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_18w.jsonl from flyyinghui/embeddinggemma-2-lean: direct link, hf CLI and curl.
- Browser
- Download file 77.1 MB
-
https://huggingface.co/flyyinghui/embeddinggemma-2-lean/resolve/main/data/declarations_18w.jsonl
- Command line
-
hf download hf://flyyinghui/embeddinggemma-2-lean/data/declarations_18w.jsonl
-
curl -L -o declarations_18w.jsonl https://huggingface.co/flyyinghui/embeddinggemma-2-lean/resolve/main/data/declarations_18w.jsonl
77.1 MB
- Xet hash:
- 691ccd1d2250269e3e0c96d5378b88ba5448642fefaef23168d3d6097170733a
- Size of remote file:
- 77.1 MB
- SHA256:
- f57d304f87eb68907b6be5678eb0e5746fd867002de591dbc4ff7d36710c9687
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