Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model3") 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/model3 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("novelcore/model3") model = AutoModel.from_pretrained("novelcore/model3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64d340a1cd5824ca75b2f7171f3f7bf5cb0344868766fd15a6a081a0e642a78a
- Size of remote file:
- 90.3 MB
- SHA256:
- d99151ccdfb700fb278610e060f3debf615b59da275ba5784385d49c8b8e8e9c
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