Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use embedingHF/Sentence_Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use embedingHF/Sentence_Transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("embedingHF/Sentence_Transformer") 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 embedingHF/Sentence_Transformer with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("embedingHF/Sentence_Transformer") model = AutoModel.from_pretrained("embedingHF/Sentence_Transformer") - Notebooks
- Google Colab
- Kaggle
File size: 133 Bytes
e83604e | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:675124690d56e46f07b91be24721b00328dcf35c9962067d94d2894dc6970402
size 90864176
|