Instructions to use tokiers/M2V_multilingual_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use tokiers/M2V_multilingual_output with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("tokiers/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 tokiers/M2V_multilingual_output with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tokiers/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 tokenizer.json from tokiers/M2V_multilingual_output: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/tokiers/M2V_multilingual_output/resolve/main/tokenizer.json
- Command line
-
hf download hf://tokiers/M2V_multilingual_output/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/tokiers/M2V_multilingual_output/resolve/main/tokenizer.json
13.6 MB
- Xet hash:
- e1e03a534a636ac70d6e904535f6c4afa317db81f6e0606b9d5b4ad5bfe4829f
- Size of remote file:
- 13.6 MB
- SHA256:
- 9e6ba9ec0fd266d352526410db93105a5a38419ace8abc878bf98e88698cbe1c
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