Mt5 M2o Chinese Simplified Crosssum

🚀 mT5-m2o-chinese_simplified-CrossSum

本仓库包含在CrossSum数据集的所有跨语言对上微调的多对一（m2o）mT5检查点，其中目标摘要为简体中文，即该模型尝试以简体中文总结任何语言撰写的文本。有关微调细节和脚本，请参阅论文和官方仓库。

🚀 快速开始

✨ 主要特性

• 支持多种语言：涵盖阿姆哈拉语（am）、阿拉伯语（ar）、阿塞拜疆语（az）、孟加拉语（bn）、缅甸语（my）、中文（zh）、英语（en）等众多语言。
• 多对一总结：能够将任意语言的文本总结为简体中文。
• 开源许可：采用CC BY-NC-SA 4.0许可协议。

📦 安装指南

文档未提及具体安装步骤，可参考transformers库的官方安装说明进行安装。

💻 使用示例

基础用法

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_chinese_simplified_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)

📚 详细文档

有关模型的详细信息，可参考相关论文和官方仓库。

📄 许可证

本项目采用CC BY-NC-SA 4.0许可协议。

📚 引用

如果您使用了该模型，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}
}