Instructions to use Helsinki-NLP/opus-mt-es-swc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-es-swc 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-es-swc")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-es-swc") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-es-swc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b7f70730c6854169e7461907216d296f49613b529bf4c5d38c0066f374a4fd1
- Size of remote file:
- 303 MB
- SHA256:
- 217a9cdf6b53f57067aeaefd9002a96764cdc034c8c49e55eaa922fe26c2729d
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