Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
OpenVINO
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/roberta-base-nli-stsb-mean-tokens with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/roberta-base-nli-stsb-mean-tokens with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/roberta-base-nli-stsb-mean-tokens") 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/roberta-base-nli-stsb-mean-tokens with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/roberta-base-nli-stsb-mean-tokens") model = AutoModel.from_pretrained("sentence-transformers/roberta-base-nli-stsb-mean-tokens", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download added_tokens.json from sentence-transformers/roberta-base-nli-stsb-mean-tokens: direct link, hf CLI and curl.
- Browser
- Download file 2 Bytes
-
https://huggingface.co/sentence-transformers/roberta-base-nli-stsb-mean-tokens/resolve/82fefee86bb9baefb6c9b44f077bc29f823745ec/added_tokens.json
- Command line
-
hf download hf://sentence-transformers/roberta-base-nli-stsb-mean-tokens@82fefee86bb9baefb6c9b44f077bc29f823745ec/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/sentence-transformers/roberta-base-nli-stsb-mean-tokens/resolve/82fefee86bb9baefb6c9b44f077bc29f823745ec/added_tokens.json
2 Bytes
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