Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Bhuvana/setfit_2class_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Bhuvana/setfit_2class_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Bhuvana/setfit_2class_model") 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 Bhuvana/setfit_2class_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Bhuvana/setfit_2class_model") model = AutoModel.from_pretrained("Bhuvana/setfit_2class_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 408e43de6d76133cd798e4aa0920d5c096d264269f5357918d4a6632e5a79ece
- Size of remote file:
- 6.94 kB
- SHA256:
- 89dbaf6eacd7cb9d59cfcef7e5cb076933ba52ad3575c6bcd57c4b5a28184900
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