Mt5 M2o Arabic Crosssum

🚀 mT5-m2o-arabic-CrossSum

This repository offers a many-to-one (m2o) mT5 checkpoint. It's finetuned on all cross - lingual pairs from the CrossSum dataset. The target summary of this model is in Arabic, meaning it aims to summarize text in any language into Arabic. For detailed finetuning information and scripts, refer to the paper and the official repository.

✨ Features

• Multilingual Summarization: Capable of summarizing text from a wide range of languages, including 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, into Arabic.
• Based on CrossSum Dataset: Leverages the rich cross - lingual data from the CrossSum dataset for finetuning.

📦 Installation

The README doesn't provide 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_m2o_arabic_crossSum"
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)

Advanced Usage

There is no advanced usage example in the original README, so this part is skipped.

📚 Documentation

The README doesn't have detailed documentation, so this section is skipped.

🔧 Technical Details

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

📄 License

📚 Citation

If you use this model, please cite the following paper:

@article{hasan2021crosssum,
  author    = {Tahmid Hasan and Abhik Bhattacharjee and Wasi Uddin Ahmad and Yuan - Fang Li and Yong - bin Kang and Rifat Shahriyar},
  title     = {CrossSum: Beyond English - Centric Cross - Lingual Abstractive Text Summarization for 1500+ Language Pairs},
  journal   = {CoRR},
  volume    = {abs/2112.08804},
  year      = {2021},
  url       = {https://arxiv.org/abs/2112.08804},
  eprinttype = {arXiv},
  eprint    = {2112.08804}
}