Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use dinait/test-setfit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dinait/test-setfit-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dinait/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 dinait/test-setfit-model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dinait/test-setfit-model") model = AutoModel.from_pretrained("dinait/test-setfit-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_head.pkl from dinait/test-setfit-model: direct link, hf CLI and curl.
- Browser
- Download file 16.7 kB
-
https://huggingface.co/dinait/test-setfit-model/resolve/main/model_head.pkl
- Command line
-
hf download hf://dinait/test-setfit-model/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/dinait/test-setfit-model/resolve/main/model_head.pkl
16.7 kB
- Xet hash:
- 432635bbebd62abf538daf1a1e8d36141b275864e13928507c3f2186130d5e7a
- Size of remote file:
- 16.7 kB
- SHA256:
- eb2ff2339ca4dd509e47b0c88c7c3a6dc3b5b5149c20b88a4771e0ea6082c14e
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