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aaa961/
modernbert-embed-base-legal_no_MRL_reverse_dataset_early_stopping

Name: modernbert-embed-base-legal_no_MRL_reverse_dataset_early_stopping
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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_reverse_dataset_early_stopping")
# Run inference
sentences = [
    'confidentiality agreement/order, that remain following those discussions.  This is a \nfinal report and notice of exceptions shall be filed within three days of the date of \nthis report, pursuant to Court of Chancery Rule 144(d)(2), given the expedited and \nsummary nature of Section 220 proceedings.  \n \n \n \n \n \n \n \nRespectfully, \n \n \n \n \n \n \n \n \n/s/ Patricia W. Griffin',
    'According to which court rule must the notice of exceptions be filed?',
    'decides whether to submit proposals on future procurements, and excluding mentor-protégé JVs \nfrom proposing on a solicitation due to Section 125.9(b)(3)(i) unnecessarily prevents protégés from \naccessing opportunities to grow as a business.  SHS MJAR at 22–23; VCH MJAR at 22–23.   \nSuch a critique, however, merely highlights Plaintiffs’ disagreement with the SBA’s',
]
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, 0.4922, 0.0280],
#         [0.4922, 1.0000, 0.0389],
#         [0.0280, 0.0389, 1.0000]])

Evaluation

Metrics

Information Retrieval

Datasets: ir and ir_eval
Evaluated with InformationRetrievalEvaluator

Metric	ir	ir_eval
cosine_accuracy@1	0.3617	0.6337
cosine_accuracy@3	0.4096	0.6893
cosine_accuracy@5	0.473	0.7759
cosine_accuracy@10	0.5255	0.8377
cosine_precision@1	0.3617	0.6337
cosine_precision@3	0.3524	0.6028
cosine_precision@5	0.277	0.4566
cosine_precision@10	0.1631	0.2583
cosine_recall@1	0.1269	0.2246
cosine_recall@3	0.3452	0.5938
cosine_recall@5	0.4405	0.7313
cosine_recall@10	0.5161	0.8237
cosine_ndcg@10	0.4459	0.736
cosine_mrr@10	0.4019	0.6825
cosine_map@100	0.442	0.7181

Training Details

Training Dataset

legal-rag-positives-synthetic

Dataset: legal-rag-positives-synthetic at f11534a
Size: 11,644 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 7 tokens
mean: 57.45 tokens
max: 160 tokens
min: 8 tokens
mean: 57.77 tokens
max: 157 tokens

Samples:

anchor	positive
`What kinds of issues are mentioned in connection with wrongdoing?`	`mismanagement, waste and wrongdoing – and that it has demonstrated more than a [object Object]credible basis from which the Court can infer possible mismanagement. It claims [object Object]DR’s management failed to follow corporate governance mechanics and made [object Object]critical business decisions without consulting with the Board or stockholders; [object Object]failed to act with due diligence related to undertaking an ICO and discontinuing`
`Project, 504 F.2d at 248 n.15). [object Object]More, the requirement of “substantial” authority suggests that the entity should be at the [object Object]“center of gravity in the exercise of administrative power.” Id. at 882 (quoting Lombardo v. [object Object]Handler, 397 F. Supp. 792, 796 (D.D.C. 1975), aff’d, 546 F.2d 1043 (D.C. Cir. 1976)). On this`	`What page reference is given for the Lombardo v. Handler case in the aforementioned citation?`
`Where can more detailed information regarding redactions be found?`	`parties specifically with respect to the FOIA request at issue in Count Eighteen of No. 11-444. This is likely [object Object]because the CIA has previously instituted a categorical policy of indicating the basis for redactions at a document [object Object]level, rather than a redaction level, as discussed above. See supra Part III.C.2. In light of the Court’s holding that`

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

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_cosine_ndcg@10	ir_eval_cosine_ndcg@10
-1	-1	-	0.4459	0.4459
0.4396	10	1.4221	-	-
0.8791	20	0.6964	-	-
1.0	23	-	-	0.6760
1.3077	30	0.4787	-	-
1.7473	40	0.4033	-	-
2.0	46	-	-	0.7196
2.1758	50	0.3770	-	-
2.6154	60	0.3159	-	-
3.0	69	-	-	0.7361
3.0440	70	0.3345	-	-
3.4835	80	0.2698	-	-
3.9231	90	0.3188	-	-
4.0	92	-	-	0.7360

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",
}

MultipleNegativesRankingLoss

bibtex

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

	anchor	positive
type	string	string
details	min: 7 tokens mean: 57.45 tokens max: 160 tokens	min: 8 tokens mean: 57.77 tokens max: 157 tokens