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:
- f7c6f999f7bc15540d61efd45eca61fe4bf849866b992db45d7520e0892fd127
- Size of remote file:
- 438 MB
- SHA256:
- d9000a622a816ed4f7e8cc359d87789d30805eb1908c582c82575f52cdb48681
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