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:
- 45597850ec079c09d3f95f1858946c8fa9d1cf2b9957b516c48bcceccea8d226
- Size of remote file:
- 512 MB
- SHA256:
- 14b5eb39cb4ce5666da8ad1f3dc6be4346e9b2d601c073302fa0a31bf7943397
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