Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
mpnet
fill-mask
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model6 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model6") 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/model6 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("novelcore/model6") model = AutoModelForMaskedLM.from_pretrained("novelcore/model6") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3cd19d06709451b5f1b9976d00e35dbfe20fdb338b455e1f4893d49361ea86f1
- Size of remote file:
- 436 MB
- SHA256:
- 74187b16d9c946fea252e120cfd7a12c5779d8b8b86838a2e4c56573c47941bd
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