Instructions to use Saminx22/text_embedding_model_14M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Saminx22/text_embedding_model_14M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Saminx22/text_embedding_model_14M") 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 embedding_model_weights.pth from Saminx22/text_embedding_model_14M: direct link, hf CLI and curl.
- Browser
- Download file 55.8 MB
-
https://huggingface.co/Saminx22/text_embedding_model_14M/resolve/main/embedding_model_weights.pth
- Command line
-
hf download hf://Saminx22/text_embedding_model_14M/embedding_model_weights.pth
-
curl -L -o embedding_model_weights.pth https://huggingface.co/Saminx22/text_embedding_model_14M/resolve/main/embedding_model_weights.pth
55.8 MB
- Xet hash:
- 82b033889da5c61607e201c624f682cb81688679185d46850ac3fca9f05fd233
- Size of remote file:
- 55.8 MB
- SHA256:
- fb2fd0821e51a8db6b768180212015f9e2ea88bfaffe0fe048604245fc251672
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