Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Core ML
ONNX
Safetensors
OpenVINO
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use novelcore/model15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model15 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model15") 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] - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from novelcore/model15: direct link, hf CLI and curl.
- Browser
- Download file 57 Bytes
-
https://huggingface.co/novelcore/model15/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://novelcore/model15/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/novelcore/model15/resolve/main/sentence_bert_config.json
57 Bytes
| { | |
| "max_seq_length": 512, | |
| "do_lower_case": false | |
| } |