Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model10") 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/model10 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("novelcore/model10") model = AutoModel.from_pretrained("novelcore/model10") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05f60ce4ed982209319e7e0cb5c70d8d209573f653e6756c29c45210429fa0a2
- Size of remote file:
- 265 MB
- SHA256:
- 0a9815b5180fd4c1ec890b79049f4e48b9c3d99c7fec3a7f2f8915d4e3b7fdd8
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