Instructions to use BlackKakapo/opus-mt-en-ro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BlackKakapo/opus-mt-en-ro 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="BlackKakapo/opus-mt-en-ro")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("BlackKakapo/opus-mt-en-ro") model = AutoModelForSeq2SeqLM.from_pretrained("BlackKakapo/opus-mt-en-ro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BlackKakapo/opus-mt-en-ro: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/BlackKakapo/opus-mt-en-ro/resolve/main/training_args.bin
- Command line
-
hf download hf://BlackKakapo/opus-mt-en-ro/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BlackKakapo/opus-mt-en-ro/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- f602c881e7858fb3f5f7f0ebcb5153743999c477e27accacf985ecf7ba40a661
- Size of remote file:
- 3.58 kB
- SHA256:
- 198bcd3b5b3b4557366d3f337903d3e5833229b56aaeb8b93e0a93a96671f869
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