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") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - 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
Download model.safetensors from minishlab/M2V_multilingual_output: direct link, hf CLI and curl.
- Browser
- Download file 513 MB
-
https://huggingface.co/minishlab/M2V_multilingual_output/resolve/main/model.safetensors
- Command line
-
hf download hf://minishlab/M2V_multilingual_output/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/minishlab/M2V_multilingual_output/resolve/main/model.safetensors
513 MB
- Xet hash:
- 76a9cba5a7f7adc459efd229276ec9089a1b3e0b3126145285cd98809f1ff917
- Size of remote file:
- 513 MB
- SHA256:
- 11e6f20c2321711c429dbb3987591eb4692a24a0af4f8118863386345c0e9b8d
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