Feature Extraction
Transformers
Safetensors
sentence-transformers
multilingual
embedding_gemma2
embedding
multimodal-embedding
multimodal
vision
audio
video
image-feature-extraction
audio-feature-extraction
video-feature-extraction
sentence-similarity
Instructions to use TechnoBaptist/embeddinggemma-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TechnoBaptist/embeddinggemma-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="TechnoBaptist/embeddinggemma-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("TechnoBaptist/embeddinggemma-2") model = AutoModel.from_pretrained("TechnoBaptist/embeddinggemma-2", device_map="auto") - sentence-transformers
How to use TechnoBaptist/embeddinggemma-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TechnoBaptist/embeddinggemma-2") 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 TechnoBaptist/embeddinggemma-2: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/TechnoBaptist/embeddinggemma-2/resolve/main/tokenizer.json
- Command line
-
hf download hf://TechnoBaptist/embeddinggemma-2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/TechnoBaptist/embeddinggemma-2/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- ff29dc00bacbe2db29a89eee9cd86b61be7936ed3d1a755ff69d1b259a18a7c7
- Size of remote file:
- 32.2 MB
- SHA256:
- 4d777ef5bdc1aa36227abdfb77c3e49e7b9c892d16e1b6bda41c393504828be4
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