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
Upload embeddinggemma-2-lean sentence-transformers model (finetuned on openai_math 377K dep pairs)
46607a6 verified Download training_args.bin from flyyinghui/embeddinggemma-2-lean: direct link, hf CLI and curl.
- Browser
- Download file 5.52 kB
-
https://huggingface.co/flyyinghui/embeddinggemma-2-lean/resolve/main/training_args.bin
- Command line
-
hf download hf://flyyinghui/embeddinggemma-2-lean/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/flyyinghui/embeddinggemma-2-lean/resolve/main/training_args.bin
5.52 kB
- Xet hash:
- c3a029253734561a08e5b59cbc7ee51236f3014ee3a0a488e7a74aeeb84b780a
- Size of remote file:
- 5.52 kB
- SHA256:
- eb0da9ea3562948ab4f20f8affb1b3d5bf1674bcbd6cc0f9c03bfb612499c413
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