ModernBERT Embed base Legal Matryoshka

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: google-bert/bert-base-uncased
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • json
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator with these parameters:
  json

  1{ 2 "truncate_dim": 768 3}

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator with these parameters:
  json

  1{ 2 "truncate_dim": 512 3}

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator with these parameters:
  json

  1{ 2 "truncate_dim": 256 3}

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator with these parameters:
  json

  1{ 2 "truncate_dim": 128 3}

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator with these parameters:
  json

  1{ 2 "truncate_dim": 64 3}

Training Details

Training Dataset

json

• Dataset: json
• Size: 1,580 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 7 tokens mean: 15.29 tokens max: 34 tokens	min: 31 tokens mean: 361.9 tokens max: 512 tokens

• Samples:

  anchor	positive
  By when does each Member State need to notify the Commission of the provisions of its law adopted pursuant to this Chapter?	1.Each Member State shall provide for one or more independent public authorities to be responsible for monitoring the application of this Regulation, in order to protect the fundamental rights and freedoms of natural persons in relation to processing and to facilitate the free flow of personal data within the Union (‘supervisory authority’).[object Object]2.Each supervisory authority shall contribute to the consistent application of this Regulation throughout the Union. For that purpose, the supervisory authorities shall cooperate with each other and the Commission in accordance with Chapter VII.[object Object]3.Where more than one supervisory authority is established in a Member State, that Member State shall designate the supervisory authority which is to represent those authorities in the Board and shall set out the mechanism to ensure compliance by the other authorities with the rules relating to the consistency mechanism referred to in Article 63[object Object]4.Each Member State shall notify to the Commission the provisi...
  How much was the defendant ordered to pay?	Court (Civil/Criminal):[object Object]Provisions:[object Object]Time of commission of the act:[object Object]Outcome (not guilty, guilty): ORDERS the defendant to pay the plaintiff the amount of two thousand four hundred thirty-four euros and eighty-three cents (€2,434.83) with legal interest from the service of the lawsuit.[object Object][object Object]Reasoning: Law 4537/2018 introduces mandatory provisions in favor of users, as according to Article 103, payment service providers are prohibited from deviating from the provisions to the detriment of payment service users, unless the possibility of deviation is expressly provided, and they can decide to offer only more favorable terms to payment service users. Under this law and its provisions, providers are only liable when there are unusual and unforeseen circumstances beyond the control of the party invoking them, and whose consequences could not have been avoided despite efforts to the contrary. However, operational risks and security risks of the system do not constitute unusual and unforeseen circu...
  On what date did the judge grant the motion?	1.A transfer of personal data to a third country or an international organisation may take place where the Commission has decided that the third country, a territory or one or more specified sectors within that third country, or the international organisation in question ensures an adequate level of protection. Such a transfer shall not require any specific authorisation.[object Object]2.When assessing the adequacy of the level of protection, the Commission shall, in particular, take account of the following elements: (a) the rule of law, respect for human rights and fundamental freedoms, relevant legislation, both general and sectoral, including concerning public security, defence, national security and criminal law and the access of public authorities to personal data, as well as the implementation of such legislation, data protection rules, professional rules and security measures, including rules for the onward transfer of personal data to another third country or international organisation whi...

• Loss: MatryoshkaLoss with these parameters:
  json

  1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• gradient_accumulation_steps: 2
• learning_rate: 2e-05
• num_train_epochs: 15
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• tf32: True
• load_best_model_at_end: True
• optim: adamw_torch_fused
• batch_sampler: no_duplicates

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.11.13
• Sentence Transformers: 4.1.0
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.8.1
• Datasets: 4.0.0
• Tokenizers: 0.21.2

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss