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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 667be1e620458626fc270855a9271ba570547e19f6f2d59a58c9716ef873ff8d
- Size of remote file:
- 438 MB
- SHA256:
- dd69134c0511ee516740ee73e1a0262c6749d541e5870940036a92d615ad0a40
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