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csebuetnlp
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mT5_m2o_arabic_crossSum

Summarization
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PyTorch
mt5
text2text-generation
mT5
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mT5_m2o_arabic_crossSum
File size: 4,692 Bytes 
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---
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
tags:
- summarization
- mT5
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-m2o-arabic-CrossSum

This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the [CrossSum](https://huggingface.co/datasets/csebuetnlp/CrossSum) dataset, where the target summary was in **arabic**, i.e. this model tries to **summarize text written in any language in Arabic.** For finetuning details and scripts, see the [paper](https://arxiv.org/abs/2112.08804) and the [official repository](https://github.com/csebuetnlp/CrossSum). 


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

```python
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)
```




## 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}
}
```