Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Laxman/setfit-model-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Laxman/setfit-model-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Laxman/setfit-model-v2") 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 Laxman/setfit-model-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Laxman/setfit-model-v2") model = AutoModel.from_pretrained("Laxman/setfit-model-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca9a1c87410069bd0b8a4ad1b75397c17b464ed41c62a47b618b7bf5170223a3
- Size of remote file:
- 438 MB
- SHA256:
- de637d6c3344ab2c8f94785a50d53054ff59a47bc89483fa602b3ccb648db384
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