Helsinki-NLP_opus-mt-tc-big-sh-en

---

language:

• bs_Latn
• en
• hr
• sh
• sr_Cyrl
• sr_Latn tags:
• translation
• opus-mt-tc license: cc-by-4.0 model-index:
• name: opus-mt-tc-big-sh-en results:
  • task: name: Translation hrv-eng type: translation args: hrv-eng dataset: name: flores101-devtest type: flores_101 args: hrv eng devtest metrics:
    • name: BLEU type: bleu value: 37.1
  • task: name: Translation bos_Latn-eng type: translation args: bos_Latn-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: bos_Latn-eng metrics:
    • name: BLEU type: bleu value: 66.5
  • task: name: Translation hbs-eng type: translation args: hbs-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hbs-eng metrics:
    • name: BLEU type: bleu value: 56.4
  • task: name: Translation hrv-eng type: translation args: hrv-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hrv-eng metrics:
    • name: BLEU type: bleu value: 58.8
  • task: name: Translation srp_Cyrl-eng type: translation args: srp_Cyrl-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: srp_Cyrl-eng metrics:
    • name: BLEU type: bleu value: 44.7
  • task: name: Translation srp_Latn-eng type: translation args: srp_Latn-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: srp_Latn-eng metrics:
    • name: BLEU type: bleu value: 58.4

---

opus-mt-tc-big-sh-en

Neural machine translation model for translating from Serbo-Croatian (sh) to English (en).

This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from OPUS and training pipelines use the procedures of OPUS-MT-train.

• Publications: OPUS-MT – Building open translation services for the World and The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT (Please, cite if you use this model.)

@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",
}

Model info

• Release: 2022-02-25
• source language(s): bos_Latn hrv srp_Cyrl srp_Latn
• target language(s): eng
• model: transformer-big
• data: opusTCv20210807+bt (source)
• tokenization: SentencePiece (spm32k,spm32k)
• original model: opusTCv20210807+bt_transformer-big_2022-02-25.zip
• more information released models: OPUS-MT hbs-eng README

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Ispostavilo se da je istina.",
    "Ovaj vikend imamo besplatne pozive."
]

model_name = "pytorch-models/opus-mt-tc-big-sh-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:
#     Turns out it's true.
#     We got free calls this weekend.

You can also use OPUS-MT models with the transformers pipelines, for example:

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-sh-en")
print(pipe("Ispostavilo se da je istina."))

# expected output: Turns out it's true.

Benchmarks

• test set translations: opusTCv20210807+bt_transformer-big_2022-02-25.test.txt
• test set scores: opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt
• benchmark results: benchmark_results.txt
• benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
bos_Latn-eng	tatoeba-test-v2021-08-07	0.80010	66.5	301	1826
hbs-eng	tatoeba-test-v2021-08-07	0.71744	56.4	10017	68934
hrv-eng	tatoeba-test-v2021-08-07	0.73563	58.8	1480	10620
srp_Cyrl-eng	tatoeba-test-v2021-08-07	0.68248	44.7	1580	10181
srp_Latn-eng	tatoeba-test-v2021-08-07	0.71781	58.4	6656	46307
hrv-eng	flores101-devtest	0.63948	37.1	1012	24721

Acknowledgements

The work is supported by the European Language Grid as pilot project 2866, by the FoTran project, funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the MeMAD project, funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by CSC -- IT Center for Science, Finland.

Model conversion info

• transformers version: 4.16.2
• OPUS-MT git hash: 3405783
• port time: Wed Apr 13 19:21:10 EEST 2022
• port machine: LM0-400-22516.local