Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use kanixwang/eth-setfit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kanixwang/eth-setfit-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kanixwang/eth-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 kanixwang/eth-setfit-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kanixwang/eth-setfit-model") model = AutoModel.from_pretrained("kanixwang/eth-setfit-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a772de8f3c67175c02f00d9c5b43878a006ab53ffd34b5fd7174afa9d3d995e0
- Size of remote file:
- 438 MB
- SHA256:
- 4289bc0c848b1f22d3760f6c05915bedb76f93938102b842af0bbd3116ae240b
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