Translation
Transformers
PyTorch
Safetensors
English
Japanese
multilingual
marian
text2text-generation
opus-mt-tc
Eval Results (legacy)
Instructions to use gsarti/opus-mt-tc-base-en-ja 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-ja 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-ja")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/opus-mt-tc-base-en-ja") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/opus-mt-tc-base-en-ja", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Opus Tatoeba English-Japanese
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): jpn
- model: transformer-align
- pre-processing: normalization + SentencePiece (spm32k,spm32k)
- download: opus+bt-2021-04-10.zip
- test set translations: opus+bt-2021-04-10.test.txt
- test set scores: opus+bt-2021-04-10.eval.txt
Benchmarks
| testset | BLEU | chr-F | #sent | #words | BP |
|---|---|---|---|---|---|
| Tatoeba-test.eng-jpn | 15.2 | 0.258 | 10000 | 99206 | 1.000 |
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Evaluation results
- BLEU on tatoeba-test-v2021-08-07self-reported15.200