Model2Vec
Safetensors
sentence-transformers
multilingual
English
Serbian
embeddings
static-embeddings
Instructions to use Stopwolf/embedic-m2v-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use Stopwolf/embedic-m2v-large with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("Stopwolf/embedic-m2v-large") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - sentence-transformers
How to use Stopwolf/embedic-m2v-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Stopwolf/embedic-m2v-large") 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 Stopwolf/embedic-m2v-large: direct link, hf CLI and curl.
- Browser
- Download file 9.09 MB
-
https://huggingface.co/Stopwolf/embedic-m2v-large/resolve/main/tokenizer.json
- Command line
-
hf download hf://Stopwolf/embedic-m2v-large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Stopwolf/embedic-m2v-large/resolve/main/tokenizer.json
9.09 MB
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