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BGE base Financial Matryoshka

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

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the finanical-rag-embedding-dataset 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: BAAI/bge-base-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- finanical-rag-embedding-dataset
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': 768, '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("bnkc123/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "What is the maximum leverage ratio allowed before default under the company's credit facility?",
    "If the company's leverage ratio exceeds 3.50 to 1, it would be in default of its revolving credit facility, impairing its ability to borrow under the facility.",
    'Research and Development Because the industries in which the Company competes are characterized by rapid technological advances, the Company’s ability to compete successfully depends heavily upon its ability to ensure a continual and timely flow of competitive products, services and technologies to the marketplace.',
]
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

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 768 3}

Metric	Value
cosine_accuracy@1	0.6771
cosine_accuracy@3	0.8371
cosine_accuracy@5	0.8686
cosine_accuracy@10	0.9186
cosine_precision@1	0.6771
cosine_precision@3	0.279
cosine_precision@5	0.1737
cosine_precision@10	0.0919
cosine_recall@1	0.6771
cosine_recall@3	0.8371
cosine_recall@5	0.8686
cosine_recall@10	0.9186
cosine_ndcg@10	0.8008
cosine_mrr@10	0.7627
cosine_map@100	0.7656

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.6829
cosine_accuracy@3	0.8371
cosine_accuracy@5	0.8757
cosine_accuracy@10	0.92
cosine_precision@1	0.6829
cosine_precision@3	0.279
cosine_precision@5	0.1751
cosine_precision@10	0.092
cosine_recall@1	0.6829
cosine_recall@3	0.8371
cosine_recall@5	0.8757
cosine_recall@10	0.92
cosine_ndcg@10	0.8044
cosine_mrr@10	0.7671
cosine_map@100	0.77

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.6757
cosine_accuracy@3	0.8229
cosine_accuracy@5	0.8643
cosine_accuracy@10	0.9186
cosine_precision@1	0.6757
cosine_precision@3	0.2743
cosine_precision@5	0.1729
cosine_precision@10	0.0919
cosine_recall@1	0.6757
cosine_recall@3	0.8229
cosine_recall@5	0.8643
cosine_recall@10	0.9186
cosine_ndcg@10	0.7984
cosine_mrr@10	0.7599
cosine_map@100	0.7625

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.6714
cosine_accuracy@3	0.8114
cosine_accuracy@5	0.8486
cosine_accuracy@10	0.9014
cosine_precision@1	0.6714
cosine_precision@3	0.2705
cosine_precision@5	0.1697
cosine_precision@10	0.0901
cosine_recall@1	0.6714
cosine_recall@3	0.8114
cosine_recall@5	0.8486
cosine_recall@10	0.9014
cosine_ndcg@10	0.7873
cosine_mrr@10	0.7507
cosine_map@100	0.7543

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.6243
cosine_accuracy@3	0.7843
cosine_accuracy@5	0.82
cosine_accuracy@10	0.8829
cosine_precision@1	0.6243
cosine_precision@3	0.2614
cosine_precision@5	0.164
cosine_precision@10	0.0883
cosine_recall@1	0.6243
cosine_recall@3	0.7843
cosine_recall@5	0.82
cosine_recall@10	0.8829
cosine_ndcg@10	0.7546
cosine_mrr@10	0.7135
cosine_map@100	0.7174

Training Details

Training Dataset

finanical-rag-embedding-dataset

Dataset: finanical-rag-embedding-dataset at e0b1781
Size: 6,300 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 7 tokens
mean: 20.5 tokens
max: 43 tokens
min: 9 tokens
mean: 46.09 tokens
max: 512 tokens

Samples:

anchor	positive
`What was the amount of premiums written by Berkshire Hathaway's Insurance Underwriting in 2023, and how did it compare to the previous year?`	`Premiums written increased $3.5 billion (24.1%) in 2023 compared to 2022. The increase was primarily due to RSUI and CapSpecialty ($2.1 billion), as well as comparative increases from BHSI and BH Direct, and to a lesser extent the other businesses. Premiums written`
`What types of transportation equipment does XTRA Corporation manage in its fleet?`	`XTRA manages a diverse fleet of approximately 90,000 units located at 47 facilities throughout the U.S. The fleet includes over-the-road and storage trailers, chassis, temperature-controlled vans and flatbed trailers.`
`What seasonal trends affect the company's sales volumes?`	`Sales volumes for the company are highest in the second fiscal quarter due to seasonal influences, particularly during the spring season in the regions it serves.`

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_train_batch_size: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
load_best_model_at_end: True
optim: adamw_torch_fused
push_to_hub: True
hub_model_id: bnkc123/bge-base-financial-matryoshka
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: 32
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
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: True
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}
tp_size: 0
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: True
resume_from_checkpoint: None
hub_model_id: bnkc123/bge-base-financial-matryoshka
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_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.8122	10	25.483	-	-	-	-	-
1.0	13	-	0.7890	0.7887	0.7815	0.7647	0.7280
1.5685	20	9.1323	-	-	-	-	-
2.0	26	-	0.7952	0.7982	0.7933	0.7801	0.7477
2.3249	30	6.7535	-	-	-	-	-
3.0	39	-	0.8019	0.8048	0.7989	0.7865	0.7547
3.0812	40	6.5646	-	-	-	-	-
3.731	48	-	0.8008	0.8044	0.7984	0.7873	0.7546

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.6
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.7.0+cu126
Accelerate: 1.6.0
Datasets: 3.5.1
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}
}

	anchor	positive
type	string	string
details	min: 7 tokens mean: 20.5 tokens max: 43 tokens	min: 9 tokens mean: 46.09 tokens max: 512 tokens