Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Bhuvana/setfit_2class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Bhuvana/setfit_2class with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Bhuvana/setfit_2class") 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 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Bhuvana/setfit_2class") model = AutoModel.from_pretrained("Bhuvana/setfit_2class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 568c82363f2c438c2a80d4188e3497c0703a7831e30023c5e5a2d2674d3b3453
- Size of remote file:
- 438 MB
- SHA256:
- 59b41fc70e945a172bb8c8bd99da30f65e4887685fe20e00582e74d29ac3d4c2
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