Instructions to use Helsinki-NLP/opus-mt-taw-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-taw-en 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-taw-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-taw-en") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-taw-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6a0744259216e5f5113b1c0d5c523651e0a19a407025a7aaf3d5eb96164a36c
- Size of remote file:
- 306 MB
- SHA256:
- e306982e51f49f7e2d8e7d7651909472fab8ca74caaa2f0b442adbe1df68c288
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.