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:
- c9c272f0f3ed6f44312ef627202c595e4b1574943dad5c552fa1cea1d2c9d316
- Size of remote file:
- 265 MB
- SHA256:
- 3083803ab54614d7c0627b19aa2d7070cd4c49116dc8efd6fae3016526a10015
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