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
File size: 313 Bytes
f92ee8b | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 1024,
"pooling_mode_cls_token": true,
"pooling_mode_mean_tokens": false,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false,
"pooling_mode_weightedmean_tokens": false,
"pooling_mode_lasttoken": false,
"include_prompt": true
} |