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modernbert-embed-base-legal-matryoshka-corrected_train_set_double_training_set_2026_03_09

Name: modernbert-embed-base-legal-matryoshka-corrected_train_set_double_training_set_2026_03_09
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ModernBERT Embed base Legal Matryoshka

This is a sentence-transformers model finetuned from nomic-ai/modernbert-embed-base. 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
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("aaa961/modernbert-embed-base-legal-matryoshka-corrected_train_set_double_training_set_2026_03_09")
# Run inference
sentences = [
    'What authority did the Court believe the Board exercised?',
    'court opined that the Board exercised “substantial independent authority” and thus was also a \nFOIA “agency” under Soucie’s functional test.  Id. at 584–85. \nThis Court’s previous opinion followed Energy Research’s analytical steps.  As with the \nBoard, Congress made the Commission an “establishment in the executive branch,” one of the',
    'On what date were the corporate filings with the Delaware Department of State mentioned?',
]
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, 0.5720, 0.0843],
#         [0.5720, 1.0000, 0.0574],
#         [0.0843, 0.0574, 1.0000]])

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 768 3}

Metric	Value
cosine_accuracy@1	0.6213
cosine_accuracy@3	0.6723
cosine_accuracy@5	0.7372
cosine_accuracy@10	0.7929
cosine_precision@1	0.6213
cosine_precision@3	0.5914
cosine_precision@5	0.4451
cosine_precision@10	0.2516
cosine_recall@1	0.2179
cosine_recall@3	0.5733
cosine_recall@5	0.6967
cosine_recall@10	0.7844
cosine_ndcg@10	0.7106
cosine_mrr@10	0.6624
cosine_map@100	0.6971

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 512 3}

Metric	Value
cosine_accuracy@1	0.6043
cosine_accuracy@3	0.66
cosine_accuracy@5	0.7264
cosine_accuracy@10	0.7821
cosine_precision@1	0.6043
cosine_precision@3	0.5781
cosine_precision@5	0.4362
cosine_precision@10	0.2467
cosine_recall@1	0.2116
cosine_recall@3	0.5623
cosine_recall@5	0.6852
cosine_recall@10	0.7701
cosine_ndcg@10	0.6959
cosine_mrr@10	0.6475
cosine_map@100	0.6831

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 256 3}

Metric	Value
cosine_accuracy@1	0.592
cosine_accuracy@3	0.6476
cosine_accuracy@5	0.7002
cosine_accuracy@10	0.762
cosine_precision@1	0.592
cosine_precision@3	0.5667
cosine_precision@5	0.4235
cosine_precision@10	0.2416
cosine_recall@1	0.2078
cosine_recall@3	0.5522
cosine_recall@5	0.6649
cosine_recall@10	0.7508
cosine_ndcg@10	0.6804
cosine_mrr@10	0.6336
cosine_map@100	0.669

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 128 3}

Metric	Value
cosine_accuracy@1	0.5209
cosine_accuracy@3	0.5688
cosine_accuracy@5	0.6383
cosine_accuracy@10	0.6909
cosine_precision@1	0.5209
cosine_precision@3	0.5013
cosine_precision@5	0.3821
cosine_precision@10	0.2189
cosine_recall@1	0.1811
cosine_recall@3	0.4851
cosine_recall@5	0.5961
cosine_recall@10	0.6832
cosine_ndcg@10	0.6095
cosine_mrr@10	0.5623
cosine_map@100	0.6014

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 64 3}

Metric	Value
cosine_accuracy@1	0.3818
cosine_accuracy@3	0.4405
cosine_accuracy@5	0.5224
cosine_accuracy@10	0.609
cosine_precision@1	0.3818
cosine_precision@3	0.3745
cosine_precision@5	0.2992
cosine_precision@10	0.1892
cosine_recall@1	0.1343
cosine_recall@3	0.3681
cosine_recall@5	0.4744
cosine_recall@10	0.5899
cosine_ndcg@10	0.495
cosine_mrr@10	0.4345
cosine_map@100	0.4799

Training Details

Training Dataset

Unnamed Dataset

Size: 11,644 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 9 tokens
mean: 56.58 tokens
max: 170 tokens
min: 7 tokens
mean: 57.48 tokens
max: 170 tokens

Samples:

anchor	positive
`What type of camera recorded the video in question?`	`conference following the defense’s objection to the video’s admission, during which the [object Object]court and the State discussed how to lay a proper foundation for authenticating the video: [object Object][MR. MOONEY’S COUNSEL]: I mean, there’s no way to know if that [object Object]video’s been altered. It’s somebody else’s Ring camera. These aren’t still [object Object]photographs of what happened. [object Object] [object Object]THE COURT: Has he watched it?`
`City Department of Education, the self-represented plaintiff [object Object]submitted a filing containing hallucinations. No. 24-cv-04232, [object Object] [object Object]20 [object Object]2024 WL 3460049, at *7 (S.D.N.Y. July 18, 2024) (unpublished [object Object]opinion). The court noted that “[s]anctions may be imposed for [object Object]submitting false and nonexistent legal authority to the [c]ourt.” Id. [object Object]However, the court declined to impose sanctions due to the`	`For what reason did the court note sanctions could be imposed?`
`What can happen to someone unfamiliar with the limitations of generative artificial intelligence tools?`	`Since the use of generative artificial intelligence (GAI) tools has [object Object]become widespread, lawyers and self-represented litigants alike [object Object]have relied on them to draft court filings. Because the most [object Object]commonly used GAI tools were not designed to create legal [object Object]documents, a person unfamiliar with the limitations of GAI tools, [object Object]such as the appellant in this case, can unwittingly produce text`

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
warmup_ratio: 0.1
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

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: adamw_torch_fused
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: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': 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: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: 0.1
local_rank: -1
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.4396	10	7.6935	-	-	-	-	-
0.8791	20	3.9596	-	-	-	-	-
1.0	23	-	0.6763	0.6624	0.6314	0.5678	0.4394
1.3077	30	2.5402	-	-	-	-	-
1.7473	40	2.1285	-	-	-	-	-
2.0	46	-	0.7016	0.6879	0.6626	0.5922	0.4883
2.1758	50	1.8468	-	-	-	-	-
2.6154	60	1.5615	-	-	-	-	-
3.0	69	-	0.7094	0.6954	0.6772	0.6024	0.4968
3.0440	70	1.4709	-	-	-	-	-
3.4835	80	1.4857	-	-	-	-	-
3.9231	90	1.3707	-	-	-	-	-
4.0	92	-	0.7106	0.6959	0.6804	0.6095	0.495

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.11
Sentence Transformers: 5.2.3
Transformers: 5.3.0
PyTorch: 2.5.1+cu121
Accelerate: 1.13.0
Datasets: 4.6.1
Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	anchor	positive
type	string	string
details	min: 9 tokens mean: 56.58 tokens max: 170 tokens	min: 7 tokens mean: 57.48 tokens max: 170 tokens