Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use Ketan3101/sentensense with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ketan3101/sentensense with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ketan3101/sentensense") 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 Ketan3101/sentensense with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Ketan3101/sentensense") model = AutoModel.from_pretrained("Ketan3101/sentensense") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4fdc52b097429852e2b7aa01bcc17491d84a64d4998bf74918516e95cd134b3
- Size of remote file:
- 329 MB
- SHA256:
- b0d9931155416f63a5175f1f97aab1151fbda7f588ae8537a27ef3801efd3456
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