csebuetnlp/mT5_multilingual_XLSum

mT5-multilingual-XLSum

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)

import re
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))

article_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."""

model_name = "csebuetnlp/mT5_multilingual_XLSum"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

input_ids = tokenizer(
    [WHITESPACE_HANDLER(article_text)],
    return_tensors="pt",
    padding="max_length",
    truncation=True,
    max_length=512
)["input_ids"]

output_ids = model.generate(
    input_ids=input_ids,
    max_length=84,
    no_repeat_ngram_size=2,
    num_beams=4
)[0]

summary = tokenizer.decode(
    output_ids,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(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",
}