模型:
ctu-aic/mt5-base-multilingual-summarization-multilarge-cs
任务:
文生文数据集:
Multilingual_large_dataset_(multilarge) cnc/dm xsum mlsum cnewsum cnc sumeczech 3Asumeczech 3Acnc 3Acnewsum 3Amlsum 3Axsum 3Acnc/dm 3AMultilingual_large_dataset_(multilarge)语言:
cs其他:
mt5 摘要生成 abstractive summarization mt5-base Czech text2text generation text generation AutoTrain Compatible abstractive+summarization text2text+generation text+generation text-generation-inference许可:
cc-by-sa-4.0这个模型是在以捷克文本为重点的多语种大型摘要数据集上,基于 google/mt5-base 进行微调的检查点,用于生成多语种摘要。
该模型处理了八种不同语言的多句子摘要。通过添加其他外语文档,并且有大量的捷克文档,我们旨在改善捷克语言的摘要。
支持的语言:'cs':'<extra_id_0>', 'en':'<extra_id_1>', 'de':'<extra_id_2>', 'es':'<extra_id_3>', 'fr':'<extra_id_4>', 'ru':'<extra_id_5>', 'tu':'<extra_id_6>', 'zh':'<extra_id_7>'#使用方法
## Configuration of summarization pipeline # def summ_config(): cfg = OrderedDict([ ## summarization model - checkpoint # ctu-aic/m2m100-418M-multilingual-summarization-multilarge-cs # ctu-aic/mt5-base-multilingual-summarization-multilarge-cs # ctu-aic/mbart25-multilingual-summarization-multilarge-cs ("model_name", "ctu-aic/mbart25-multilingual-summarization-multilarge-cs"), ## language of summarization task # language : string : cs, en, de, fr, es, tr, ru, zh ("language", "en"), ## generation method parameters in dictionary # ("inference_cfg", OrderedDict([ ("num_beams", 4), ("top_k", 40), ("top_p", 0.92), ("do_sample", True), ("temperature", 0.95), ("repetition_penalty", 1.23), ("no_repeat_ngram_size", None), ("early_stopping", True), ("max_length", 128), ("min_length", 10), ])), #texts to summarize values = (list of strings, string, dataset) ("texts", [ "english text1 to summarize", "english text2 to summarize", ] ), #OPTIONAL: Target summaries values = (list of strings, string, None) ('golds', [ "target english text1", "target english text2", ]), #('golds', None), ]) return cfg cfg = summ_config() mSummarize = MultiSummarizer(**cfg) summaries,scores = mSummarize(**cfg)
多语种大型摘要数据集由10个子数据集组成,主要基于新闻和每日邮件。训练过程中使用了整个训练集和验证集的72%。
Train set: 3 464 563 docs Validation set: 121 260 docs
Stats | fragment | avg document length | avg summary length | Documents | ||||
---|---|---|---|---|---|---|---|---|
dataset | compression | density | coverage | nsent | nwords | nsent | nwords | count |
cnc | 7.388 | 0.303 | 0.088 | 16.121 | 316.912 | 3.272 | 46.805 | 750K |
sumeczech | 11.769 | 0.471 | 0.115 | 27.857 | 415.711 | 2.765 | 38.644 | 1M |
cnndm | 13.688 | 2.983 | 0.538 | 32.783 | 676.026 | 4.134 | 54.036 | 300K |
xsum | 18.378 | 0.479 | 0.194 | 18.607 | 369.134 | 1.000 | 21.127 | 225K |
mlsum/tu | 8.666 | 5.418 | 0.461 | 14.271 | 214.496 | 1.793 | 25.675 | 274K |
mlsum/de | 24.741 | 8.235 | 0.469 | 32.544 | 539.653 | 1.951 | 23.077 | 243K |
mlsum/fr | 24.388 | 2.688 | 0.424 | 24.533 | 612.080 | 1.320 | 26.93 | 425K |
mlsum/es | 36.185 | 3.705 | 0.510 | 31.914 | 746.927 | 1.142 | 21.671 | 291K |
mlsum/ru | 78.909 | 1.194 | 0.246 | 62.141 | 948.079 | 1.012 | 11.976 | 27K |
cnewsum | 20.183 | 0.000 | 0.000 | 16.834 | 438.271 | 1.109 | 21.926 | 304K |
编码器(输入文本)设定为512个标记,解码器(摘要)设定为128个标记。
基于交叉熵损失进行训练。
Time: 3 days 20 hours Epochs: 1080K steps = 10 (from 10) GPUs: 4x NVIDIA A100-SXM4-40GB eloss: 2.462 - 1.797 tloss: 17.322 - 1.578
ROUGE | ROUGE-1 | ROUGE-2 | ROUGE-L | ||||||
---|---|---|---|---|---|---|---|---|---|
Precision | Recall | Fscore | Precision | Recall | Fscore | Precision | Recall | Fscore | |
cnc | 30.62 | 19.83 | 23.44 | 9.94 | 6.52 | 7.67 | 22.92 | 14.92 | 17.6 |
sumeczech | 27.57 | 17.6 | 20.85 | 8.12 | 5.23 | 6.17 | 20.84 | 13.38 | 15.81 |
cnndm | 43.83 | 37.73 | 39.34 | 20.81 | 17.82 | 18.6 | 31.8 | 27.42 | 28.55 |
xsum | 41.63 | 30.54 | 34.56 | 16.13 | 11.76 | 13.33 | 33.65 | 24.74 | 27.97 |
mlsum-tu- | 54.4 | 43.29 | 46.2 | 38.78 | 31.31 | 33.23 | 48.18 | 38.44 | 41 |
mlsum-de | 47.94 | 44.14 | 45.11 | 36.42 | 35.24 | 35.42 | 44.43 | 41.42 | 42.16 |
mlsum-fr | 35.26 | 25.96 | 28.98 | 16.72 | 12.35 | 13.75 | 28.06 | 20.75 | 23.12 |
mlsum-es | 33.37 | 24.84 | 27.52 | 13.29 | 10.05 | 11.05 | 27.63 | 20.69 | 22.87 |
mlsum-ru | 0.79 | 0.66 | 0.66 | 0.26 | 0.2 | 0.22 | 0.79 | 0.66 | 0.65 |
cnewsum | 24.49 | 24.38 | 23.23 | 6.48 | 6.7 | 6.24 | 24.18 | 24.04 | 22.91 |
soon