Add multilingual to the language tag
#2
by
lbourdois
- opened
README.md
CHANGED
@@ -2,49 +2,47 @@
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language:
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- en
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- hu
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tags:
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- translation
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- opus-mt-tc
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-
license: cc-by-4.0
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model-index:
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- name: opus-mt-tc-big-hu-en
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results:
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- task:
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name: Translation hun-eng
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type: translation
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-
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dataset:
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name: flores101-devtest
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type: flores_101
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args: hun eng devtest
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metrics:
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-
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type: bleu
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value: 34.6
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- task:
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name: Translation hun-eng
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type: translation
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-
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dataset:
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name: tatoeba-test-v2021-08-07
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type: tatoeba_mt
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args: hun-eng
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metrics:
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-
-
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type: bleu
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value: 50.4
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- task:
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name: Translation hun-eng
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type: translation
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-
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dataset:
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name: newstest2009
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type: wmt-2009-news
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args: hun-eng
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metrics:
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-
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type: bleu
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value: 23.4
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---
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# opus-mt-tc-big-hu-en
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@@ -52,7 +50,7 @@ Neural machine translation model for translating from Hungarian (hu) to English
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This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), 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](https://marian-nmt.github.io/), 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](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
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* Publications: [OPUS-MT
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```
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@inproceedings{tiedemann-thottingal-2020-opus,
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@@ -99,8 +97,8 @@ A short example code:
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from transformers import MarianMTModel, MarianTokenizer
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src_text = [
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"
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"
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]
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model_name = "pytorch-models/opus-mt-tc-big-hu-en"
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@@ -121,7 +119,7 @@ You can also use OPUS-MT models with the transformers pipelines, for example:
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```python
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from transformers import pipeline
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pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-hu-en")
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-
print(pipe("
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# expected output: I wish I hadn't seen such a terrible movie.
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```
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@@ -142,7 +140,7 @@ print(pipe("Bárcsak ne láttam volna ilyen borzalmas filmet!"))
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## Acknowledgements
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-
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union
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## Model conversion info
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language:
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- en
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- hu
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- multilingual
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license: cc-by-4.0
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tags:
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- translation
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- opus-mt-tc
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model-index:
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- name: opus-mt-tc-big-hu-en
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results:
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- task:
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type: translation
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name: Translation hun-eng
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dataset:
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name: flores101-devtest
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type: flores_101
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args: hun eng devtest
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metrics:
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- type: bleu
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value: 34.6
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name: BLEU
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- task:
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type: translation
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name: Translation hun-eng
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dataset:
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name: tatoeba-test-v2021-08-07
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type: tatoeba_mt
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args: hun-eng
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metrics:
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- type: bleu
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value: 50.4
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name: BLEU
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- task:
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type: translation
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name: Translation hun-eng
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dataset:
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name: newstest2009
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type: wmt-2009-news
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args: hun-eng
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metrics:
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- type: bleu
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value: 23.4
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name: BLEU
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---
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# opus-mt-tc-big-hu-en
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This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), 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](https://marian-nmt.github.io/), 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](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
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* Publications: [OPUS-MT � Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge � Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
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```
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@inproceedings{tiedemann-thottingal-2020-opus,
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from transformers import MarianMTModel, MarianTokenizer
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src_text = [
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"B�rcsak ne l�ttam volna ilyen borzalmas filmet!",
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"Iskol�ban van."
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]
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model_name = "pytorch-models/opus-mt-tc-big-hu-en"
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```python
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from transformers import pipeline
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pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-hu-en")
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print(pipe("B�rcsak ne l�ttam volna ilyen borzalmas filmet!"))
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# expected output: I wish I hadn't seen such a terrible movie.
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```
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## Acknowledgements
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The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), 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](https://memad.eu/), 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](https://www.csc.fi/), Finland.
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## Model conversion info
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