Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
mpnet
fill-mask
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model9 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model9") 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/model9 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("novelcore/model9") model = AutoModelForMaskedLM.from_pretrained("novelcore/model9") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 33d21d295328942a39647767a45fd53aa1faf6853a8156ebd078ea745190eed0
- Size of remote file:
- 436 MB
- SHA256:
- 33077af1c77a1bc6e99867b72b0a253a1a524e446b400e51126197aaa8c4b64f
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