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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6a978e23f19f7457280e3824d27d2f0763387c643bee51d722760045e90deb0
- Size of remote file:
- 1.42 GB
- SHA256:
- c20de690cbe7db43036643c675237ea3c92ae81de2089fd68324fa24caeb750e
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