Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model10") 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 novelcore/model10 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("novelcore/model10") model = AutoModel.from_pretrained("novelcore/model10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a9a18c4c09649beed9f72feaa5f04dc7f1ee6a424df918edfe4f10b046a9882
- Size of remote file:
- 266 MB
- SHA256:
- e2f09f497645cba83892f6d05f41fd65bd4cef5a99c18dd42ecbc764d24aefa2
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