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aaa961/
modernbert-embed-base-legal-no_MRL_symmetricMNRL_3sets

Name: modernbert-embed-base-legal-no_MRL_symmetricMNRL_3sets
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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
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.

python

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]])

Evaluation

Metrics

Information Retrieval

Datasets: ir_eval_test and ir_eval_eval
Evaluated with InformationRetrievalEvaluator

Metric	ir_eval_test	ir_eval_eval
cosine_accuracy@1	0.3431	0.5796
cosine_accuracy@3	0.5131	0.7527
cosine_accuracy@5	0.575	0.8176
cosine_accuracy@10	0.6785	0.864
cosine_precision@1	0.3431	0.5796
cosine_precision@3	0.171	0.2509
cosine_precision@5	0.115	0.1635
cosine_precision@10	0.0679	0.0864
cosine_recall@1	0.3431	0.5796
cosine_recall@3	0.5131	0.7527
cosine_recall@5	0.575	0.8176
cosine_recall@10	0.6785	0.864
cosine_ndcg@10	0.5028	0.7246
cosine_mrr@10	0.4477	0.6795
cosine_map@100	0.4565	0.6842

Training Details

Training Dataset

legal-rag-positives-synthetic

Dataset: legal-rag-positives-synthetic at f11534a
Size: 5,175 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 8 tokens
mean: 16.61 tokens
max: 34 tokens
min: 44 tokens
mean: 96.79 tokens
max: 157 tokens

Samples:

anchor	positive
`Where is the similar statement to the one about business judgment and scoring merit found?`	`is with each bidder itself, and its own business judgment in forming a team and what score it thinks [object Object]is enough to merit an award.”) (emphasis in original); VCH MJAR at 21–22 (same); Oral Ar. Tr. [object Object]at 10:5–7 (“[C]ompetition involves . . . some sort of tradeoff between offerors, some sort of [object Object]evaluation of how offerors are against one another, and that’s not the case here. The case here is`
`Who do lawyers generally employ as assistants in their practice?`	`abide by the Rules of Professional Conduct. See rule 4-5.2(a). [object Object] [object Object]RULE 4-5.3. [object Object]RESPONSIBILITIES REGARDING NONLAWYER [object Object]ASSISTANTS [object Object](a) – (c) [No Change] [object Object]Comment [object Object]Lawyers generally employ assistants in their practice, [object Object]including secretaries, investigators, law student interns, and [object Object]paraprofessionals such as paralegals and legal assistants. Such`
`Which court case is cited with a page number of 1327?`	`30; VCH MJAR at 28–30 (same). [object Object]As noted, this Court applies the same interpretive rules to analyze both statutes and federal [object Object]regulations. See Boeing, 983 F.3d at 1327 (citing Mass. Mut. Life Ins. Co., 782 F.3d at 1365); see [object Object]also supra Discussion Section I. It is a “fundamental canon of statutory construction that the words`

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "mini_batch_size": 32, 5 "gather_across_devices": false, 6 "directions": [ 7 "query_to_doc", 8 "doc_to_query" 9 ], 10 "partition_mode": "per_direction", 11 "hardness_mode": null, 12 "hardness_strength": 0.0 13}

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

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: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	ir_eval_test_cosine_ndcg@10	ir_eval_eval_cosine_ndcg@10
-1	-1	-	0.5028	-
0.9877	10	0.9180	-	-
1.0	11	-	-	0.6723
1.8889	20	0.4042	-	-
2.0	22	-	-	0.7082
2.7901	30	0.2940	-	-
3.0	33	-	-	0.7236
3.6914	40	0.2646	-	-
4.0	44	-	-	0.7246

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

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",
}

CachedMultipleNegativesRankingLoss

bibtex

@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}
}

	anchor	positive
type	string	string
details	min: 8 tokens mean: 16.61 tokens max: 34 tokens	min: 44 tokens mean: 96.79 tokens max: 157 tokens