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--- |
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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-zls-zle |
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results: |
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- task: |
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name: Translation bul-rus |
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type: translation |
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args: bul-rus |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: bul rus devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 24.6 |
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- task: |
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name: Translation bul-ukr |
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type: translation |
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args: bul-ukr |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: bul ukr devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 22.9 |
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- task: |
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name: Translation hrv-rus |
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type: translation |
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args: hrv-rus |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: hrv rus devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 23.5 |
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- task: |
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name: Translation hrv-ukr |
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type: translation |
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args: hrv-ukr |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: hrv ukr devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 21.9 |
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- task: |
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name: Translation mkd-rus |
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type: translation |
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args: mkd-rus |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: mkd rus 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 |
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- task: |
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name: Translation mkd-ukr |
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type: translation |
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args: mkd-ukr |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: mkd ukr devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 22.5 |
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- task: |
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name: Translation slv-rus |
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type: translation |
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args: slv-rus |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: slv rus devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 22.0 |
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- task: |
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name: Translation slv-ukr |
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type: translation |
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args: slv-ukr |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: slv ukr devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 20.2 |
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- task: |
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name: Translation srp_Cyrl-rus |
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type: translation |
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args: srp_Cyrl-rus |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: srp_Cyrl rus devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 25.7 |
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- task: |
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name: Translation srp_Cyrl-ukr |
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type: translation |
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args: srp_Cyrl-ukr |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: srp_Cyrl ukr devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 24.4 |
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- task: |
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name: Translation bul-rus |
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type: translation |
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args: bul-rus |
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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: bul-rus |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 52.6 |
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- task: |
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name: Translation bul-ukr |
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type: translation |
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args: bul-ukr |
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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: bul-ukr |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 53.3 |
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- task: |
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name: Translation hbs-rus |
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type: translation |
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args: hbs-rus |
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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: hbs-rus |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 58.5 |
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- task: |
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name: Translation hbs-ukr |
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type: translation |
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args: hbs-ukr |
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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: hbs-ukr |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 52.3 |
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- task: |
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name: Translation hrv-ukr |
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type: translation |
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args: hrv-ukr |
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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: hrv-ukr |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 50.0 |
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- task: |
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name: Translation slv-rus |
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type: translation |
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args: slv-rus |
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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: slv-rus |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 27.3 |
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- task: |
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name: Translation srp_Cyrl-rus |
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type: translation |
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args: srp_Cyrl-rus |
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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: srp_Cyrl-rus |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 56.2 |
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- task: |
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name: Translation srp_Cyrl-ukr |
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type: translation |
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args: srp_Cyrl-ukr |
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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: srp_Cyrl-ukr |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 51.8 |
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- task: |
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name: Translation srp_Latn-rus |
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type: translation |
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args: srp_Latn-rus |
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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: srp_Latn-rus |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 60.1 |
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- task: |
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name: Translation srp_Latn-ukr |
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type: translation |
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args: srp_Latn-ukr |
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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: srp_Latn-ukr |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 55.8 |
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--- |
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# opus-mt-tc-big-zls-zle |
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|
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Neural machine translation model for translating from South Slavic languages (zls) to East Slavic languages (zle). |
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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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@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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@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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## Model info |
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|
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* Release: 2022-03-23 |
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* source language(s): bul hbs hrv slv srp_Cyrl srp_Latn |
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* target language(s): bel rus ukr |
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* valid target language labels: >>bel<< >>rus<< >>ukr<< |
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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/zls-zle/opusTCv20210807+bt_transformer-big_2022-03-23.zip) |
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* more information released models: [OPUS-MT zls-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-zle/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. `>>bel<<` |
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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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">>rus<< Gdje je brigadir?", |
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">>ukr<< Zovem se Seli." |
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] |
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model_name = "pytorch-models/opus-mt-tc-big-zls-zle" |
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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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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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# Где бригадир? |
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# Мене звати Саллі. |
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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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```python |
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from transformers import pipeline |
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pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zls-zle") |
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print(pipe(">>rus<< Gdje je brigadir?")) |
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# expected output: Где бригадир? |
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``` |
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## Benchmarks |
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* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-zle/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/zls-zle/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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| bul-rus | tatoeba-test-v2021-08-07 | 0.71467 | 52.6 | 1247 | 7870 | |
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| bul-ukr | tatoeba-test-v2021-08-07 | 0.71757 | 53.3 | 1020 | 4932 | |
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| hbs-rus | tatoeba-test-v2021-08-07 | 0.74593 | 58.5 | 2500 | 14213 | |
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| hbs-ukr | tatoeba-test-v2021-08-07 | 0.70244 | 52.3 | 942 | 4961 | |
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| hrv-ukr | tatoeba-test-v2021-08-07 | 0.68931 | 50.0 | 389 | 2232 | |
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| slv-rus | tatoeba-test-v2021-08-07 | 0.42255 | 27.3 | 657 | 4056 | |
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| srp_Cyrl-rus | tatoeba-test-v2021-08-07 | 0.74112 | 56.2 | 881 | 5117 | |
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| srp_Cyrl-ukr | tatoeba-test-v2021-08-07 | 0.68915 | 51.8 | 205 | 1061 | |
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| srp_Latn-rus | tatoeba-test-v2021-08-07 | 0.75340 | 60.1 | 1483 | 8311 | |
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| srp_Latn-ukr | tatoeba-test-v2021-08-07 | 0.73106 | 55.8 | 348 | 1668 | |
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| bul-rus | flores101-devtest | 0.54226 | 24.6 | 1012 | 23295 | |
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| bul-ukr | flores101-devtest | 0.53382 | 22.9 | 1012 | 22810 | |
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| hrv-rus | flores101-devtest | 0.51726 | 23.5 | 1012 | 23295 | |
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| hrv-ukr | flores101-devtest | 0.51011 | 21.9 | 1012 | 22810 | |
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| mkd-bel | flores101-devtest | 0.40885 | 10.7 | 1012 | 24829 | |
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| mkd-rus | flores101-devtest | 0.52509 | 24.3 | 1012 | 23295 | |
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| mkd-ukr | flores101-devtest | 0.52021 | 22.5 | 1012 | 22810 | |
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| slv-rus | flores101-devtest | 0.50349 | 22.0 | 1012 | 23295 | |
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| slv-ukr | flores101-devtest | 0.49156 | 20.2 | 1012 | 22810 | |
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| srp_Cyrl-rus | flores101-devtest | 0.53656 | 25.7 | 1012 | 23295 | |
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| srp_Cyrl-ukr | flores101-devtest | 0.53623 | 24.4 | 1012 | 22810 | |
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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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* transformers version: 4.16.2 |
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* OPUS-MT git hash: 1bdabf7 |
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* port time: Thu Mar 24 04:08:51 EET 2022 |
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* port machine: LM0-400-22516.local |
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