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
Update README.md with the full model name
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by krumeto - opened
README.md
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@@ -141,7 +141,7 @@ Load this model using the `from_pretrained` method:
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from model2vec import StaticModel
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# Load a pretrained Model2Vec model
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model = StaticModel.from_pretrained("potion-multilingual-128M")
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# Compute text embeddings
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embeddings = model.encode(["Example sentence"])
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from model2vec import StaticModel
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# Load a pretrained Model2Vec model
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model = StaticModel.from_pretrained("minishlab/potion-multilingual-128M")
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# Compute text embeddings
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embeddings = model.encode(["Example sentence"])
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