metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:6300
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
  - source_sentence: >-
      Health Care Benefits revenue is principally derived from insurance
      premiums and fees billed to customers.
    sentences:
      - >-
        How much was the cumulative impairment and downward adjustments for
        observable price changes for the equity investments without readily
        determinable fair values as of December 31, 2023?
      - >-
        What are the revenue sources for the Company’s Health Care Benefits
        Segment?
      - >-
        What types of legal issues are generally categorized under Commitments
        and Contingencies in a Form 10-K?
  - source_sentence: >-
      Total net sales increased by 7% during the fiscal year ending December 30,
      2023 compared to the previous fiscal year.
    sentences:
      - >-
        What was the percentage increase in Data Center revenue for fiscal year
        2023 compared to the previous year?
      - >-
        What was the percentage increase in total net sales during the fiscal
        year ending December 30, 2023 compared to the previous fiscal year?
      - >-
        What were the expenses related to the fair value of restricted stock
        units (RSUs) and stock options for the years 2022, 2021, and 2020?
  - source_sentence: >-
      The laws and regulations of the jurisdictions in which our insurance and
      reinsurance subsidiaries are domiciled require among other things that
      these subsidiaries maintain minimum levels of statutory capital, surplus,
      and liquidity, meet solvency standards, and submit to periodic
      examinations of their financial condition.
    sentences:
      - >-
        What statutory requirements must insurance and reinsurance subsidiaries
        meet in their domiciled jurisdictions?
      - What activities has the federal government used the FCA to prosecute?
      - >-
        How are self-insurance reserves computed and presented in financial
        statements?
  - source_sentence: >-
      Services net sales increased 9% or $7.1 billion during 2023 compared to
      2022 due to higher net sales across all lines of business.
    sentences:
      - >-
        What is the leverage ratio requirement under the company's financial
        covenant as of January 28, 2023?
      - >-
        What are the enrollment periods for Medicare Advantage and stand-alone
        prescription drug plans?
      - >-
        What was the percentage increase in Services net sales from 2022 to
        2023?
  - source_sentence: >-
      Certain vendors have been impacted by volatility in the supply chain
      financing market.
    sentences:
      - >-
        How have certain vendors been impacted in the supply chain financing
        market?
      - >-
        What was the total value of the company's cash commitments as of
        December 31, 2023?
      - >-
        What are the key components used to define free cash flow in financial
        evaluations?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: BGE base Financial Matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.6871428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8171428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8485714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9085714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6871428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2723809523809524
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16971428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09085714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6871428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8171428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8485714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9085714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7960378752604689
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7603769841269836
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7640840138316877
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6828571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8114285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8528571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9085714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6828571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2704761904761904
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17057142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09085714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6828571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8114285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8528571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9085714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7936620196836198
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7572222222222219
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7609298999926937
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.68
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8071428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8485714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8957142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.68
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26904761904761904
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16971428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08957142857142855
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.68
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8071428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8485714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8957142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7883110340362532
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7539733560090701
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7582685695127231
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6585714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7942857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.83
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8842857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6585714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26476190476190475
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16599999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08842857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6585714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7942857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.83
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8842857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7727884715594033
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.737036848072562
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7419081242961935
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6357142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7628571428571429
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8142857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.87
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6357142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2542857142857142
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16285714285714287
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.087
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6357142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7628571428571429
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8142857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.87
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7501277228250628
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7121167800453513
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7171110018302509
            name: Cosine Map@100

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}) 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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("MarekMarik/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Certain vendors have been impacted by volatility in the supply chain financing market.',
    'How have certain vendors been impacted in the supply chain financing market?',
    "What was the total value of the company's cash commitments as of December 31, 2023?",
]
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

Datasets: dim_768, dim_512, dim_256, dim_128 and dim_64
Evaluated with InformationRetrievalEvaluator

