Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
distilbert
fill-mask
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model20 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model20") 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/model20 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("novelcore/model20") model = AutoModelForMaskedLM.from_pretrained("novelcore/model20", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 060c0b40f45b2524d43ef53e06adf6d05c6ac2cc9750a671652a977ac5d5d4d5
- Size of remote file:
- 266 MB
- SHA256:
- 11cfe071068b6857e9323f73883079e9d720b6672c6092d894c29ed4bdfa353d
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