Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:5822
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use AdamLucek/ModernBERT-embed-base-legal-MRL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AdamLucek/ModernBERT-embed-base-legal-MRL with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AdamLucek/ModernBERT-embed-base-legal-MRL") sentences = [ "this information about the two documents withheld in part under the deliberative-process \n128 \n \nprivilege in No. 11-444, see First Lutz Decl. Ex. DD at 17, 141, but the descriptions of the \ndecisionmaking authority are generic, stating that the withheld information is a “recommendation \nfrom the [FOIA] analyst to his/her supervisor,” id. at 17, and a “recommendation from the", "What did the plaintiff assert about the CIA's inaccurate representations?", "What type of document is mentioned as an exhibit in conjunction with the withheld documents?", "¿Qué ámbito jurisdiccional es mencionado en el contexto de derechos sobre la propia imagen?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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