Opus Mt Tc Big En Tr

🚀 opus-mt-tc-big-en-tr

這是一個用於從英語（en）到土耳其語（tr）翻譯的神經機器翻譯模型。該模型屬於 [OPUS - MT 項目](https://github.com/Helsinki - NLP/Opus - MT) 的一部分，此項目致力於讓神經機器翻譯模型在全球多種語言中廣泛可用且易於獲取。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++ 編寫的高效神經機器翻譯實現。這些模型通過 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) 的方法。

• 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，請引用這些文獻）

@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

使用說明

💻 使用示例

基礎用法

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "I know Tom didn't want to eat that.",
    "On Sundays, we would get up early and go fishing."
]

model_name = "pytorch - models/opus - mt - tc - big - en - tr"
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:
#     Tom'un bunu yemek istemediğini biliyorum.
#     Pazar günleri erkenden kalkıp balık tutmaya giderdik.

高級用法

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki - NLP/opus - mt - tc - big - en - tr")
print(pipe("I know Tom didn't want to eat that."))

# 預期輸出: Tom'un bunu yemek istemediğini biliyorum.

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/eng - tur/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/eng - tur/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)，由歐洲研究理事會（ERC）在歐盟的“地平線 2020”研究與創新計劃（資助協議編號 771113）下資助。
• MeMAD 項目，由歐盟的“地平線 2020”研究與創新計劃資助（資助協議編號 780069）。

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

模型轉換信息

📄 許可證

本模型採用 CC - BY - 4.0 許可證。