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 modules.json from flyyinghui/embeddinggemma-2-lean: direct link, hf CLI and curl.
- Browser
- Download file 432 Bytes
-
https://huggingface.co/flyyinghui/embeddinggemma-2-lean/resolve/main/modules.json
- Command line
-
hf download hf://flyyinghui/embeddinggemma-2-lean/modules.json
-
curl -L -o modules.json https://huggingface.co/flyyinghui/embeddinggemma-2-lean/resolve/main/modules.json
432 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.base.modules.normalize.Normalize" | |
| } | |
| ] |