Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/nli-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/nli-roberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/nli-roberta-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/nli-roberta-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/nli-roberta-base") model = AutoModel.from_pretrained("sentence-transformers/nli-roberta-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7daefe62c24146b7fd6481cb52c70ae961d32475d397bb4ba4311dfb86f6f96
- Size of remote file:
- 499 MB
- SHA256:
- e967212c6d2185195ac0fb81c47400446ca2b04fe10e7077ab520912b660f543
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