Sentence Similarity
sentence-transformers
PyTorch
Rust
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model2") 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/model2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("novelcore/model2") model = AutoModel.from_pretrained("novelcore/model2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fdc03fb5634897694c52eae5e441d92a67b399c7ef013a836f42495c113d804e
- Size of remote file:
- 133 MB
- SHA256:
- 84c56795d395593cbee215e2d635a8f0ad3199ae99f99299c44cf1eaecff3ad4
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