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.model from CSI-lab/US-State-Law-EmbeddingGemma-2: direct link, hf CLI and curl.
- Browser
- Download file 4.69 MB
-
https://huggingface.co/CSI-lab/US-State-Law-EmbeddingGemma-2/resolve/main/tokenizer.model
- Command line
-
hf download hf://CSI-lab/US-State-Law-EmbeddingGemma-2/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/CSI-lab/US-State-Law-EmbeddingGemma-2/resolve/main/tokenizer.model
4.69 MB
- Xet hash:
- 4eb043187a5f8683c446d6cad7e1904e93c694559e4dd27fafeb32f78ec9a960
- Size of remote file:
- 4.69 MB
- SHA256:
- e594c8a90eb08d8bda498ff4747977dc827ae0c3c56b5c0d41a605a22d02ef03
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.