Opus Mt Tc Big It En

🚀 opus-mt-tc-big-it-en

這是一個用於從意大利語（it）翻譯成英語（en）的神經機器翻譯模型。

該模型是 [OPUS - MT 項目](https://github.com/Helsinki - NLP/Opus - MT) 的一部分，該項目致力於讓神經機器翻譯模型廣泛可用，惠及世界上多種語言。所有模型最初都使用 [Marian NMT](https://marian - nmt.github.io/) 這一出色的框架進行訓練，它是一個用純 C++ 編寫的高效 NMT 實現。這些模型已通過 huggingface 的 transformers 庫轉換為 PyTorch 格式。訓練數據來自 OPUS，訓練流程採用 [OPUS - MT - train](https://github.com/Helsinki - NLP/Opus - MT - train) 的程序。

• 相關出版物：[OPUS - MT – Building open translation services for the World](https://aclanthology.org/2020.eamt - 1.61/) 和 [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt - 1.139/)（如果使用此模型，請引用）

@inproceedings{tiedemann-thottingal-2020-opus,
    title = "{OPUS}-{MT} {--} Building open translation services for the World",
    author = {Tiedemann, J{\"o}rg  and Thottingal, Santhosh},
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2020.eamt-1.61",
    pages = "479--480",
}

@inproceedings{tiedemann-2020-tatoeba,
    title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.wmt-1.139",
    pages = "1174--1182",
}

📚 詳細文檔

模型信息

模型轉換信息

💻 使用示例

基礎用法

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "So chi è il mio nemico.",
    "Tom è illetterato; non capisce assolutamente nulla."
]

model_name = "pytorch-models/opus-mt-tc-big-it-en"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))

for t in translated:
    print( tokenizer.decode(t, skip_special_tokens=True) )

# 預期輸出:
#     I know who my enemy is.
#     Tom is illiterate; he understands absolutely nothing.

高級用法

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-it-en")
print(pipe("So chi è il mio nemico."))

# 預期輸出: I know who my enemy is.

🔍 基準測試

• 測試集翻譯結果：[opusTCv20210807+bt_transformer - big_2022 - 02 - 25.test.txt](https://object.pouta.csc.fi/Tatoeba - MT - models/ita - eng/opusTCv20210807+bt_transformer - big_2022 - 02 - 25.test.txt)
• 測試集得分：[opusTCv20210807+bt_transformer - big_2022 - 02 - 25.eval.txt](https://object.pouta.csc.fi/Tatoeba - MT - models/ita - eng/opusTCv20210807+bt_transformer - big_2022 - 02 - 25.eval.txt)
• 基準測試結果：benchmark_results.txt
• 基準測試輸出：benchmark_translations.zip

🙏 致謝

這項工作得到了以下項目的支持：

• [歐洲語言網格](https://www.european - language - grid.eu/) 的 [試點項目 2866](https://live.european - language - grid.eu/catalogue/#/resource/projects/2866)。
• [FoTran 項目](https://www.helsinki.fi/en/researchgroups/natural - language - understanding - with - cross - lingual - grounding)，由歐盟的“地平線 2020”研究與創新計劃（資助協議編號 771113）下的歐洲研究理事會（ERC）資助。
• MeMAD 項目，由歐盟的“地平線 2020”研究與創新計劃（資助協議編號 780069）資助。

我們也感謝 芬蘭科學信息技術中心（CSC） 提供的慷慨計算資源和 IT 基礎設施。

📄 許可證

本模型使用 CC - BY - 4.0 許可證。