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--- |
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library_name: transformers |
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language: |
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- de |
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- en |
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- es |
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- fr |
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- lt |
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- lv |
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- prg |
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- pt |
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- sgs |
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tags: |
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- translation |
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- opus-mt-tc-bible |
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license: apache-2.0 |
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model-index: |
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- name: opus-mt-tc-bible-big-deu_eng_fra_por_spa-bat |
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results: |
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- task: |
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name: Translation deu-lit |
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type: translation |
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args: deu-lit |
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dataset: |
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name: flores200-devtest |
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type: flores200-devtest |
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args: deu-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 22.6 |
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- name: chr-F |
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type: chrf |
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value: 0.54957 |
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- task: |
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name: Translation eng-lit |
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type: translation |
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args: eng-lit |
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dataset: |
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name: flores200-devtest |
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type: flores200-devtest |
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args: eng-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 27.7 |
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- name: chr-F |
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type: chrf |
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value: 0.59338 |
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- task: |
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name: Translation fra-lit |
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type: translation |
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args: fra-lit |
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dataset: |
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name: flores200-devtest |
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type: flores200-devtest |
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args: fra-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 22.3 |
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- name: chr-F |
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type: chrf |
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value: 0.54683 |
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- task: |
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name: Translation por-lit |
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type: translation |
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args: por-lit |
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dataset: |
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name: flores200-devtest |
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type: flores200-devtest |
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args: por-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 22.6 |
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- name: chr-F |
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type: chrf |
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value: 0.55033 |
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- task: |
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name: Translation spa-lit |
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type: translation |
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args: spa-lit |
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dataset: |
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name: flores200-devtest |
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type: flores200-devtest |
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args: spa-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 16.9 |
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- name: chr-F |
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type: chrf |
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value: 0.50725 |
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- task: |
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name: Translation deu-lav |
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type: translation |
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args: deu-lav |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: deu lav 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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- name: chr-F |
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type: chrf |
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value: 0.54724 |
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- task: |
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name: Translation eng-lav |
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type: translation |
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args: eng-lav |
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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 lav devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 31.0 |
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- name: chr-F |
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type: chrf |
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value: 0.59955 |
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- task: |
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name: Translation eng-lit |
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type: translation |
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args: eng-lit |
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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 lit devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 27.2 |
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- name: chr-F |
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type: chrf |
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value: 0.58961 |
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- task: |
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name: Translation fra-lav |
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type: translation |
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args: fra-lav |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: fra lav devtest |
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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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- name: chr-F |
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type: chrf |
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value: 0.54276 |
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- task: |
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name: Translation fra-lit |
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type: translation |
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args: fra-lit |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: fra lit devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 22.4 |
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- name: chr-F |
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type: chrf |
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value: 0.54665 |
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- task: |
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name: Translation spa-lav |
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type: translation |
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args: spa-lav |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: spa lav devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 17.8 |
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- name: chr-F |
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type: chrf |
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value: 0.50131 |
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- task: |
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name: Translation deu-lav |
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type: translation |
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args: deu-lav |
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dataset: |
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name: ntrex128 |
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type: ntrex128 |
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args: deu-lav |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 16.8 |
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- name: chr-F |
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type: chrf |
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value: 0.47980 |
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- task: |
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name: Translation deu-lit |
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type: translation |
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args: deu-lit |
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dataset: |
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name: ntrex128 |
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type: ntrex128 |
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args: deu-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 17.6 |
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- name: chr-F |
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type: chrf |
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value: 0.50645 |
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- task: |
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name: Translation eng-lav |
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type: translation |
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args: eng-lav |
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dataset: |
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name: ntrex128 |
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type: ntrex128 |
