---
tags:
- summarization
datasets:
- csebuetnlp/xlsum
languages:
- 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
multilinguality:
- multilingual
paperswithcode_id: xl-sum
---

# mT5-multilingual-XLSum

This repository contains the mT5 checkpoint finetuned on the 45 languages of [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum) dataset. For finetuning details and scripts,
see the [paper](https://aclanthology.org/2021.findings-acl.413/) and the [official repository](https://github.com/csebuetnlp/xl-sum). 


## Using this model in `transformers` (tested on 4.11.0.dev0)

```python
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.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",
}
```