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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 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: BAAI/bge-base-en-v1.5
Maximum Sequence Length: 512 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': 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("phucvt0302/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'How many shares were outstanding at the beginning of 2023 and what was their aggregate intrinsic value?',
    'At the beginning of 2023, there were 355 shares outstanding with an aggregate intrinsic value of $142,916.',
    'In IBM’s 2023 Annual Report to Stockholders, the Financial Statements and Supplementary Data are included 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.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	0.7171
cosine_accuracy@3	0.8414
cosine_accuracy@5	0.87
cosine_accuracy@10	0.9071
cosine_precision@1	0.7171
cosine_precision@3	0.2805
cosine_precision@5	0.174
cosine_precision@10	0.0907
cosine_recall@1	0.7171
cosine_recall@3	0.8414
cosine_recall@5	0.87
cosine_recall@10	0.9071
cosine_ndcg@10	0.8148
cosine_mrr@10	0.785
cosine_map@100	0.7888

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.7171
cosine_accuracy@3	0.8471
cosine_accuracy@5	0.8671
cosine_accuracy@10	0.9086
cosine_precision@1	0.7171
cosine_precision@3	0.2824
cosine_precision@5	0.1734
cosine_precision@10	0.0909
cosine_recall@1	0.7171
cosine_recall@3	0.8471
cosine_recall@5	0.8671
cosine_recall@10	0.9086
cosine_ndcg@10	0.8147
cosine_mrr@10	0.7844
cosine_map@100	0.788

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.7171
cosine_accuracy@3	0.8414
cosine_accuracy@5	0.8729
cosine_accuracy@10	0.9014
cosine_precision@1	0.7171
cosine_precision@3	0.2805
cosine_precision@5	0.1746
cosine_precision@10	0.0901
cosine_recall@1	0.7171
cosine_recall@3	0.8414
cosine_recall@5	0.8729
cosine_recall@10	0.9014
cosine_ndcg@10	0.8139
cosine_mrr@10	0.7854
cosine_map@100	0.7895

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.7129
cosine_accuracy@3	0.8243
cosine_accuracy@5	0.8586
cosine_accuracy@10	0.9029
cosine_precision@1	0.7129
cosine_precision@3	0.2748
cosine_precision@5	0.1717
cosine_precision@10	0.0903
cosine_recall@1	0.7129
cosine_recall@3	0.8243
cosine_recall@5	0.8586
cosine_recall@10	0.9029
cosine_ndcg@10	0.8072
cosine_mrr@10	0.7766
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	0.6914
cosine_accuracy@3	0.7943
cosine_accuracy@5	0.8314
cosine_accuracy@10	0.8729
cosine_precision@1	0.6914
cosine_precision@3	0.2648
cosine_precision@5	0.1663
cosine_precision@10	0.0873
cosine_recall@1	0.6914
cosine_recall@3	0.7943
cosine_recall@5	0.8314
cosine_recall@10	0.8729
cosine_ndcg@10	0.7805
cosine_mrr@10	0.7512
cosine_map@100	0.7554

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: 7 tokens
mean: 20.53 tokens
max: 46 tokens
min: 8 tokens
mean: 44.95 tokens
max: 512 tokens

Samples:

anchor	positive
`What does the No Surprises Act require providers to develop and disclose?`	`Under the No Surprises Act, which went into effect January 1, 2022, certain providers, including DaVita, are required to develop and disclose a 'Good Faith Estimate' that details the expected charges for furnishing certain items or services.`
`What does Gross Merchandise Volume (GMV) represent in financial terms?`	`GMV consists of the total value of all paid transactions between users on our platforms during the applicable period inclusive of shipping fees and taxes.`
`What was the pre-tax restructuring charge for the fiscal year 2023 related to the discontinuation of certain R&D programs?`	`The pre-tax restructuring charge of approximately $0.5 billion in the fiscal year 2023 included the termination of partnered and non-partnered program costs and asset impairments.`

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

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
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
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
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
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}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, '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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
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
0.9697	6	-	0.7993	0.7967	0.7941	0.7845	0.7431
1.6162	10	2.3395	-	-	-	-	-
1.9394	12	-	0.8089	0.8086	0.8108	0.8007	0.7669
2.9091	18	-	0.8158	0.8134	0.8144	0.8066	0.7761
3.2323	20	1.0419	-	-	-	-	-
3.8788	24	-	0.8148	0.8147	0.8139	0.8072	0.7805

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.6
Sentence Transformers: 4.1.0
Transformers: 4.40.2
PyTorch: 2.6.0+cu124
Accelerate: 1.6.0
Datasets: 2.19.1
Tokenizers: 0.19.1

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: 20.53 tokens max: 46 tokens	min: 8 tokens mean: 44.95 tokens max: 512 tokens