Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:11644
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use aaa961/modernbert-embed-base-legal_no_MRL_reverse_dataset_early_stopping with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aaa961/modernbert-embed-base-legal_no_MRL_reverse_dataset_early_stopping with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aaa961/modernbert-embed-base-legal_no_MRL_reverse_dataset_early_stopping") sentences = [ "What section of the U.S. Code is cited in relation to Exemption 2?", "forma inmediata de la utilización de cualquiera y todo material en el \nque se utilizara la imagen de la parte apelada. En adición, le \ncondenó solidariamente al pago de $20,000.00 por la utilización no \n \n \n \nKLAN202300916 \n \n6\nautorizada de la imagen del señor Friger Salgueiro y $4,000.00 por \nhonorarios de abogado. \nEn desacuerdo, el 20 de septiembre de 2023, la parte apelante", "How does the invocation of the attorney-client privilege by the CIA affect summary judgment?", "Decl. Ex. K pt. 2, at 1, 8–14, 16–18, 22, 27, No. 11-445, ECF No. 29-3. Exemption 2 applies to \nmatters that “related solely to the internal personnel rules and practices of an agency.” 5 U.S.C. \n§ 552(b)(2). The CIA states in its declaration that all thirteen documents withheld under" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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| "type": "sentence_transformers.models.Pooling" | |
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