Instructions to use minishlab/potion-multilingual-128M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use minishlab/potion-multilingual-128M with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/potion-multilingual-128M") - sentence-transformers
How to use minishlab/potion-multilingual-128M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minishlab/potion-multilingual-128M") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fad5a27f01d241804e66a47d5998b24ec06d3760a2d18141ef5443adb31e2ff6
- Size of remote file:
- 18.6 MB
- SHA256:
- 19f1909063da3cfe3bd83a782381f040dccea475f4816de11116444a73e1b6a1
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