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 2_Normalize/config.json from flyyinghui/embeddinggemma-2-lean: direct link, hf CLI and curl.
- Browser
- Download file 100 Bytes
-
https://huggingface.co/flyyinghui/embeddinggemma-2-lean/resolve/main/2_Normalize/config.json
- Command line
-
hf download hf://flyyinghui/embeddinggemma-2-lean/2_Normalize/config.json
-
curl -L -o config.json https://huggingface.co/flyyinghui/embeddinggemma-2-lean/resolve/main/2_Normalize/config.json
100 Bytes
| { | |
| "module_input_name": "sentence_embedding", | |
| "module_output_name": "sentence_embedding" | |
| } |