Add multilingual to the language tag
Browse filesHi! A PR to add multilingual to the language tag to improve the referencing.
README.md
CHANGED
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@@ -2,97 +2,91 @@
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language:
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- en
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- fi
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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-en-fi
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results:
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- task:
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name: Translation eng-fin
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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: eng fin devtest
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metrics:
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-
-
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type: bleu
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value: 27.6
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- task:
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name: Translation eng-fin
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type: translation
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-
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dataset:
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name: newsdev2015
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type: newsdev2015
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args: eng-fin
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metrics:
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-
-
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type: bleu
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value: 24.2
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- task:
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name: Translation eng-fin
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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: eng-fin
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metrics:
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-
-
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type: bleu
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value: 39.3
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- task:
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-
name: Translation eng-fin
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type: translation
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-
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dataset:
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name: newstest2015
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type: wmt-2015-news
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args: eng-fin
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metrics:
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-
-
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type: bleu
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value: 26.4
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- task:
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-
name: Translation eng-fin
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type: translation
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-
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dataset:
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name: newstest2016
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type: wmt-2016-news
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args: eng-fin
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metrics:
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-
-
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type: bleu
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value: 28.8
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- task:
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-
name: Translation eng-fin
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type: translation
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-
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dataset:
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name: newstest2017
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type: wmt-2017-news
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args: eng-fin
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metrics:
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-
-
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type: bleu
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value: 31.3
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- task:
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-
name: Translation eng-fin
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type: translation
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-
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dataset:
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name: newstest2019
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type: wmt-2019-news
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args: eng-fin
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metrics:
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-
-
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-
type: bleu
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value: 26.4
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---
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# opus-mt-tc-big-en-fi
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@@ -100,7 +94,7 @@ Neural machine translation model for translating from English (en) to Finnish (f
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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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@@ -164,7 +158,7 @@ for t in translated:
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print( tokenizer.decode(t, skip_special_tokens=True) )
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# expected output:
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-
#
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# Kosketa puuta!
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```
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@@ -175,7 +169,7 @@ from transformers import pipeline
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pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-fi")
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print(pipe("Russia is big."))
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-
# expected output:
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```
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## Benchmarks
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@@ -200,7 +194,7 @@ print(pipe("Russia is big."))
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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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- fi
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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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|
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model-index:
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| 11 |
- name: opus-mt-tc-big-en-fi
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results:
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- task:
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type: translation
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name: Translation eng-fin
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dataset:
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name: flores101-devtest
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type: flores_101
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args: eng fin devtest
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metrics:
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- type: bleu
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value: 27.6
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name: BLEU
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- task:
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type: translation
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+
name: Translation eng-fin
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dataset:
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name: newsdev2015
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type: newsdev2015
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args: eng-fin
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metrics:
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+
- type: bleu
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value: 24.2
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+
name: BLEU
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- task:
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type: translation
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+
name: Translation eng-fin
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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: eng-fin
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metrics:
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+
- type: bleu
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value: 39.3
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+
name: BLEU
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- task:
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type: translation
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+
name: Translation eng-fin
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dataset:
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name: newstest2015
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type: wmt-2015-news
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args: eng-fin
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metrics:
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+
- type: bleu
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value: 26.4
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+
name: BLEU
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- task:
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type: translation
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+
name: Translation eng-fin
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dataset:
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name: newstest2016
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type: wmt-2016-news
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args: eng-fin
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metrics:
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+
- type: bleu
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value: 28.8
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+
name: BLEU
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- task:
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type: translation
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+
name: Translation eng-fin
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dataset:
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name: newstest2017
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type: wmt-2017-news
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args: eng-fin
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metrics:
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+
- type: bleu
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value: 31.3
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+
name: BLEU
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- task:
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type: translation
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+
name: Translation eng-fin
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dataset:
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name: newstest2019
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type: wmt-2019-news
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args: eng-fin
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metrics:
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+
- type: bleu
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value: 26.4
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+
name: BLEU
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---
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# opus-mt-tc-big-en-fi
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|
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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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print( tokenizer.decode(t, skip_special_tokens=True) )
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# expected output:
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# Ven�j� on suuri.
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# Kosketa puuta!
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```
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pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-fi")
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print(pipe("Russia is big."))
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# expected output: Ven�j� on suuri.
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```
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## Benchmarks
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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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