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:
- e3871b983b98e5a2b071419b886f08ecc0c791fb469ece62ece2e17d579ca6e0
- Size of remote file:
- 90.9 MB
- SHA256:
- 5d716de760acbdc09e79a11e718c5606e0812b6aeb76c6664cba876d174e3ecd
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