|
--- |
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language: |
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- be |
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- bg |
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- hr |
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- ru |
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- sh |
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- sl |
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- sr_Cyrl |
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- sr_Latn |
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- uk |
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- zle |
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- zls |
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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-zle-zls |
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results: |
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- task: |
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name: Translation rus-bul |
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type: translation |
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args: rus-bul |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: rus bul devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 28.9 |
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- task: |
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name: Translation rus-hrv |
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type: translation |
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args: rus-hrv |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: rus hrv devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 23.2 |
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- task: |
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name: Translation rus-mkd |
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type: translation |
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args: rus-mkd |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: rus mkd devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 24.3 |
|
- task: |
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name: Translation rus-slv |
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type: translation |
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args: rus-slv |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: rus slv devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 23.1 |
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- task: |
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name: Translation rus-srp_Cyrl |
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type: translation |
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args: rus-srp_Cyrl |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: rus srp_Cyrl devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 24.1 |
|
- task: |
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name: Translation ukr-bul |
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type: translation |
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args: ukr-bul |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: ukr bul devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 30.8 |
|
- task: |
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name: Translation ukr-hrv |
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type: translation |
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args: ukr-hrv |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: ukr hrv devtest |
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metrics: |
|
- name: BLEU |
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type: bleu |
|
value: 24.6 |
|
- task: |
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name: Translation ukr-mkd |
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type: translation |
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args: ukr-mkd |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: ukr mkd devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 26.2 |
|
- task: |
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name: Translation ukr-slv |
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type: translation |
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args: ukr-slv |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: ukr slv devtest |
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metrics: |
|
- name: BLEU |
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type: bleu |
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value: 24.2 |
|
- task: |
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name: Translation ukr-srp_Cyrl |
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type: translation |
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args: ukr-srp_Cyrl |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: ukr srp_Cyrl devtest |
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metrics: |
|
- name: BLEU |
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type: bleu |
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value: 26.2 |
|
- task: |
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name: Translation rus-bul |
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type: translation |
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args: rus-bul |
|
dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: rus-bul |
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metrics: |
|
- name: BLEU |
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type: bleu |
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value: 53.7 |
|
- task: |
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name: Translation rus-hbs |
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type: translation |
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args: rus-hbs |
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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: rus-hbs |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 49.4 |
|
- task: |
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name: Translation rus-slv |
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type: translation |
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args: rus-slv |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: rus-slv |
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metrics: |
|
- name: BLEU |
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type: bleu |
|
value: 21.5 |
|
- task: |
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name: Translation rus-srp_Cyrl |
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type: translation |
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args: rus-srp_Cyrl |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: rus-srp_Cyrl |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 46.1 |
|
- task: |
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name: Translation rus-srp_Latn |
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type: translation |
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args: rus-srp_Latn |
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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: rus-srp_Latn |
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metrics: |
|
- name: BLEU |
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type: bleu |
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value: 51.7 |
|
- task: |
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name: Translation ukr-bul |
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type: translation |
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args: ukr-bul |
|
dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: ukr-bul |
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metrics: |
|
- name: BLEU |
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type: bleu |
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value: 61.3 |
|
- task: |
|
name: Translation ukr-hbs |
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type: translation |
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args: ukr-hbs |
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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: ukr-hbs |
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metrics: |
|
- name: BLEU |
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type: bleu |
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value: 52.1 |
|
- task: |
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name: Translation ukr-hrv |
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type: translation |
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args: ukr-hrv |
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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: ukr-hrv |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 50.1 |
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- task: |
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name: Translation ukr-srp_Cyrl |
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type: translation |
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args: ukr-srp_Cyrl |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: ukr-srp_Cyrl |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 54.7 |
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- task: |
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name: Translation ukr-srp_Latn |
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type: translation |
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args: ukr-srp_Latn |
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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: ukr-srp_Latn |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 53.4 |
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--- |
|
# opus-mt-tc-big-zle-zls |
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|
|
Neural machine translation model for translating from East Slavic languages (zle) to South Slavic languages (zls). |
