Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/stsb-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/stsb-bert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/stsb-bert-base") 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 sentence-transformers/stsb-bert-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/stsb-bert-base") model = AutoModel.from_pretrained("sentence-transformers/stsb-bert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/model_O2.onnx from sentence-transformers/stsb-bert-base: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/sentence-transformers/stsb-bert-base/resolve/main/onnx/model_O2.onnx
- Command line
-
hf download hf://sentence-transformers/stsb-bert-base/onnx/model_O2.onnx
-
curl -L -o model_O2.onnx https://huggingface.co/sentence-transformers/stsb-bert-base/resolve/main/onnx/model_O2.onnx
436 MB
- Xet hash:
- 88b94652a31bf18aefd7e7846c8c2293c7ea91cc20fcb1d861a50ad7a158ba21
- Size of remote file:
- 436 MB
- SHA256:
- 27cf5ad36ce2cd0e40889e46dcf1c84b872ea67e875a3b8b202c685e3f32b3cc
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