Instructions to use Helsinki-NLP/opus-mt-cs-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-cs-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-cs-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-cs-en") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-cs-en", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ef4a84b553c9a1bc6a67235a8206de71caa71f3e489a8d5e8e5ed109a202cfa7
- Size of remote file:
- 307 MB
- SHA256:
- 91000ac36ad57cbcf06c0fe601107d3aa84440e00ce5b9b5c1f564b0605136e1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.