Feature Extraction
Transformers
Safetensors
sentence-transformers
English
embedding_gemma2
embedding
legal
law
WA
multimodal-embedding
multimodal
sentence-similarity
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use CSI-lab/US-State-Law-EmbeddingGemma-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CSI-lab/US-State-Law-EmbeddingGemma-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="CSI-lab/US-State-Law-EmbeddingGemma-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("CSI-lab/US-State-Law-EmbeddingGemma-2") model = AutoModel.from_pretrained("CSI-lab/US-State-Law-EmbeddingGemma-2", device_map="auto") - sentence-transformers
How to use CSI-lab/US-State-Law-EmbeddingGemma-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CSI-lab/US-State-Law-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 CSI-lab/US-State-Law-EmbeddingGemma-2: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/CSI-lab/US-State-Law-EmbeddingGemma-2/resolve/main/tokenizer.json
- Command line
-
hf download hf://CSI-lab/US-State-Law-EmbeddingGemma-2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/CSI-lab/US-State-Law-EmbeddingGemma-2/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- 5704eb9ce94e376c0aba4999da5557c00748ff1bb69093c0e251971633b4b46f
- Size of remote file:
- 32.2 MB
- SHA256:
- d45a00f6b0fb155a84f449a2499bf03165d8350a3ede590c7401a15e77d362d2
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