Translation
Transformers
PyTorch
Safetensors
marian
text2text-generation
opus-mt-tc-bible
Eval Results (legacy)
Instructions to use Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mkh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mkh with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mkh")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mkh") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mkh") - Notebooks
- Google Colab
- Kaggle
| multi-multi tatoeba-test-v2020-07-28-v2023-09-26 0.43068 24.9 8636 61987 | |
| deu-khm flores101-devtest 0.38874 2.5 1012 7006 | |
| deu-vie flores101-devtest 0.53623 33.9 1012 33331 | |
| eng-khm flores101-devtest 0.42022 1.4 1012 7006 | |
| eng-vie flores101-devtest 0.59986 42.7 1012 33331 | |
| por-vie flores101-devtest 0.54819 35.7 1012 33331 | |
| spa-khm flores101-devtest 0.37253 1.5 1012 7006 | |
| deu-khm flores200-devtest 0.38872 2.5 1012 7006 | |
| deu-vie flores200-devtest 0.53535 33.9 1012 33331 | |
| eng-khm flores200-devtest 0.41987 1.3 1012 7006 | |
| eng-vie flores200-devtest 0.60021 42.6 1012 33331 | |
| fra-khm flores200-devtest 0.40241 2.3 1012 7006 | |
| fra-vie flores200-devtest 0.54168 34.6 1012 33331 | |
| por-khm flores200-devtest 0.41582 2.3 1012 7006 | |
| por-vie flores200-devtest 0.55046 35.9 1012 33331 | |
| spa-khm flores200-devtest 0.36975 1.5 1012 7006 | |
| spa-vie flores200-devtest 0.50262 28.1 1012 33331 | |
| eng-khm newstest2020 0.35200 0.9 2320 15454 | |
| deu-khm ntrex128 0.44917 3.2 1997 15866 | |
| deu-vie ntrex128 0.51996 31.2 1997 64655 | |
| eng-khm ntrex128 0.50215 1.6 1997 15866 | |
| eng-vie ntrex128 0.60050 42.7 1997 64655 | |
| fra-khm ntrex128 0.44024 2.3 1997 15866 | |
| fra-vie ntrex128 0.51988 31.7 1997 64655 | |
| por-khm ntrex128 0.46752 2.4 1997 15866 | |
| por-vie ntrex128 0.52931 33.3 1997 64655 | |
| spa-khm ntrex128 0.46166 2.5 1997 15866 | |
| spa-vie ntrex128 0.53347 33.1 1997 64655 | |
| eng-khm tatoeba-test-v2020-07-28 0.33749 0.2 752 1737 | |
| spa-khm tatoeba-test-v2020-07-28 0.36872 0.2 1472 3391 | |
| deu-vie tatoeba-test-v2021-03-30 0.45438 25.3 401 3775 | |
| eng-khm tatoeba-test-v2021-03-30 0.33751 0.2 754 1741 | |
| spa-khm tatoeba-test-v2021-03-30 0.36872 0.2 1472 3391 | |
| spa-vie tatoeba-test-v2021-03-30 0.51477 33.9 604 4824 | |
| deu-vie tatoeba-test-v2021-08-07 0.45222 25.3 400 3768 | |
| eng-kha tatoeba-test-v2021-08-07 9.076 0.4 1314 9269 | |
| eng-khm tatoeba-test-v2021-08-07 0.33349 0.2 726 1692 | |
| eng-vie tatoeba-test-v2021-08-07 0.56413 39.0 2500 24427 | |
| fra-vie tatoeba-test-v2021-08-07 0.53078 35.6 1299 13219 | |
| spa-khm tatoeba-test-v2021-08-07 0.36552 0.3 1448 3343 | |
| spa-vie tatoeba-test-v2021-08-07 0.51783 34.0 594 4740 | |
| eng-khm tico19-test 0.54267 3.4 2100 15810 | |
| fra-khm tico19-test 0.45333 4.8 2100 15810 | |
| por-khm tico19-test 0.52339 6.8 2100 15810 | |
| spa-khm tico19-test 0.51848 6.8 2100 15810 | |