Metric	dim_768	dim_512	dim_256	dim_128	dim_64
cosine_accuracy@1	0.6871	0.6829	0.68	0.6586	0.6357
cosine_accuracy@3	0.8171	0.8114	0.8071	0.7943	0.7629
cosine_accuracy@5	0.8486	0.8529	0.8486	0.83	0.8143
cosine_accuracy@10	0.9086	0.9086	0.8957	0.8843	0.87
cosine_precision@1	0.6871	0.6829	0.68	0.6586	0.6357
cosine_precision@3	0.2724	0.2705	0.269	0.2648	0.2543
cosine_precision@5	0.1697	0.1706	0.1697	0.166	0.1629
cosine_precision@10	0.0909	0.0909	0.0896	0.0884	0.087
cosine_recall@1	0.6871	0.6829	0.68	0.6586	0.6357
cosine_recall@3	0.8171	0.8114	0.8071	0.7943	0.7629
cosine_recall@5	0.8486	0.8529	0.8486	0.83	0.8143
cosine_recall@10	0.9086	0.9086	0.8957	0.8843	0.87
cosine_ndcg@10	0.796	0.7937	0.7883	0.7728	0.7501
cosine_mrr@10	0.7604	0.7572	0.754	0.737	0.7121
cosine_map@100	0.7641	0.7609	0.7583	0.7419	0.7171

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: 8 tokens
mean: 45.84 tokens
max: 439 tokens

min: 7 tokens
mean: 20.62 tokens
max: 42 tokens

	positive	anchor
type	string	string
details	min: 8 tokens mean: 45.84 tokens max: 439 tokens	min: 7 tokens mean: 20.62 tokens max: 42 tokens

Samples:

positive	anchor
`We adopted SAB 121 during fiscal 2022, with no impact on our consolidated financial statements.`	`What accounting guidance did the company adopt in fiscal 2022 and what was its impact on the consolidated financial statements?`
`Mortgage Solutions revenue decreased 18% in 2023 compared to 2022, due to significantly lower mortgage credit inquiry volumes in 2023 compared to the prior year.`	`What caused the 18% decline in Mortgage Solutions revenue in 2023 compared to 2022?`
`Adoption of SBTi goals would build on our current science-based goals to reduce Scope 1 and 2 carbon emissions by 2.1% per year, to achieve a 40% reduction by the end of fiscal 2030 and a 50% reduction by the end of fiscal 2035.`	`What is the company's percentage target for reducing Scope 1 and 2 carbon emissions by end of fiscal 2035?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_eval_batch_size: 4
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: 4
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
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
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
0.1015	10	0.614	-	-	-	-	-
0.2030	20	0.5098	-	-	-	-	-
0.3046	30	0.426	-	-	-	-	-
0.4061	40	0.3262	-	-	-	-	-
0.5076	50	0.2131	-	-	-	-	-
0.6091	60	0.1892	-	-	-	-	-
0.7107	70	0.3049	-	-	-	-	-
0.8122	80	0.1617	-	-	-	-	-
0.9137	90	0.1214	-	-	-	-	-
1.0	99	-	0.7895	0.7919	0.7800	0.7685	0.7361
1.0102	100	0.147	-	-	-	-	-
1.1117	110	0.0938	-	-	-	-	-
1.2132	120	0.1406	-	-	-	-	-
1.3147	130	0.1058	-	-	-	-	-
1.4162	140	0.1072	-	-	-	-	-
1.5178	150	0.0352	-	-	-	-	-
1.6193	160	0.0568	-	-	-	-	-
1.7208	170	0.1283	-	-	-	-	-
1.8223	180	0.066	-	-	-	-	-
1.9239	190	0.038	-	-	-	-	-
2.0	198	-	0.7945	0.7945	0.7860	0.7736	0.7462
2.0203	200	0.0544	-	-	-	-	-
2.1218	210	0.0333	-	-	-	-	-
2.2234	220	0.042	-	-	-	-	-
2.3249	230	0.0489	-	-	-	-	-
2.4264	240	0.0498	-	-	-	-	-
2.5279	250	0.0119	-	-	-	-	-
2.6294	260	0.0273	-	-	-	-	-
2.7310	270	0.0719	-	-	-	-	-
2.8325	280	0.0366	-	-	-	-	-
2.9340	290	0.0333	-	-	-	-	-
3.0	297	-	0.7927	0.7952	0.7881	0.7743	0.7477
3.0305	300	0.0193	-	-	-	-	-
3.1320	310	0.0254	-	-	-	-	-
3.2335	320	0.0252	-	-	-	-	-
3.3350	330	0.039	-	-	-	-	-
3.4365	340	0.0224	-	-	-	-	-
3.5381	350	0.0091	-	-	-	-	-
3.6396	360	0.0356	-	-	-	-	-
3.7411	370	0.042	-	-	-	-	-
3.8426	380	0.038	-	-	-	-	-
3.9442	390	0.0088	-	-	-	-	-
3.9645	392	-	0.7960	0.7937	0.7883	0.7728	0.7501

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.8
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.5.1+cu124
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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

@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

@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}
}