Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use menbom/test-setfit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use menbom/test-setfit-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("menbom/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 menbom/test-setfit-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("menbom/test-setfit-model") model = AutoModel.from_pretrained("menbom/test-setfit-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ffe388f9b31ae1399884468eb60751c1b2f08e0050e24eaadf8f2060f6d1eab
- Size of remote file:
- 438 MB
- SHA256:
- 6de8b3b1cb4b48100d5ff930f3964743a4003ed87364adca52f38622d136f065
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