Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use jpostma/s-RobBERT-STSb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jpostma/s-RobBERT-STSb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jpostma/s-RobBERT-STSb") 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 jpostma/s-RobBERT-STSb with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jpostma/s-RobBERT-STSb") model = AutoModel.from_pretrained("jpostma/s-RobBERT-STSb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jpostma/s-RobBERT-STSb: direct link, hf CLI and curl.
- Browser
- Download file 467 MB
-
https://huggingface.co/jpostma/s-RobBERT-STSb/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jpostma/s-RobBERT-STSb/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jpostma/s-RobBERT-STSb/resolve/main/pytorch_model.bin
467 MB
- Xet hash:
- a572c4b61f8cdd03c729da0f6b0c9d83932314a4ec22e905a09f1e656ae23159
- Size of remote file:
- 467 MB
- SHA256:
- 4697f1228a4cd801b3231c6df9bf09a62d93b541472f5763baa16318216a0a0c
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