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alantang2025/
bge-base-financial-matryoshka

Name: bge-base-financial-matryoshka
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BGE base Financial Matryoshka

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, 'architecture': '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("alantang2025/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "The audited components of Salesforce, Inc.'s financial statements as of January 31, 2023, and for the three years ending on that date, included the consolidated balance sheets, the related consolidated statements of operations, comprehensive income, stockholders' equity, and cash flows.",
    "What were the key components audited in Salesforce, Inc.'s financial statements for the years ending January 31, 2023?",
    'Where can one find the details mentioned in Item 3 regarding Legal Proceedings?',
]
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, 0.9157, 0.2390],
#         [0.9157, 1.0000, 0.2109],
#         [0.2390, 0.2109, 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	0.7157
cosine_accuracy@3	0.8386
cosine_accuracy@5	0.87
cosine_accuracy@10	0.9214
cosine_precision@1	0.7157
cosine_precision@3	0.2795
cosine_precision@5	0.174
cosine_precision@10	0.0921
cosine_recall@1	0.7157
cosine_recall@3	0.8386
cosine_recall@5	0.87
cosine_recall@10	0.9214
cosine_ndcg@10	0.8186
cosine_mrr@10	0.7858
cosine_map@100	0.789

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.7129
cosine_accuracy@3	0.8429
cosine_accuracy@5	0.8743
cosine_accuracy@10	0.9157
cosine_precision@1	0.7129
cosine_precision@3	0.281
cosine_precision@5	0.1749
cosine_precision@10	0.0916
cosine_recall@1	0.7129
cosine_recall@3	0.8429
cosine_recall@5	0.8743
cosine_recall@10	0.9157
cosine_ndcg@10	0.8166
cosine_mrr@10	0.7847
cosine_map@100	0.7882

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.7143
cosine_accuracy@3	0.8314
cosine_accuracy@5	0.87
cosine_accuracy@10	0.9129
cosine_precision@1	0.7143
cosine_precision@3	0.2771
cosine_precision@5	0.174
cosine_precision@10	0.0913
cosine_recall@1	0.7143
cosine_recall@3	0.8314
cosine_recall@5	0.87
cosine_recall@10	0.9129
cosine_ndcg@10	0.8135
cosine_mrr@10	0.7818
cosine_map@100	0.7853

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.7043
cosine_accuracy@3	0.8214
cosine_accuracy@5	0.8557
cosine_accuracy@10	0.9057
cosine_precision@1	0.7043
cosine_precision@3	0.2738
cosine_precision@5	0.1711
cosine_precision@10	0.0906
cosine_recall@1	0.7043
cosine_recall@3	0.8214
cosine_recall@5	0.8557
cosine_recall@10	0.9057
cosine_ndcg@10	0.8047
cosine_mrr@10	0.7725
cosine_map@100	0.7765

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.6643
cosine_accuracy@3	0.7986
cosine_accuracy@5	0.8343
cosine_accuracy@10	0.8786
cosine_precision@1	0.6643
cosine_precision@3	0.2662
cosine_precision@5	0.1669
cosine_precision@10	0.0879
cosine_recall@1	0.6643
cosine_recall@3	0.7986
cosine_recall@5	0.8343
cosine_recall@10	0.8786
cosine_ndcg@10	0.7729
cosine_mrr@10	0.7389
cosine_map@100	0.7437

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: 6 tokens
mean: 45.06 tokens
max: 512 tokens
min: 7 tokens
mean: 20.42 tokens
max: 38 tokens

Samples:

positive	anchor
`We adjust unrecognized tax benefits and related interests as facts and circumstances change, such as receiving audit assessments.`	`Under what circumstances are unrecognized tax benefits adjusted?`
`‘expanded’ also includes stores that are relocated. Stores that have been re-bannered are considered to be new stores and are not included in the calculation of the comparable store net sales change until after the first fifteen months of operation under the new brand.`	`What does 'expanded' refer to in the context of calculating comparable store net sales changes?`
`The identification of observable transactions will depend on the timely reporting of these transactions from our investee companies, which may occur in a period subsequent to when the transactions take place. Therefore, our fair value adjustment for these observable transactions may occur in a period subsequent to when the transaction actually occurred.`	`When might fair value adjustments for observable transactions occur for non-marketable equity securities?`

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
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: 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
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
0.8122	10	25.3913	-	-	-	-	-
1.0	13	-	0.7963	0.7987	0.7931	0.7756	0.7376
1.5685	20	10.5619	-	-	-	-	-
2.0	26	-	0.8042	0.8044	0.7987	0.7895	0.7521
0.8122	10	7.453	-	-	-	-	-
1.0	13	-	0.8096	0.8085	0.8050	0.7931	0.7591
1.5685	20	5.5897	-	-	-	-	-
2.0	26	-	0.8162	0.8138	0.8094	0.7973	0.7664
2.3249	30	4.5948	-	-	-	-	-
3.0	39	-	0.8168	0.8117	0.8101	0.7996	0.7631
3.0812	40	4.6814	-	-	-	-	-
3.7310	48	-	0.8164	0.8119	0.8101	0.7978	0.7648
0.8122	10	4.0773	-	-	-	-	-
1.0	13	-	0.8170	0.8133	0.8115	0.8015	0.7657
1.5685	20	3.2546	-	-	-	-	-
2.0	26	-	0.8168	0.8138	0.8107	0.8029	0.7710
2.3249	30	2.9727	-	-	-	-	-
3.0	39	-	0.819	0.8157	0.8126	0.8055	0.7727
3.0812	40	3.3666	-	-	-	-	-
3.7310	48	-	0.8186	0.8166	0.8135	0.8047	0.7729

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.9
Sentence Transformers: 5.1.2
Transformers: 4.51.3
PyTorch: 2.6.0
Accelerate: 1.11.0
Datasets: 3.6.0
Tokenizers: 0.21.4

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: 6 tokens mean: 45.06 tokens max: 512 tokens	min: 7 tokens mean: 20.42 tokens max: 38 tokens