Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model18 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model18") 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/model18 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("novelcore/model18") model = AutoModelForMultimodalLM.from_pretrained("novelcore/model18") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba9a55954344aea344b2384ea1c24fc8d3674eefdbcf66c26e08347c683d4c9e
- Size of remote file:
- 436 MB
- SHA256:
- b26070b85e939c1f20134c7b34d9b96b6aba35a248f5def3dbd5fd9f37cdf202
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