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:
# 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
- Xet hash:
- 46fd5fe9b9fac1beb3ebdae3eaf892b37ab414ef8c9d5312545ed5575731f5a5
- Size of remote file:
- 438 MB
- SHA256:
- 30d78fa33795c0fb1c53d0b65c70866e44f6965d4d3fb2a07833a7a91e92a9f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.