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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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|
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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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``` |
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@inproceedings{tiedemann-thottingal-2020-opus, |
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title = "{OPUS}-{MT} {--} Building open translation services for the World", |
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author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, |
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booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Lisboa, Portugal", |
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publisher = "European Association for Machine Translation", |
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url = "https://aclanthology.org/2020.eamt-1.61", |
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pages = "479--480", |
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} |
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|
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@inproceedings{tiedemann-2020-tatoeba, |
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title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", |
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author = {Tiedemann, J{\"o}rg}, |
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booktitle = "Proceedings of the Fifth Conference on Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2020.wmt-1.139", |
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pages = "1174--1182", |
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} |
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``` |
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|
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## Model info |
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|
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* Release: 2022-03-23 |
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* source language(s): bel rus ukr |
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* target language(s): bul hbs hrv slv srp_Cyrl srp_Latn |
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* valid target language labels: >>bul<< >>hbs<< >>hrv<< >>slv<< >>srp_Cyrl<< >>srp_Latn<< |
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* model: transformer-big |
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* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
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* tokenization: SentencePiece (spm32k,spm32k) |
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* original model: [opusTCv20210807+bt_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zls/opusTCv20210807+bt_transformer-big_2022-03-23.zip) |
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* more information released models: [OPUS-MT zle-zls README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-zls/README.md) |
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* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian) |
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This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>bul<<` |
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## Usage |
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A short example code: |
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```python |
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from transformers import MarianMTModel, MarianTokenizer |
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src_text = [ |
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">>bul<< Новы каранавірус вельмі заразны.", |
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">>srp_Latn<< Моє ім'я — Саллі." |
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] |
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model_name = "pytorch-models/opus-mt-tc-big-zle-zls" |
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tokenizer = MarianTokenizer.from_pretrained(model_name) |
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model = MarianMTModel.from_pretrained(model_name) |
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translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) |
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|
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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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# Короната е силно заразна. |
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# Zovem se Sali. |
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``` |
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|
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You can also use OPUS-MT models with the transformers pipelines, for example: |
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|
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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-zle-zls") |
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print(pipe(">>bul<< Новы каранавірус вельмі заразны.")) |
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|
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# expected output: Короната е силно заразна. |
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``` |
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|
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## Benchmarks |
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|
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* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zls/opusTCv20210807+bt_transformer-big_2022-03-23.test.txt) |
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* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zls/opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt) |
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* benchmark results: [benchmark_results.txt](benchmark_results.txt) |
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* benchmark output: [benchmark_translations.zip](benchmark_translations.zip) |
|
|
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| langpair | testset | chr-F | BLEU | #sent | #words | |
|
|----------|---------|-------|-------|-------|--------| |
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| rus-bul | tatoeba-test-v2021-08-07 | 0.71515 | 53.7 | 1247 | 8272 | |
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| rus-hbs | tatoeba-test-v2021-08-07 | 0.69192 | 49.4 | 2500 | 14736 | |
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| rus-slv | tatoeba-test-v2021-08-07 | 0.38051 | 21.5 | 657 | 3969 | |
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| rus-srp_Cyrl | tatoeba-test-v2021-08-07 | 0.66622 | 46.1 | 881 | 5407 | |
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| rus-srp_Latn | tatoeba-test-v2021-08-07 | 0.70990 | 51.7 | 1483 | 8552 | |
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| ukr-bul | tatoeba-test-v2021-08-07 | 0.77283 | 61.3 | 1020 | 5181 | |
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| ukr-hbs | tatoeba-test-v2021-08-07 | 0.69401 | 52.1 | 942 | 5130 | |
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| ukr-hrv | tatoeba-test-v2021-08-07 | 0.67202 | 50.1 | 389 | 2302 | |
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| ukr-srp_Cyrl | tatoeba-test-v2021-08-07 | 0.70064 | 54.7 | 205 | 1112 | |
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| ukr-srp_Latn | tatoeba-test-v2021-08-07 | 0.72405 | 53.4 | 348 | 1716 | |
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| bel-bul | flores101-devtest | 0.49528 | 16.1 | 1012 | 24700 | |
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| bel-hrv | flores101-devtest | 0.46308 | 12.4 | 1012 | 22423 | |
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| bel-mkd | flores101-devtest | 0.48608 | 13.5 | 1012 | 24314 | |
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| bel-slv | flores101-devtest | 0.44452 | 12.2 | 1012 | 23425 | |
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| bel-srp_Cyrl | flores101-devtest | 0.44424 | 12.6 | 1012 | 23456 | |
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| rus-bul | flores101-devtest | 0.58653 | 28.9 | 1012 | 24700 | |
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| rus-hrv | flores101-devtest | 0.53494 | 23.2 | 1012 | 22423 | |
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| rus-mkd | flores101-devtest | 0.55184 | 24.3 | 1012 | 24314 | |
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| rus-slv | flores101-devtest | 0.52201 | 23.1 | 1012 | 23425 | |
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| rus-srp_Cyrl | flores101-devtest | 0.53038 | 24.1 | 1012 | 23456 | |
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| ukr-bul | flores101-devtest | 0.59625 | 30.8 | 1012 | 24700 | |
|
| ukr-hrv | flores101-devtest | 0.54530 | 24.6 | 1012 | 22423 | |
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| ukr-mkd | flores101-devtest | 0.56822 | 26.2 | 1012 | 24314 | |
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| ukr-slv | flores101-devtest | 0.53092 | 24.2 | 1012 | 23425 | |
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| ukr-srp_Cyrl | flores101-devtest | 0.54618 | 26.2 | 1012 | 23456 | |
|
|
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## Acknowledgements |
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|
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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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|
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* transformers version: 4.16.2 |
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* OPUS-MT git hash: 1bdabf7 |
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* port time: Thu Mar 24 00:46:26 EET 2022 |
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* port machine: LM0-400-22516.local |
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|
