opus-mt-tc-big-zls-en

用于从南斯拉夫语言（zls）翻译为英语（en）的神经机器翻译模型。

此模型是 OPUS-MT project 的一部分，旨在使神经机器翻译模型在世界上许多语言中广泛可用和易于访问。所有模型最初使用纯C++编写的令人惊叹的 Marian NMT 框架进行训练。使用 transformers library by huggingface 将模型转换为pyTorch。训练数据来自 OPUS ，并且训练流程使用 OPUS-MT-train 的程序。

• 发表物： OPUS-MT – Building open translation services for the World 和 The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT （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",
}

模型信息

• 发布日期：2022-03-17
• 源语言：bos_Latn bul hbs hrv mkd slv srp_Cyrl srp_Latn
• 目标语言：eng
• 模型：transformer-big
• 数据：opusTCv20210807+bt（ source ）
• 分词：SentencePiece（spm32k，spm32k）
• 原始模型： opusTCv20210807+bt_transformer-big_2022-03-17.zip
• 更多信息发布模型： OPUS-MT zls-eng README

用法

简短的示例代码：

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Да не би случайно Том да остави Мери да кара колата?",
    "Какво е времето днес?"
]

model_name = "pytorch-models/opus-mt-tc-big-zls-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) )

# expected output:
#     Did Tom just let Mary drive the car?
#     What's the weather like today?

您还可以使用transformers pipelines来使用OPUS-MT模型，例如：

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zls-en")
print(pipe("Да не би случайно Том да остави Мери да кара колата?"))

# expected output: Did Tom just let Mary drive the car?

基准

• 测试集翻译： opusTCv20210807+bt_transformer-big_2022-03-17.test.txt
• 测试集得分： opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt
• 基准结果：benchmark_results.txt
• 基准输出：benchmark_translations.zip

致谢

该工作得到 European Language Grid 提供的支持，如 pilot project 2866 ，以及欧洲研究理事会（ERC）根据欧洲联盟的“Horizon 2020”研究与创新计划（授予协议编号771113）和 MeMAD project 提供的支持，该计划是由欧洲联盟的“Horizon 2020”研究与创新计划在授权协议号780069下提供的。我们还感谢 CSC -- IT Center for Science 提供的慷慨的计算资源和IT基础设施，芬兰。

模型转换信息

• transformers 版本：4.16.2
• OPUS-MT git散列：3405783
• 转换时间：Wed Apr 13 20:12:26 EEST 2022
• 转换机器：LM0-400-22516.local