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args: eng-lav |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 20.6 |
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- name: chr-F |
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type: chrf |
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value: 0.51026 |
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- task: |
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name: Translation eng-lit |
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type: translation |
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args: eng-lit |
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dataset: |
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name: ntrex128 |
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type: ntrex128 |
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args: eng-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 21.5 |
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- name: chr-F |
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type: chrf |
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value: 0.54187 |
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- task: |
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name: Translation fra-lav |
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type: translation |
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args: fra-lav |
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dataset: |
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name: ntrex128 |
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type: ntrex128 |
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args: fra-lav |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 15.5 |
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- name: chr-F |
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type: chrf |
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value: 0.45346 |
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- task: |
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name: Translation fra-lit |
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type: translation |
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args: fra-lit |
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dataset: |
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name: ntrex128 |
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type: ntrex128 |
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args: fra-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 16.2 |
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- name: chr-F |
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type: chrf |
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value: 0.48870 |
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- task: |
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name: Translation por-lav |
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type: translation |
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args: por-lav |
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dataset: |
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name: ntrex128 |
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type: ntrex128 |
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args: por-lav |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 17.3 |
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- name: chr-F |
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type: chrf |
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value: 0.47809 |
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- task: |
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name: Translation por-lit |
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type: translation |
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args: por-lit |
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dataset: |
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name: ntrex128 |
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type: ntrex128 |
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args: por-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 17.5 |
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- name: chr-F |
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type: chrf |
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value: 0.50653 |
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- task: |
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name: Translation spa-lav |
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type: translation |
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args: spa-lav |
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dataset: |
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name: ntrex128 |
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type: ntrex128 |
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args: spa-lav |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 17.1 |
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- name: chr-F |
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type: chrf |
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value: 0.47690 |
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- task: |
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name: Translation spa-lit |
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type: translation |
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args: spa-lit |
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dataset: |
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name: ntrex128 |
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type: ntrex128 |
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args: spa-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 17.1 |
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- name: chr-F |
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type: chrf |
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value: 0.50412 |
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- task: |
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name: Translation deu-lit |
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type: translation |
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args: deu-lit |
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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: deu-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 39.8 |
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- name: chr-F |
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type: chrf |
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value: 0.65379 |
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- task: |
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name: Translation eng-lav |
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type: translation |
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args: eng-lav |
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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-lav |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 46.4 |
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- name: chr-F |
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type: chrf |
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value: 0.68823 |
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- task: |
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name: Translation eng-lit |
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type: translation |
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args: eng-lit |
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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-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 39.8 |
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- name: chr-F |
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type: chrf |
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value: 0.67792 |
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- task: |
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name: Translation multi-multi |
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type: translation |
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args: multi-multi |
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dataset: |
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name: tatoeba-test-v2020-07-28-v2023-09-26 |
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type: tatoeba_mt |
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args: multi-multi |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 43.3 |
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- name: chr-F |
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type: chrf |
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value: 0.68018 |
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- task: |
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name: Translation spa-lit |
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type: translation |
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args: spa-lit |
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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: spa-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 43.3 |
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- name: chr-F |
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type: chrf |
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value: 0.68133 |
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- task: |
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name: Translation eng-lav |
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type: translation |
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args: eng-lav |
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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-lav |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 21.5 |
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- name: chr-F |
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type: chrf |
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value: 0.53192 |
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- task: |
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name: Translation eng-lit |
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type: translation |
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args: eng-lit |
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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-lit |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 18.3 |
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- name: chr-F |
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type: chrf |
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value: 0.51714 |
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--- |
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# opus-mt-tc-bible-big-deu_eng_fra_por_spa-bat |
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## Table of Contents |
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- [Model Details](#model-details) |
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- [Uses](#uses) |
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- [Risks, Limitations and Biases](#risks-limitations-and-biases) |
