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 tokenizer.json from gaggi009/sbert-su-docs: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/gaggi009/sbert-su-docs/resolve/main/tokenizer.json
- Command line
-
hf download hf://gaggi009/sbert-su-docs/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/gaggi009/sbert-su-docs/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- d4efbe00e16f78f5b7cee303d34fe104f1662805f44225c75a3a7010516b5ca0
- Size of remote file:
- 17.1 MB
- SHA256:
- 84a8f3498ffb9411167dc02842967c4adb9479d41a2fec95dc63d80a1e833ae5
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