ModernBERT Embed base Legal Matryoshka
This is a sentence-transformers model finetuned from nomic-ai/modernbert-embed-base on the json 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:
- 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}) with Transformer model: 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("manu-reyes-23p/modernbert-embed-base-legal-matryoshka-2")
# Run inference
sentences = [
'Importantly here, though a mentor may have more than one protégé, it cannot bid on the same \nSolicitation multiple times using different protégé relationships. See 13 C.F.R. § 125.9(b)(3)(i). \nSpecifically, the law dictates that “[a] mentor that has more than one protégé cannot submit \ncompeting offers in response to a solicitation for a specific procurement through separate joint',
'According to the law, can a mentor submit competing offers for the same procurement?',
'At what level are specific IT services contracted for and performed?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Datasets:
dim_768
,dim_512
,dim_256
,dim_128
anddim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
---|---|---|---|---|---|
cosine_accuracy@1 | 0.6 | 0.6 | 0.6 | 0.5 | 0.6 |
cosine_accuracy@3 | 0.6 | 0.6 | 0.7 | 0.6 | 0.6 |
cosine_accuracy@5 | 0.7 | 0.7 | 0.8 | 0.8 | 0.7 |
cosine_accuracy@10 | 0.8 | 0.8 | 0.9 | 0.9 | 0.8 |
cosine_precision@1 | 0.6 | 0.6 | 0.6 | 0.5 | 0.6 |
cosine_precision@3 | 0.5667 | 0.5667 | 0.6 | 0.5 | 0.5667 |
cosine_precision@5 | 0.4 | 0.4 | 0.46 | 0.42 | 0.4 |
cosine_precision@10 | 0.27 | 0.27 | 0.29 | 0.29 | 0.27 |
cosine_recall@1 | 0.2 | 0.2 | 0.2 | 0.175 | 0.2 |
cosine_recall@3 | 0.55 | 0.55 | 0.575 | 0.5 | 0.55 |
cosine_recall@5 | 0.625 | 0.625 | 0.7 | 0.65 | 0.625 |
cosine_recall@10 | 0.8 | 0.8 | 0.8667 | 0.8667 | 0.8 |
cosine_ndcg@10 | 0.7027 | 0.7027 | 0.7474 | 0.7062 | 0.7053 |
cosine_mrr@10 | 0.6343 | 0.6343 | 0.6644 | 0.5894 | 0.6367 |
cosine_map@100 | 0.6878 | 0.6925 | 0.7156 | 0.6572 | 0.6806 |
Training Details
Training Dataset
json
- Dataset: json
- Size: 90 training samples
- Columns:
positive
andanchor
- Approximate statistics based on the first 90 samples:
positive anchor type string string details - min: 73 tokens
- mean: 100.82 tokens
- max: 135 tokens
- min: 9 tokens
- mean: 17.13 tokens
- max: 29 tokens
- Samples:
positive anchor 1 This Memorandum and Order was filed under seal in accordance with the Amended Protective
Order entered in this case (ECF No. 22) and was publicly reissued after incorporating all
appropriate redactions proposed by the parties (ECF No. 44-1). The sealed and public versions of
this Memorandum and Order are otherwise substantively identical, except for the publication date,Under what condition was the Memorandum and Order filed?
the pagination within that document. Citations to all other documents, including briefing and
exhibits, reference the ECF-assigned page numbers, which do not always correspond to the
pagination within the document.
3
BACKGROUND
I.
The Parties
SHS and VCH are IT service providers and mentor-protégé joint ventures (JVs) formedWhat do the ECF-assigned page numbers not always correspond to?
https://www.gsa.gov/policy-regulations/regulations/federal-acquisition-regulation-far (last visited
Apr. 19, 2023) (“The Department of Defense (DoD), GSA, and the National Aeronautics and
Space Administration (NASA) jointly issue the FAR.”).
5 SBA Mentor-Protégé Program, Small Business Administration, https://www.sba.gov/federal-Which organizations jointly issue the FAR?
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 32per_device_eval_batch_size
: 16gradient_accumulation_steps
: 16learning_rate
: 2e-05num_train_epochs
: 1lr_scheduler_type
: cosinewarmup_ratio
: 0.1tf32
: Falseload_best_model_at_end
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 16eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Falselocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | 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 |
---|---|---|---|---|---|---|
1.0 | 1 | 0.7027 | 0.7027 | 0.7474 | 0.7062 | 0.7053 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0
- PyTorch: 2.7.0
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
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",
}
MatryoshkaLoss
@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
@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}
}
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Model tree for manu-reyes-23p/modernbert-embed-base-legal-matryoshka-2
Base model
answerdotai/ModernBERT-base
Finetuned
nomic-ai/modernbert-embed-base
Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.600
- Cosine Accuracy@3 on dim 768self-reported0.600
- Cosine Accuracy@5 on dim 768self-reported0.700
- Cosine Accuracy@10 on dim 768self-reported0.800
- Cosine Precision@1 on dim 768self-reported0.600
- Cosine Precision@3 on dim 768self-reported0.567
- Cosine Precision@5 on dim 768self-reported0.400
- Cosine Precision@10 on dim 768self-reported0.270
- Cosine Recall@1 on dim 768self-reported0.200
- Cosine Recall@3 on dim 768self-reported0.550