metadata
tags:
- summarization
- mT5
datasets:
- csebuetnlp/xlsum
language:
- am
- ar
- az
- bn
- my
- zh
- en
- fr
- gu
- ha
- hi
- ig
- id
- ja
- rn
- ko
- ky
- mr
- ne
- om
- ps
- fa
- pcm
- pt
- pa
- ru
- gd
- sr
- si
- so
- es
- sw
- ta
- te
- th
- ti
- tr
- uk
- ur
- uz
- vi
- cy
- yo
licenses:
- cc-by-nc-sa-4.0
widget:
- 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.
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)
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
article_text = """Input article text"""
model_name = "csebuetnlp/mT5_multilingual_XLSum"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
input_ids = tokenizer.prepare_seq2seq_batch(
[article_text.replace("\n", " ").strip()],
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 test sets are given below.
| 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",
}