Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model14 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model14") 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/model14 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("novelcore/model14") model = AutoModel.from_pretrained("novelcore/model14") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 460b95a6e455d4537b5ccc55932af63932d3b88ba47b6156b18649f9e9d383f9
- Size of remote file:
- 134 MB
- SHA256:
- b17442e1b4a5db687084be01f47d35f683f35ffadbfe26ca42622fec51d013a8
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