Add multilingual to the language tag

#2
by lbourdois - opened
Files changed (1) hide show
  1. README.md +27 -33
README.md CHANGED
@@ -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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- args: 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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- - name: BLEU
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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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- args: 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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- - name: BLEU
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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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- args: 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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- - name: BLEU
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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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- args: 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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- - name: BLEU
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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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- args: 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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- - name: BLEU
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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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- args: 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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- - name: BLEU
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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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- args: 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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- - name: BLEU
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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 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,
@@ -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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- # Venäjä on suuri.
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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: Venäjä on suuri.
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  ```
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  ## Benchmarks
@@ -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 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
206
 
 
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  language:
3
  - en
4
  - fi
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+ - multilingual
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+ license: cc-by-4.0
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  tags:
8
  - translation
9
  - opus-mt-tc
 
10
  model-index:
11
  - name: opus-mt-tc-big-en-fi
12
  results:
13
  - task:
 
14
  type: translation
15
+ name: Translation eng-fin
16
  dataset:
17
  name: flores101-devtest
18
  type: flores_101
19
  args: eng fin devtest
20
  metrics:
21
+ - 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
30
  args: eng-fin
31
  metrics:
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+ - type: bleu
 
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  value: 24.2
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+ name: BLEU
35
  - task:
 
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  type: translation
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+ name: Translation eng-fin
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  dataset:
39
  name: tatoeba-test-v2021-08-07
40
  type: tatoeba_mt
41
  args: eng-fin
42
  metrics:
43
+ - type: bleu
 
44
  value: 39.3
45
+ 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:
50
  name: newstest2015
51
  type: wmt-2015-news
52
  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
63
  args: eng-fin
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  metrics:
65
+ - type: bleu
 
66
  value: 28.8
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+ name: BLEU
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  - task:
 
69
  type: translation
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+ name: Translation eng-fin
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  dataset:
72
  name: newstest2017
73
  type: wmt-2017-news
74
  args: eng-fin
75
  metrics:
76
+ - type: bleu
 
77
  value: 31.3
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+ name: BLEU
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  - task:
 
80
  type: translation
81
+ name: Translation eng-fin
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  dataset:
83
  name: newstest2019
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  type: wmt-2019-news
85
  args: eng-fin
86
  metrics:
87
+ - type: bleu
 
88
  value: 26.4
89
+ name: BLEU
90
  ---
91
  # opus-mt-tc-big-en-fi
92
 
 
94
 
95
  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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97
+ * 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.)
98
 
99
  ```
100
  @inproceedings{tiedemann-thottingal-2020-opus,
 
158
  print( tokenizer.decode(t, skip_special_tokens=True) )
159
 
160
  # expected output:
161
+ # Ven�j� on suuri.
162
  # Kosketa puuta!
163
  ```
164
 
 
169
  pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-fi")
170
  print(pipe("Russia is big."))
171
 
172
+ # expected output: Ven�j� on suuri.
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  ```
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175
  ## Benchmarks
 
194
 
195
  ## Acknowledgements
196
 
197
+ 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.
198
 
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  ## Model conversion info
200