Add multilingual to the language tag

#2
by lbourdois - opened
Files changed (1) hide show
  1. README.md +16 -18
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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- args: 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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- - name: BLEU
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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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- args: 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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- - name: BLEU
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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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- args: 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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- - name: BLEU
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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 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,
@@ -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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- "Bárcsak ne láttam volna ilyen borzalmas filmet!",
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- "Iskolában van."
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  ]
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  model_name = "pytorch-models/opus-mt-tc-big-hu-en"
@@ -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("Bárcsak ne láttam volna ilyen borzalmas filmet!"))
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  # expected output: I wish I hadn't seen such a terrible movie.
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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 Unions Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Unions 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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  language:
3
  - en
4
  - hu
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+ - multilingual
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+ license: cc-by-4.0
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  tags:
8
  - 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:
13
  - 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 Unions Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Unions 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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