aaa961/modernbert-embed-base-legal-no_MRL_symmetricMNRL_3sets

ModernBERT Embed Base Legal Fine-tuned

This is a sentence-transformers model finetuned from nomic-ai/modernbert-embed-base on the legal-rag-positives-synthetic dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• —Model Type: Sentence Transformer
• —Base model: nomic-ai/modernbert-embed-base <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
• —Maximum Sequence Length: 8192 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity
• —Training Dataset:
• —legal-rag-positives-synthetic
• —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)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("aaa961/modernbert-embed-base-legal-no_MRL_symmetricMNRL_3sets")
# Run inference
sentences = [
    'What sections of the document are referenced in the location Supplement 2, AR?',
    'the Polaris Solicitations as currently drafted do not comply with Section 3306(c)(3).  In its request \nto apply Section 3306(c)(3) to the Polaris Solicitations, GSA stated that \n \n \n  \nSupplement 2, AR at 2907–08.  Because GSA adopted an overly broad understanding of Section \n3306(c)(3)’s scope, GSA stated the Solicitations will include a “full range of order types,”',
    '“Based on this misunderstanding, the CIA attorney incorrectly cited some of the justifications for \nredacting the material to the DOJ attorney, who in turn shared that information with plaintiff.”  \nId. ¶ 9. \nE. \nProcedural History \nThe plaintiff filed the Complaints in each of these three actions on February 28, 2011,',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3908, 0.0520],
#         [0.3908, 1.0000, 0.0703],
#         [0.0520, 0.0703, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Information Retrieval

• —Datasets: ir_eval_test and ir_eval_eval
• —Evaluated with <code>InformationRetrievalEvaluator</code>

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Training Details

Training Dataset

legal-rag-positives-synthetic

• —Dataset: legal-rag-positives-synthetic at f11534a
• —Size: 5,175 training samples
• —Columns: <code>anchor</code> and <code>positive</code>
• —Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 16.61 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 96.79 tokens</li><li>max: 157 tokens</li></ul> |
• —Samples: | anchor | positive | |:--------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Where is the similar statement to the one about business judgment and scoring merit found?</code> | <code>is with each bidder itself, and its own business judgment in forming a team and what score it thinks <br>is enough to merit an award.”) (emphasis in original); VCH MJAR at 21–22 (same); Oral Ar. Tr. <br>at 10:5–7 (“[C]ompetition involves . . . some sort of tradeoff between offerors, some sort of <br>evaluation of how offerors are against one another, and that’s not the case here. The case here is</code> | | <code>Who do lawyers generally employ as assistants in their practice?</code> | <code>abide by the Rules of Professional Conduct. See rule 4-5.2(a). <br> <br>RULE 4-5.3. <br>RESPONSIBILITIES REGARDING NONLAWYER <br>ASSISTANTS <br>(a) – (c) [No Change] <br>Comment <br>Lawyers generally employ assistants in their practice, <br>including secretaries, investigators, law student interns, and <br>paraprofessionals such as paralegals and legal assistants. Such</code> | | <code>Which court case is cited with a page number of 1327?</code> | <code>30; VCH MJAR at 28–30 (same). <br>As noted, this Court applies the same interpretive rules to analyze both statutes and federal <br>regulations. See Boeing, 983 F.3d at 1327 (citing Mass. Mut. Life Ins. Co., 782 F.3d at 1365); see <br>also supra Discussion Section I. It is a “fundamental canon of statutory construction that the words</code> |
• —Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 32,
      "gather_across_devices": false,
      "directions": [
          "query_to_doc",
          "doc_to_query"
      ],
      "partition_mode": "per_direction",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }

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: adamwtorchfused
• —gradient_accumulation_steps: 16
• —bf16: True
• —tf32: True
• —eval_strategy: epoch
• —per_device_eval_batch_size: 16
• —load_best_model_at_end: True

All Hyperparameters

<details><summary>Click to expand</summary>

• —per_device_train_batch_size: 32
• —num_train_epochs: 4
• —max_steps: -1
• —learning_rate: 2e-05
• —lr_scheduler_type: cosine
• —lr_scheduler_kwargs: None
• —warmup_steps: 0.1
• —optim: adamwtorchfused
• —optim_args: None
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —optim_target_modules: None
• —gradient_accumulation_steps: 16
• —average_tokens_across_devices: True
• —max_grad_norm: 1.0
• —label_smoothing_factor: 0.0
• —bf16: True
• —fp16: False
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: True
• —gradient_checkpointing: False
• —gradient_checkpointing_kwargs: None
• —torch_compile: False
• —torch_compile_backend: None
• —torch_compile_mode: None
• —use_liger_kernel: False
• —liger_kernel_config: None
• —use_cache: False
• —neftune_noise_alpha: None
• —torch_empty_cache_steps: None
• —auto_find_batch_size: False
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —include_num_input_tokens_seen: no
• —log_level: passive
• —log_level_replica: warning
• —disable_tqdm: False
• —project: huggingface
• —trackio_space_id: trackio
• —eval_strategy: epoch
• —per_device_eval_batch_size: 16
• —prediction_loss_only: True
• —eval_on_start: False
• —eval_do_concat_batches: True
• —eval_use_gather_object: False
• —eval_accumulation_steps: None
• —include_for_metrics: []
• —batch_eval_metrics: False
• —save_only_model: False
• —save_on_each_node: False
• —enable_jit_checkpoint: False
• —push_to_hub: False
• —hub_private_repo: None
• —hub_model_id: None
• —hub_strategy: every_save
• —hub_always_push: False
• —hub_revision: None
• —load_best_model_at_end: True
• —ignore_data_skip: False
• —restore_callback_states_from_checkpoint: False
• —full_determinism: False
• —seed: 42
• —data_seed: None
• —use_cpu: False
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —dataloader_drop_last: False
• —dataloader_num_workers: 0
• —dataloader_pin_memory: True
• —dataloader_persistent_workers: False
• —dataloader_prefetch_factor: None
• —remove_unused_columns: True
• —label_names: None
• —train_sampling_strategy: random
• —length_column_name: length
• —ddp_find_unused_parameters: None
• —ddp_bucket_cap_mb: None
• —ddp_broadcast_buffers: False
• —ddp_backend: None
• —ddp_timeout: 1800
• —fsdp: []
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —deepspeed: None
• —debug: []
• —skip_memory_metrics: True
• —do_predict: False
• —resume_from_checkpoint: None
• —warmup_ratio: None
• —local_rank: -1
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

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

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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