Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model19 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model19 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model19") 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/model19 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("novelcore/model19") model = AutoModelForMultimodalLM.from_pretrained("novelcore/model19") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d5137a15b48df9a714715a5c3cf006194939bdc590ca2aca182ec8f9ed017bd
- Size of remote file:
- 436 MB
- SHA256:
- 7c3231e70e28a1a2232e0c026fc85368465b2d62e9116f8737b53fdd7bd1f275
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