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 tokenizer.json from flyyinghui/embeddinggemma-2-lean: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/flyyinghui/embeddinggemma-2-lean/resolve/main/tokenizer.json
- Command line
-
hf download hf://flyyinghui/embeddinggemma-2-lean/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/flyyinghui/embeddinggemma-2-lean/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- 7c3b4db32b66c61afda8712f3d4a806b4393d31e7c9a007cb33eec9795b8aaa7
- Size of remote file:
- 32.2 MB
- SHA256:
- 22c064aaa468855d6211fe5c2ec8fdec595c97c55eb2ffd3b9fdf71a93b16292
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