Mt5 M2m Crosssum Enhanced

🚀 mT5-m2m-CrossSum-enhanced

このリポジトリには、CrossSumデータセットのすべてのクロス言語ペアでファインチューニングされた拡張型多対多（m2m）mT5チェックポイントが含まれています。このモデルは、提供されたターゲット言語で任意の言語で書かれたテキストを要約しようとします。ファインチューニングの詳細とスクリプトについては、論文と公式リポジトリを参照してください。

🚀 クイックスタート

💻 使用例

基本的な使用法

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_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)

利用可能なターゲット言語名

• 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

📚 詳細ドキュメント

引用

このモデルを使用する場合は、次の論文を引用してください。

@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}
}

情報テーブル