Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use scaperex/SetFit-all-data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use scaperex/SetFit-all-data with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("scaperex/SetFit-all-data") 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 scaperex/SetFit-all-data with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("scaperex/SetFit-all-data") model = AutoModel.from_pretrained("scaperex/SetFit-all-data") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7bc987935ca173a7825c5619d346c10cad92f418bff9e077fc9f3285738edd19
- Size of remote file:
- 19.2 kB
- SHA256:
- 28f7087789dc3756b44dbab790673f96feb54ef56fd381509a8383ff01e97e12
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