Sentence Similarity
sentence-transformers
Safetensors
Turkish
embedding_gemma2
feature-extraction
embeddinggemma
embeddinggemma-2
turkish
retrieval
matryoshka
Eval Results (legacy)
Instructions to use thealper2/embeddinggemma-2-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thealper2/embeddinggemma-2-tr with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thealper2/embeddinggemma-2-tr") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from thealper2/embeddinggemma-2-tr: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/thealper2/embeddinggemma-2-tr/resolve/main/tokenizer.json
- Command line
-
hf download hf://thealper2/embeddinggemma-2-tr/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/thealper2/embeddinggemma-2-tr/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- caf34544b54e06b075afb3dc1a0a5314713cdde029fe7355d74039b525b6711c
- Size of remote file:
- 32.2 MB
- SHA256:
- 7c33896a1e8751e4a5634cd20fcc0a01aaae1a773b1f8150d867b1034f3dff95
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