Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model11 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model11") 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/model11 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("novelcore/model11") model = AutoModel.from_pretrained("novelcore/model11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/model_O2.onnx from novelcore/model11: direct link, hf CLI and curl.
- Browser
- Download file 69 MB
-
https://huggingface.co/novelcore/model11/resolve/main/onnx/model_O2.onnx
- Command line
-
hf download hf://novelcore/model11/onnx/model_O2.onnx
-
curl -L -o model_O2.onnx https://huggingface.co/novelcore/model11/resolve/main/onnx/model_O2.onnx
69 MB
- Xet hash:
- 5b7b382a4eede01e46a87502f9dcb8c1fd24d6ade0da082c501adab9552d5ae9
- Size of remote file:
- 69 MB
- SHA256:
- 2312521d80b6996cbf2fad7dc1984b1384036a6e772e2ba9ef1f9d231fa6c0c9
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