Opus Mt Tc Big It En

🚀 opus-mt-tc-big-it-en

这是一个用于从意大利语（it）翻译成英语（en）的神经机器翻译模型。

该模型是 [OPUS - MT 项目](https://github.com/Helsinki - NLP/Opus - MT) 的一部分，该项目致力于让神经机器翻译模型广泛可用，惠及世界上多种语言。所有模型最初都使用 [Marian NMT](https://marian - nmt.github.io/) 这一出色的框架进行训练，它是一个用纯 C++ 编写的高效 NMT 实现。这些模型已通过 huggingface 的 transformers 库转换为 PyTorch 格式。训练数据来自 OPUS，训练流程采用 [OPUS - MT - train](https://github.com/Helsinki - NLP/Opus - MT - train) 的程序。

• 相关出版物：[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，请引用）

@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",
}

📚 详细文档

模型信息

模型转换信息

💻 使用示例

基础用法

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) )

# 预期输出:
#     I know who my enemy is.
#     Tom is illiterate; he understands absolutely nothing.

高级用法

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

# 预期输出: I know who my enemy is.

🔍 基准测试

• 测试集翻译结果：[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)
• 测试集得分：[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)
• 基准测试结果：benchmark_results.txt
• 基准测试输出：benchmark_translations.zip

🙏 致谢

这项工作得到了以下项目的支持：

• [欧洲语言网格](https://www.european - language - grid.eu/) 的 [试点项目 2866](https://live.european - language - grid.eu/catalogue/#/resource/projects/2866)。
• [FoTran 项目](https://www.helsinki.fi/en/researchgroups/natural - language - understanding - with - cross - lingual - grounding)，由欧盟的“地平线 2020”研究与创新计划（资助协议编号 771113）下的欧洲研究理事会（ERC）资助。
• MeMAD 项目，由欧盟的“地平线 2020”研究与创新计划（资助协议编号 780069）资助。

我们也感谢 芬兰科学信息技术中心（CSC） 提供的慷慨计算资源和 IT 基础设施。

📄 许可证

本模型使用 CC - BY - 4.0 许可证。