Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:43
loss:MultipleNegativesRankingLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use gaggi009/sbert-su-docs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gaggi009/sbert-su-docs with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gaggi009/sbert-su-docs", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gaggi009/sbert-su-docs: direct link, hf CLI and curl.
- Browser
- Download file 5.56 kB
-
https://huggingface.co/gaggi009/sbert-su-docs/resolve/main/training_args.bin
- Command line
-
hf download hf://gaggi009/sbert-su-docs/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gaggi009/sbert-su-docs/resolve/main/training_args.bin
5.56 kB
- Xet hash:
- ce4bf919d7cdac82047918b16fa3c7f3ddb3b162bfb6c399c5dfd50df8153064
- Size of remote file:
- 5.56 kB
- SHA256:
- 109a5afacbd9ff6a029759ec12ce643a1da5d2096055485437af14b825774471
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