Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model8") 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/model8 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("novelcore/model8") model = AutoModelForMultimodalLM.from_pretrained("novelcore/model8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 17d582e3b2a4bf43b2b9939e74a4e103b561d7ab44e3bc5a13b5ea6f2ece3694
- Size of remote file:
- 436 MB
- SHA256:
- 813b36389099ed4b37c52a9058ee0b9fda10011cc22ad80cada74e2cf0de21c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.