Translation
Transformers
PyTorch
English
Dutch
multilingual
marian
text2text-generation
opus-mt-tc
Eval Results (legacy)
Instructions to use gsarti/opus-mt-tc-base-en-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/opus-mt-tc-base-en-nl 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="gsarti/opus-mt-tc-base-en-nl")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/opus-mt-tc-base-en-nl") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/opus-mt-tc-base-en-nl") - Notebooks
- Google Colab
- Kaggle
Opus Tatoeba English-Dutch
This model was obtained by running the script convert_marian_to_pytorch.py. The original models were trained by J�rg Tiedemann using the MarianNMT library. See all available MarianMTModel models on the profile of the Helsinki NLP group.
- dataset: opus+bt
- model: transformer-align
- source language(s): eng
- target language(s): nld
- model: transformer-align
- pre-processing: normalization + SentencePiece (spm32k,spm32k)
- download: opus+bt-2021-04-14.zip
- test set translations: opus+bt-2021-04-14.test.txt
- test set scores: opus+bt-2021-04-14.eval.txt
Benchmarks
| testset | BLEU | chr-F | #sent | #words | BP |
|---|---|---|---|---|---|
| Tatoeba-test.eng-nld | 57.5 | 0.731 | 10000 | 71436 | 0.986 |
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Evaluation results
- BLEU on tatoeba-test-v2021-08-07self-reported57.500