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BGE base Financial Matryoshka

Name: bge-small-financial-matryoshka
Availability: InStock

This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5 on the json dataset. It maps sentences & paragraphs to a 384-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: BAAI/bge-small-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("MistyDragon/bge-small-financial-matryoshka")
# Run inference
sentences = [
    'Caterpillar Insurance Co. Ltd. is registered as a Class 2 (General Business) and Class B (Long-Term) insurer with the Bermuda Monetary Authority.',
    'What types of insurance licenses does Caterpillar Insurance Co. Ltd. hold in Bermuda?',
    "What is indicated by 'Item 8' in a financial document?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.6986
cosine_accuracy@3	0.8314
cosine_accuracy@5	0.8729
cosine_accuracy@10	0.9171
cosine_precision@1	0.6986
cosine_precision@3	0.2771
cosine_precision@5	0.1746
cosine_precision@10	0.0917
cosine_recall@1	0.6986
cosine_recall@3	0.8314
cosine_recall@5	0.8729
cosine_recall@10	0.9171
cosine_ndcg@10	0.8091
cosine_mrr@10	0.7745
cosine_map@100	0.7781

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.6771
cosine_accuracy@3	0.8171
cosine_accuracy@5	0.8643
cosine_accuracy@10	0.9171
cosine_precision@1	0.6771
cosine_precision@3	0.2724
cosine_precision@5	0.1729
cosine_precision@10	0.0917
cosine_recall@1	0.6771
cosine_recall@3	0.8171
cosine_recall@5	0.8643
cosine_recall@10	0.9171
cosine_ndcg@10	0.7978
cosine_mrr@10	0.7596
cosine_map@100	0.7626

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.66
cosine_accuracy@3	0.8014
cosine_accuracy@5	0.8543
cosine_accuracy@10	0.9029
cosine_precision@1	0.66
cosine_precision@3	0.2671
cosine_precision@5	0.1709
cosine_precision@10	0.0903
cosine_recall@1	0.66
cosine_recall@3	0.8014
cosine_recall@5	0.8543
cosine_recall@10	0.9029
cosine_ndcg@10	0.7797
cosine_mrr@10	0.7405
cosine_map@100	0.7439

Training Details

Training Dataset

json

Dataset: json
Size: 6,300 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 10 tokens
mean: 47.77 tokens
max: 439 tokens
min: 8 tokens
mean: 20.48 tokens
max: 45 tokens

Samples:

positive	anchor
`Return on investment (ROI)`	12.7
`According to the terms of the Senior Credit Facilities, cash amounts exceeding $175 million can be deducted from the total debt in the leverage ratio calculation, though this is subject to certain restrictions.`	`How does the Senior Credit Facilities' treatment of cash affect the calculation of the leverage ratio?`
`In 2023, approximately 67% of the total U.S. dialysis patient service revenues were generated from government-based programs.`	`What percentage of the total U.S. dialysis patient service revenues were generated from government-based programs in 2023?`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 256, 5 128, 6 64 7 ], 8 "matryoshka_weights": [ 9 1, 10 1, 11 1 12 ], 13 "n_dims_per_step": -1 14}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_eval_batch_size: 16
gradient_accumulation_steps: 8
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: False
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
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: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: False
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
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
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.1015	10	4.9287	-	-	-
0.2030	20	3.7753	-	-	-
0.3046	30	2.7807	-	-	-
0.4061	40	2.6642	-	-	-
0.5076	50	1.8158	-	-	-
0.6091	60	1.2895	-	-	-
0.7107	70	1.356	-	-	-
0.8122	80	1.2217	-	-	-
0.9137	90	1.2548	-	-	-
1.0	99	-	0.7949	0.7853	0.7609
1.0102	100	1.1693	-	-	-
1.1117	110	1.0828	-	-	-
1.2132	120	0.9545	-	-	-
1.3147	130	1.1774	-	-	-
1.4162	140	0.55	-	-	-
1.5178	150	0.891	-	-	-
1.6193	160	0.9661	-	-	-
1.7208	170	0.9355	-	-	-
1.8223	180	0.9888	-	-	-
1.9239	190	1.0157	-	-	-
2.0	198	-	0.8067	0.7945	0.7742
2.0203	200	0.7944	-	-	-
2.1218	210	0.5637	-	-	-
2.2234	220	0.3895	-	-	-
2.3249	230	1.0888	-	-	-
2.4264	240	0.8784	-	-	-
2.5279	250	0.5746	-	-	-
2.6294	260	1.064	-	-	-
2.7310	270	0.8036	-	-	-
2.8325	280	0.6005	-	-	-
2.9340	290	0.7571	-	-	-
3.0	297	-	0.81	0.7982	0.7785
3.0305	300	0.6178	-	-	-
3.1320	310	0.5013	-	-	-
3.2335	320	0.7171	-	-	-
3.3350	330	0.5717	-	-	-
3.4365	340	0.7031	-	-	-
3.5381	350	0.8601	-	-	-
3.6396	360	0.597	-	-	-
3.7411	370	0.4611	-	-	-
3.8426	380	0.6503	-	-	-
3.9442	390	0.3176	-	-	-
4.0	396	-	0.8091	0.7978	0.7797

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.13
Sentence Transformers: 4.1.0
Transformers: 4.52.4
PyTorch: 2.6.0+cu124
Accelerate: 1.8.1
Datasets: 3.6.0
Tokenizers: 0.21.1

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}
}

	positive	anchor
type	string	string
details	min: 10 tokens mean: 47.77 tokens max: 439 tokens	min: 8 tokens mean: 20.48 tokens max: 45 tokens