Sentence Similarity
sentence-transformers
PyTorch
JAX
ONNX
Safetensors
OpenVINO
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/nli-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/nli-roberta-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/nli-roberta-large") 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/nli-roberta-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/nli-roberta-large") model = AutoModel.from_pretrained("sentence-transformers/nli-roberta-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55c717b415f8531e591bae772e148e2683d2bcbb5dcaf6388d0638a6d734e2dd
- Size of remote file:
- 1.42 GB
- SHA256:
- 889a95a777917d5d78c590c00b7da89c0589b03f0bc988651979ae8a4a18207b
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