Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model4") 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/model4 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("novelcore/model4") model = AutoModel.from_pretrained("novelcore/model4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 607e328b6a7d66a9d331767e0f2890ef179ef27eabcd2ddbbb3c2d8ecb1fa8af
- Size of remote file:
- 91 MB
- SHA256:
- d356e3a2867336eb0c469be0151e1edf79666e6399dd050a4c382e3c407432a2
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