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- [How to Get Started With the Model](#how-to-get-started-with-the-model) |
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- [Training](#training) |
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- [Evaluation](#evaluation) |
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- [Citation Information](#citation-information) |
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- [Acknowledgements](#acknowledgements) |
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|
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## Model Details |
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Neural machine translation model for translating from unknown (deu+eng+fra+por+spa) to Baltic languages (bat). |
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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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**Model Description:** |
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- **Developed by:** Language Technology Research Group at the University of Helsinki |
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- **Model Type:** Translation (transformer-big) |
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- **Release**: 2024-05-30 |
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- **License:** Apache-2.0 |
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- **Language(s):** |
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- Source Language(s): deu eng fra por spa |
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- Target Language(s): lav lit prg sgs |
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- Valid Target Language Labels: >>lav<< >>lit<< >>ndf<< >>olt<< >>prg<< >>prg_Latn<< >>sgs<< >>svx<< >>sxl<< >>xcu<< >>xgl<< >>xsv<< >>xzm<< |
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- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-bat/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip) |
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- **Resources for more information:** |
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- [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/deu%2Beng%2Bfra%2Bpor%2Bspa-bat/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-30) |
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- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) |
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- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) |
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- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/) |
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- [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1) |
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- [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/) |
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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. `>>lav<<` |
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## Uses |
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This model can be used for translation and text-to-text generation. |
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## Risks, Limitations and Biases |
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**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** |
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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). |
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## How to Get Started With the Model |
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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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">>lav<< Replace this with text in an accepted source language.", |
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">>sgs<< This is the second sentence." |
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] |
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model_name = "pytorch-models/opus-mt-tc-bible-big-deu_eng_fra_por_spa-bat" |
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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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``` |
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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-bible-big-deu_eng_fra_por_spa-bat") |
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print(pipe(">>lav<< Replace this with text in an accepted source language.")) |
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``` |
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## Training |
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- **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
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- **Pre-processing**: SentencePiece (spm32k,spm32k) |
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- **Model Type:** transformer-big |
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- **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-bat/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip) |
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- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) |
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## Evaluation |
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* [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/deu%2Beng%2Bfra%2Bpor%2Bspa-bat/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-30) |
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* test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-bat/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt) |
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* test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-bat/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.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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|
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| langpair | testset | chr-F | BLEU | #sent | #words | |
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|----------|---------|-------|-------|-------|--------| |
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| deu-lit | tatoeba-test-v2021-08-07 | 0.65379 | 39.8 | 1115 | 7091 | |
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| eng-lav | tatoeba-test-v2021-08-07 | 0.68823 | 46.4 | 1631 | 9932 | |
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| eng-lit | tatoeba-test-v2021-08-07 | 0.67792 | 39.8 | 2528 | 14942 | |
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| spa-lit | tatoeba-test-v2021-08-07 | 0.68133 | 43.3 | 454 | 2352 | |
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| deu-lav | flores101-devtest | 0.54724 | 24.4 | 1012 | 22092 | |
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| eng-lav | flores101-devtest | 0.59955 | 31.0 | 1012 | 22092 | |
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| eng-lit | flores101-devtest | 0.58961 | 27.2 | 1012 | 20695 | |
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| fra-lav | flores101-devtest | 0.54276 | 24.2 | 1012 | 22092 | |
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| fra-lit | flores101-devtest | 0.54665 | 22.4 | 1012 | 20695 | |
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| spa-lav | flores101-devtest | 0.50131 | 17.8 | 1012 | 22092 | |
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| deu-lit | flores200-devtest | 0.54957 | 22.6 | 1012 | 20695 | |
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| eng-lit | flores200-devtest | 0.59338 | 27.7 | 1012 | 20695 | |
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| fra-lit | flores200-devtest | 0.54683 | 22.3 | 1012 | 20695 | |
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| por-lit | flores200-devtest | 0.55033 | 22.6 | 1012 | 20695 | |
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| spa-lit | flores200-devtest | 0.50725 | 16.9 | 1012 | 20695 | |
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| eng-lav | newstest2017 | 0.53192 | 21.5 | 2001 | 39392 | |
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| eng-lit | newstest2019 | 0.51714 | 18.3 | 998 | 19711 | |
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| deu-lav | ntrex128 | 0.47980 | 16.8 | 1997 | 44709 | |
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| deu-lit | ntrex128 | 0.50645 | 17.6 | 1997 | 41189 | |
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| eng-lav | ntrex128 | 0.51026 | 20.6 | 1997 | 44709 | |
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| eng-lit | ntrex128 | 0.54187 | 21.5 | 1997 | 41189 | |
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| fra-lav | ntrex128 | 0.45346 | 15.5 | 1997 | 44709 | |
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| fra-lit | ntrex128 | 0.48870 | 16.2 | 1997 | 41189 | |
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| por-lav | ntrex128 | 0.47809 | 17.3 | 1997 | 44709 | |
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| por-lit | ntrex128 | 0.50653 | 17.5 | 1997 | 41189 | |
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| spa-lav | ntrex128 | 0.47690 | 17.1 | 1997 | 44709 | |
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| spa-lit | ntrex128 | 0.50412 | 17.1 | 1997 | 41189 | |
|
|
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## Citation Information |
|
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* Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [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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```bibtex |
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@article{tiedemann2023democratizing, |
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title={Democratizing neural machine translation with {OPUS-MT}}, |
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author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami}, |
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journal={Language Resources and Evaluation}, |
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number={58}, |
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pages={713--755}, |
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year={2023}, |
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publisher={Springer Nature}, |
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issn={1574-0218}, |
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doi={10.1007/s10579-023-09704-w} |
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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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## Acknowledgements |
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The work is supported by the [HPLT project](https://hplt-project.org/), funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/). |
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## Model conversion info |
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* transformers version: 4.45.1 |
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* OPUS-MT git hash: 0882077 |
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* port time: Tue Oct 8 08:59:36 EEST 2024 |
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* port machine: LM0-400-22516.local |
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