Sentence Similarity
sentence-transformers
Safetensors
English
bert
legal
law
WA
feature-extraction
dense
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use CSI-lab/Washington-state-law-embedding-model-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use CSI-lab/Washington-state-law-embedding-model-Large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CSI-lab/Washington-state-law-embedding-model-Large") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from CSI-lab/Washington-state-law-embedding-model-Large: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/CSI-lab/Washington-state-law-embedding-model-Large/resolve/main/tokenizer.json
- Command line
-
hf download hf://CSI-lab/Washington-state-law-embedding-model-Large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/CSI-lab/Washington-state-law-embedding-model-Large/resolve/main/tokenizer.json
712 kB
File too large to display, you can check the raw version instead.