Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use jamescalam/bert-stsb-gold with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jamescalam/bert-stsb-gold with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jamescalam/bert-stsb-gold") 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 jamescalam/bert-stsb-gold with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jamescalam/bert-stsb-gold") model = AutoModel.from_pretrained("jamescalam/bert-stsb-gold", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jamescalam/bert-stsb-gold: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jamescalam/bert-stsb-gold/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jamescalam/bert-stsb-gold/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jamescalam/bert-stsb-gold/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 786fa4d8799cec5038ee950056b9fa73e7af235203dc2d04b43b292859e97ff4
- Size of remote file:
- 438 MB
- SHA256:
- d39f2b7f7faba3138c6e33ce0e70fa4e9516c24320d90b0d1f5dc60383970015
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