Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use gubartz/dense_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gubartz/dense_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gubartz/dense_model") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use gubartz/dense_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gubartz/dense_model") model = AutoModel.from_pretrained("gubartz/dense_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ecf3d757976dcfae86ab1022039ce61ccc03ee4d8abeabcaa8b9c028dbbe01bf
- Size of remote file:
- 440 MB
- SHA256:
- a60f60259cb922bd347959de2fc50186271b8e5b5baf2a238c6c14fb0a9cbe8f
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