Sentence Similarity
sentence-transformers
Safetensors
embedding_gemma2
feature-extraction
dense
Generated from Trainer
dataset_size:346930
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use flyyinghui/embeddinggemma-2-lean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use flyyinghui/embeddinggemma-2-lean with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("flyyinghui/embeddinggemma-2-lean") sentences = [ "U card", "region", "clique", "tokens" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download data/declarations.jsonl from flyyinghui/embeddinggemma-2-lean: direct link, hf CLI and curl.
- Browser
- Download file 426 MB
-
https://huggingface.co/flyyinghui/embeddinggemma-2-lean/resolve/main/data/declarations.jsonl
- Command line
-
hf download hf://flyyinghui/embeddinggemma-2-lean/data/declarations.jsonl
-
curl -L -o declarations.jsonl https://huggingface.co/flyyinghui/embeddinggemma-2-lean/resolve/main/data/declarations.jsonl
426 MB
- Xet hash:
- 319e2b5658a5f8313d42d2612c9ea101f585a4ca4bad0291d8d853f296550879
- Size of remote file:
- 426 MB
- SHA256:
- 892c3ac7c19c88ba0e9804329d08a4e2fa981b464a4a7dfb5036676666334a6b
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