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
| { | |
| "architectures": [ | |
| "ModernBertModel" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 50281, | |
| "classifier_activation": "gelu", | |
| "classifier_bias": false, | |
| "classifier_dropout": 0.0, | |
| "classifier_pooling": "mean", | |
| "cls_token_id": 50281, | |
| "decoder_bias": true, | |
| "deterministic_flash_attn": false, | |
| "dtype": "float32", | |
| "embedding_dropout": 0.0, | |
| "eos_token_id": 50282, | |
| "global_attn_every_n_layers": 3, | |
| "gradient_checkpointing": false, | |
| "hidden_activation": "gelu", | |
| "hidden_size": 768, | |
| "initializer_cutoff_factor": 2.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1152, | |
| "layer_norm_eps": 1e-05, | |
| "layer_types": [ | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention" | |
| ], | |
| "local_attention": 128, | |
| "max_position_embeddings": 8192, | |
| "mlp_bias": false, | |
| "mlp_dropout": 0.0, | |
| "model_type": "modernbert", | |
| "norm_bias": false, | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 22, | |
| "pad_token_id": 50283, | |
| "position_embedding_type": "absolute", | |
| "rope_parameters": { | |
| "full_attention": { | |
| "rope_theta": 160000.0, | |
| "rope_type": "default" | |
| }, | |
| "sliding_attention": { | |
| "rope_theta": 10000.0, | |
| "rope_type": "default" | |
| } | |
| }, | |
| "sep_token_id": 50282, | |
| "sparse_pred_ignore_index": -100, | |
| "sparse_prediction": false, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.3.0", | |
| "vocab_size": 50368 | |
| } | |