mT5-m2m-CrossSum-enhanced

该存储库包含一个增强的mT5多对多（m2m）模型，该模型在 CrossSum 数据集的所有跨语言对上进行了微调。该模型试图将任何语言的文本摘要为提供的目标语言。有关微调细节和脚本，请参阅 paper 和 official repository 。

在transformers中使用此模型（已在4.11.0.dev0上进行测试）

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_m2m_crossSum_enhanced"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

get_lang_id = lambda lang: tokenizer._convert_token_to_id(
    model.config.task_specific_params["langid_map"][lang][1]
) 

target_lang = "english" # for a list of available language names see below

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,
    decoder_start_token_id=get_lang_id(target_lang),
    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)

可用的目标语言名称

• amharic
• arabic
• azerbaijani
• bengali
• burmese
• chinese_simplified
• chinese_traditional
• english
• french
• gujarati
• hausa
• hindi
• igbo
• indonesian
• japanese
• kirundi
• korean
• kyrgyz
• marathi
• nepali
• oromo
• pashto
• persian
• pidgin
• portuguese
• punjabi
• russian
• scottish_gaelic
• serbian_cyrillic
• serbian_latin
• sinhala
• somali
• spanish
• swahili
• tamil
• telugu
• thai
• tigrinya
• turkish
• ukrainian
• urdu
• uzbek
• vietnamese
• welsh
• yoruba

引用

如果您使用了此模型，请引用以下论文：

@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}
}