Instructions to use sofom/Style-Embedding-m4_semeval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sofom/Style-Embedding-m4_semeval with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sofom/Style-Embedding-m4_semeval")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sofom/Style-Embedding-m4_semeval") model = AutoModel.from_pretrained("sofom/Style-Embedding-m4_semeval") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1363e72d7bba4281a01732ca12d2c922350b3ada5a5cd3173fdf24c2d5085fab
- Size of remote file:
- 249 MB
- SHA256:
- 6fcdc9fe9b5983d01b66bb63bc62669f0a8b80eb144e1d3f8ee1827790e29345
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