Instructions to use minishlab/M2V_multilingual_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use minishlab/M2V_multilingual_output with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/M2V_multilingual_output") - sentence-transformers
How to use minishlab/M2V_multilingual_output with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minishlab/M2V_multilingual_output") 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:
- c9222b99ad8a2986be9c8a255b1ccb7f63bdbe6d8506b39b56c4c6f43cf3222d
- Size of remote file:
- 13.6 MB
- SHA256:
- 9e6ba9ec0fd266d352526410db93105a5a38419ace8abc878bf98e88698cbe1c
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