ModernBERT Embed base Legal Matryoshka

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: nomic-ai/modernbert-embed-base
• Maximum Sequence Length: 8192 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': 8192, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (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})
  (2): Normalize()
)

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: 5,822 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 33 tokens mean: 97.83 tokens max: 160 tokens	min: 8 tokens mean: 16.69 tokens max: 38 tokens

• Samples:

  positive	anchor
  the IRGs. Id. at 248 & n.15. It did not matter that the NIMH “may be greatly influenced” by an [object Object]IRG’s “expert view.” Id. at 248. Given the functions that IRGs were “empowered by law to [object Object]perform,” they did not wield “substantial independent authority.” Id. at 247–48. [object Object] [object Object]Two months after Washington Research Project, Congress enacted the 1974 amendment	What did Congress enact two months after Washington Research Project?
  GSA’s interpretation of 13 C.F.R. § 125.9(b)(3)(i) harms protégés has broad implications. If [object Object]exclusion from bidding on the SB Solicitation indeed harms either protégé member of SHS or [object Object]VCH, perhaps this suggests the mentor-protégé relationships should not have been approved in the [object Object]first instance. See 13 C.F.R. § 125.9(b)(3) (“In order for SBA to agree to allow a mentor to have	Which two protégés could be harmed by exclusion from bidding on the SB Solicitation?
  Black’s Law Dictionary 742 (9th ed. 2009) (defining “function” as “[a]ctivity that is appropriate [object Object]to a particular business or profession”); Webster’s Third New Int’l Dictionary 920 (1981) [object Object](defining “function” as “the action for which a person or thing is specially fitted, used, or [object Object]responsible or for which a thing exists”).	What year was the 9th edition of Black’s Law Dictionary published?

• 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

• per_device_train_batch_size: 32
• num_train_epochs: 4
• learning_rate: 2e-05
• lr_scheduler_type: cosine
• warmup_steps: 0.1
• optim: adamw_torch_fused
• gradient_accumulation_steps: 16
• bf16: True
• tf32: True
• eval_strategy: epoch
• per_device_eval_batch_size: 16
• load_best_model_at_end: True
• batch_sampler: no_duplicates

All Hyperparameters

Training Logs

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.11
• Sentence Transformers: 5.3.0
• Transformers: 5.3.0
• PyTorch: 2.5.1+cu121
• Accelerate: 1.13.0
• Datasets: 4.8.2
• Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss