Csebuetnlp Mt5 Multilingual XLSum

🚀 mT5-multilingual-XLSum

This repository hosts the mT5 checkpoint fine - tuned on 45 languages of the XL - Sum dataset. It provides a powerful tool for multilingual abstractive summarization.

🚀 Quick Start

✨ Features

• Supports 45 languages, enabling multilingual abstractive summarization.
• Fine - tuned on the XL - Sum dataset, ensuring high - quality performance.

📦 Installation

The README doesn't provide specific installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

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)

📚 Documentation

The model csebuetnlp/mT5_multilingual_XLSum is designed for summarization tasks. It has been fine - tuned on the XL - Sum dataset. For fine - tuning details and scripts, refer to the [paper](https://aclanthology.org/2021.findings - acl.413/) and the [official repository](https://github.com/csebuetnlp/xl - sum).

🔧 Technical Details

The README doesn't provide specific technical details, so this section is skipped.

📊 Benchmarks

Scores on the XL - Sum test sets are as follows:

📄 License

The model is licensed under cc - by - nc - sa - 4.0.

📖 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",
}