Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Bhuvana/test-setfit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Bhuvana/test-setfit-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Bhuvana/test-setfit-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/test-setfit-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Bhuvana/test-setfit-model") model = AutoModel.from_pretrained("Bhuvana/test-setfit-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1610a2b4a9a0f39f968429179690791be42a582167e96a5511a29ea7ad41b691
- Size of remote file:
- 438 MB
- SHA256:
- 7743b60a2795f08fbf426c638545e387290d3c93241140b6f74ba51267c0bcab
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