Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model5") 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/model5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("novelcore/model5") model = AutoModel.from_pretrained("novelcore/model5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa665699ae9101ccd041c1e23200e4ef046f8fef6a57fb2e207e9e5e68789403
- Size of remote file:
- 90.9 MB
- SHA256:
- 4ce670108f3abe4a1513aa973c696e273397db899a983deecdd86a987f9a63dc
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