.bleu_score import SmoothingFunction, sentence_bleu
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from rouge_chinese import Rouge
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jieba.setLogLevel(logging.CRITICAL)
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jieba.initialize()
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except ImportError:
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print("Please install llamafactory with `pip install -e .[metrics]`.")
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raise
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def compute_metrics(sample):
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hypothesis = list(jieba.cut(sample["predict"]))
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reference = list(jieba.cut(sample["label"]))
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bleu_score = sentence_bleu(
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[list(sample["label"])],
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list(sample["predict"]),
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smoothing_function=SmoothingFunction().method3,
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)
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if len(" ".join(hypothesis).split()) == 0 or len(" ".join(reference).split()) == 0:
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result = {"rouge-1": {"f": 0.0}, "rouge-2": {"f": 0.0}, "rouge-l": {"f": 0.0}}
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else:
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rouge = Rouge()
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scores = rouge.get_scores(" ".join(hypothesis), " ".join(reference))
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result = scores[0]
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metric_result = {}
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for k, v in result.items():
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metric_result[k] = round(v["f"] * 100, 4)
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metric_result["bleu-4"] = round(bleu_score * 100, 4)
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return metric_result
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def main(filename: str):
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start_time = time.time()
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dataset = load_dataset("json", data_files=filename, split="train")
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dataset = dataset.map(compute_metrics, num_proc=8, remove_columns=dataset.column_names)
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score_dict = dataset.to_dict()
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average_score = {}
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for task, scores in sorted(score_dict.items(), key=lambda x: x[0]):
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print(f"{task}: {sum(scores) / len(scores):.4f}")
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average_score[task] = sum(scores) / len(scores)
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with open("predictions_score.json", "w", encoding="utf-8") as f:
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json.dump(average_score, f, indent=4)
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print(f"\nDone in {time.time() - start_time:.3f}s.\nScore file saved to predictions_score.json")
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if __name__ == "__main__":
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fire.Fire(main)
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