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ModernBERT Embed base Finance 10k Matryoshka

Name: modernbert-embed-finance-matryoshka
Availability: InStock

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

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("rya23/modernbert-embed-finance-matryoshka")
# Run inference
sentences = [
    "What typical reimbursement methods are used in the company's contracts with hospitals for inpatient and outpatient services?",
    'We typically contract with hospitals on either (1) a per diem rate, which is an all-inclusive rate per day, (2) a case rate for diagnosis-related groups (DRG), which is an all-inclusive rate per admission, or (3) a discounted charge for inpatient hospital services. Outpatient hospital services generally are contracted at a flat rate by type of service, ambulatory payment classifications, or APCs, or at a discounted charge.',
    'In IBM’s 2023 Annual Report to Stockholders, the Financial Statements and Supplementary Data are detailed on pages 44 through 121.',
]
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, 0.6756, 0.0659],
#         [0.6756, 1.0000, 0.0087],
#         [0.0659, 0.0087, 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	0.7244
cosine_accuracy@3	0.8554
cosine_accuracy@5	0.8903
cosine_accuracy@10	0.9271
cosine_precision@1	0.7244
cosine_precision@3	0.2851
cosine_precision@5	0.1781
cosine_precision@10	0.0927
cosine_recall@1	0.7244
cosine_recall@3	0.8554
cosine_recall@5	0.8903
cosine_recall@10	0.9271
cosine_ndcg@10	0.8286
cosine_mrr@10	0.7968
cosine_map@100	0.7999

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.7239
cosine_accuracy@3	0.8533
cosine_accuracy@5	0.8874
cosine_accuracy@10	0.927
cosine_precision@1	0.7239
cosine_precision@3	0.2844
cosine_precision@5	0.1775
cosine_precision@10	0.0927
cosine_recall@1	0.7239
cosine_recall@3	0.8533
cosine_recall@5	0.8874
cosine_recall@10	0.927
cosine_ndcg@10	0.8273
cosine_mrr@10	0.7952
cosine_map@100	0.7983

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.7231
cosine_accuracy@3	0.8513
cosine_accuracy@5	0.886
cosine_accuracy@10	0.924
cosine_precision@1	0.7231
cosine_precision@3	0.2838
cosine_precision@5	0.1772
cosine_precision@10	0.0924
cosine_recall@1	0.7231
cosine_recall@3	0.8513
cosine_recall@5	0.886
cosine_recall@10	0.924
cosine_ndcg@10	0.8256
cosine_mrr@10	0.7938
cosine_map@100	0.7971

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.7017
cosine_accuracy@3	0.8363
cosine_accuracy@5	0.8726
cosine_accuracy@10	0.9166
cosine_precision@1	0.7017
cosine_precision@3	0.2788
cosine_precision@5	0.1745
cosine_precision@10	0.0917
cosine_recall@1	0.7017
cosine_recall@3	0.8363
cosine_recall@5	0.8726
cosine_recall@10	0.9166
cosine_ndcg@10	0.8108
cosine_mrr@10	0.7767
cosine_map@100	0.7803

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.6703
cosine_accuracy@3	0.8053
cosine_accuracy@5	0.8491
cosine_accuracy@10	0.8959
cosine_precision@1	0.6703
cosine_precision@3	0.2684
cosine_precision@5	0.1698
cosine_precision@10	0.0896
cosine_recall@1	0.6703
cosine_recall@3	0.8053
cosine_recall@5	0.8491
cosine_recall@10	0.8959
cosine_ndcg@10	0.7834
cosine_mrr@10	0.7474
cosine_map@100	0.7516

Training Details

Training Dataset

json

Dataset: json
Size: 6,300 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 9 tokens
mean: 20.55 tokens
max: 43 tokens
min: 5 tokens
mean: 46.3 tokens
max: 243 tokens

Samples:

anchor	positive
`How many shares of class A common stock were authorized for grant under Visa's Equity Incentive Compensation Plan?`	`Under the Company’s 2007 Amended and Restated Equity Incentive Compensation Plan (EIP), the compensation committee of the board of directors was authorized to grant up to 198 million shares of class A common stock to its employees and non-employee directors.`
`What was Garmin Ltd.'s net income for the fiscal year ended December 30, 2023?`	`Garmin Ltd. reported a net income of $1,289,636 for the fiscal year ended December 30, 2023.`
`Why are some device sales revenue at AT&T not immediately recognized upon the device sale?`	`AT&T recognizes revenue from device sales with promotions or installment payments differently. For promotional discounts, revenue is deferred and amortized over the contract term. Meanwhile, installment sales involve recognizing revenue upfront but deferring the cash receipt until payments are made, resulting in a recorded contract asset to be amortized over time.`

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

eval_strategy: epoch
per_device_eval_batch_size: 4
gradient_accumulation_steps: 48
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
warmup_steps: 0.1
fp16: True
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 4
gradient_accumulation_steps: 48
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: None
warmup_ratio: 0.1
warmup_steps: 0.1
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 42
data_seed: None
bf16: False
fp16: True
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_for_metrics: []
eval_do_concat_batches: True
auto_find_batch_size: False
full_determinism: False
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
use_cache: False
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
0.6091	10	0.3092	-	-	-	-	-
1.0	17	-	0.8155	0.8138	0.8104	0.7948	0.7647
1.1827	20	0.0958	-	-	-	-	-
1.7919	30	0.0675	-	-	-	-	-
2.0	34	-	0.8257	0.8245	0.8219	0.8045	0.7757
2.3655	40	0.0458	-	-	-	-	-
2.9746	50	0.0505	-	-	-	-	-
3.0	51	-	0.8277	0.8259	0.8243	0.8087	0.7819
3.5482	60	0.0593	-	-	-	-	-
4.0	68	-	0.8286	0.8273	0.8256	0.8108	0.7834

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.2
Transformers: 5.0.0
PyTorch: 2.9.0+cu128
Accelerate: 1.12.0
Datasets: 4.0.0
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: 20.55 tokens max: 43 tokens	min: 5 tokens mean: 46.3 tokens max: 243 tokens