This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. For finetuning details and scripts,
see the paper and the official repository.
Using this model in transformers (tested on 4.11.0.dev0)
python
1import re
2from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
34WHITESPACE_HANDLER =lambda k: re.sub('\s+',' ', re.sub('\n+',' ', k.strip()))56article_text ="""Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs "spill over into misinformation about vaccines in general". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. "We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO," the post said, referring to the World Health Organization."""78model_name ="csebuetnlp/mT5_multilingual_XLSum"9tokenizer = AutoTokenizer.from_pretrained(model_name)10model = AutoModelForSeq2SeqLM.from_pretrained(model_name)1112input_ids = tokenizer(13[WHITESPACE_HANDLER(article_text)],14 return_tensors="pt",15 padding="max_length",16 truncation=True,17 max_length=51218)["input_ids"]1920output_ids = model.generate(21 input_ids=input_ids,22 max_length=84,23 no_repeat_ngram_size=2,24 num_beams=425)[0]2627summary = tokenizer.decode(28 output_ids,29 skip_special_tokens=True,30 clean_up_tokenization_spaces=False31)3233print(summary)
Benchmarks
Scores on the XL-Sum test sets are as follows:
Language
ROUGE-1 / ROUGE-2 / ROUGE-L
Amharic
20.0485 / 7.4111 / 18.0753
Arabic
34.9107 / 14.7937 / 29.1623
Azerbaijani
21.4227 / 9.5214 / 19.3331
Bengali
29.5653 / 12.1095 / 25.1315
Burmese
15.9626 / 5.1477 / 14.1819
Chinese (Simplified)
39.4071 / 17.7913 / 33.406
Chinese (Traditional)
37.1866 / 17.1432 / 31.6184
English
37.601 / 15.1536 / 29.8817
French
35.3398 / 16.1739 / 28.2041
Gujarati
21.9619 / 7.7417 / 19.86
Hausa
39.4375 / 17.6786 / 31.6667
Hindi
38.5882 / 16.8802 / 32.0132
Igbo
31.6148 / 10.1605 / 24.5309
Indonesian
37.0049 / 17.0181 / 30.7561
Japanese
48.1544 / 23.8482 / 37.3636
Kirundi
31.9907 / 14.3685 / 25.8305
Korean
23.6745 / 11.4478 / 22.3619
Kyrgyz
18.3751 / 7.9608 / 16.5033
Marathi
22.0141 / 9.5439 / 19.9208
Nepali
26.6547 / 10.2479 / 24.2847
Oromo
18.7025 / 6.1694 / 16.1862
Pashto
38.4743 / 15.5475 / 31.9065
Persian
36.9425 / 16.1934 / 30.0701
Pidgin
37.9574 / 15.1234 / 29.872
Portuguese
37.1676 / 15.9022 / 28.5586
Punjabi
30.6973 / 12.2058 / 25.515
Russian
32.2164 / 13.6386 / 26.1689
Scottish Gaelic
29.0231 / 10.9893 / 22.8814
Serbian (Cyrillic)
23.7841 / 7.9816 / 20.1379
Serbian (Latin)
21.6443 / 6.6573 / 18.2336
Sinhala
27.2901 / 13.3815 / 23.4699
Somali
31.5563 / 11.5818 / 24.2232
Spanish
31.5071 / 11.8767 / 24.0746
Swahili
37.6673 / 17.8534 / 30.9146
Tamil
24.3326 / 11.0553 / 22.0741
Telugu
19.8571 / 7.0337 / 17.6101
Thai
37.3951 / 17.275 / 28.8796
Tigrinya
25.321 / 8.0157 / 21.1729
Turkish
32.9304 / 15.5709 / 29.2622
Ukrainian
23.9908 / 10.1431 / 20.9199
Urdu
39.5579 / 18.3733 / 32.8442
Uzbek
16.8281 / 6.3406 / 15.4055
Vietnamese
32.8826 / 16.2247 / 26.0844
Welsh
32.6599 / 11.596 / 26.1164
Yoruba
31.6595 / 11.6599 / 25.0898
Citation
If you use this model, please cite the following paper:
@inproceedings{hasan-etal-2021-xl,
title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Islam, Md. Saiful and
Mubasshir, Kazi and
Li, Yuan-Fang and
Kang, Yong-Bin and
Rahman, M. Sohel and
Shahriyar, Rifat",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.413",
pages = "4693--4703",
}
