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: >-
      Favorable resolution of tax positions would be recognized as a reduction
      to the effective income tax rate in the period of resolution.
    sentences:
      - What was the operational trend for voice connections from 2021 to 2023?
      - >-
        How is a favorable resolution of a tax position recognized in financial
        terms?
      - >-
        What was the amount of cash generated from operations by the company in
        fiscal year 2023?
  - source_sentence: >-
      The cumulative basis adjustments associated with these hedging
      relationships are a reduction of the amortized cost basis of the closed
      portfolios of $19 million.
    sentences:
      - >-
        What was the reduction in the amortized cost basis of the closed
        portfolios due to cumulative basis adjustments in these hedging
        relationships?
      - >-
        How is the inclusion of the financial statements in the IBM's Form 10-K
        described?
      - >-
        What was the percentage increase in Electronic Arts' diluted earnings
        per share in the fiscal year ended March 31, 2023?
  - source_sentence: >-
      Walmart's fintech venture, ONE, provides financial services such as money
      orders, prepaid access, money transfers, check cashing, bill payment, and
      certain types of installment lending.
    sentences:
      - >-
        What types of financial services are offered through Walmart's fintech
        venture, ONE?
      - How much cash did FedEx have at the end of May 2023?
      - What is the purpose of Visa according to the overview provided?
  - source_sentence: >-
      Medicare Star Ratings - A portion of each Medicare Advantage plan’s
      reimbursement is tied to the plan’s “star ratings.” The star rating system
      considers a variety of measures adopted by CMS, including quality of
      preventative services, chronic illness management, compliance and overall
      customer satisfaction. Only Medicare Advantage plans with an overall star
      rating of 4 or more stars (out of 5 stars) are eligible for a quality
      bonus in their basic premium rates.
    sentences:
      - >-
        What authority does the Macao government have over VML's
        recapitalization plans?
      - >-
        How do Medicare Advantage star ratings impact the financial
        reimbursements of plans?
      - >-
        How are the adjusted income from operations and gross profit figures
        calculated for the company?
  - source_sentence: >-
      For the year ended December 31, 2022, the free cash flow reported was
      -$11,569 million.
    sentences:
      - What new initiative did Dollar Tree announce in September 2021?
      - >-
        What was the amount of deferred net loss on derivatives included in
        accumulated other comprehensive income as of December 31, 2023?
      - >-
        What was the free cash flow reported for the year ended December 31,
        2022?
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: 0.7028571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8242857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8585714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8942857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7028571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2747619047619047
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1717142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08942857142857143
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7028571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8242857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8585714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8942857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8013128307721423
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7712681405895693
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7753497571186561
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7071428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8228571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8571428571428571
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8928571428571429
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7071428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2742857142857143
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1714285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08928571428571427
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7071428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8228571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8571428571428571
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8928571428571429
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8020949349247011
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7728367346938774
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7769793740668877
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7057142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8185714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.85
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8914285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7057142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27285714285714285
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16999999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08914285714285713
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7057142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8185714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.85
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8914285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7985100284142371
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.768848072562358
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7728585204433037
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6842857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8085714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8442857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8842857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6842857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26952380952380955
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16885714285714284
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08842857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6842857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8085714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8442857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8842857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7851722154382534
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.753300453514739
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7577938812506425
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6685714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.78
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8114285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8628571428571429
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6685714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16228571428571426
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08628571428571427
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6685714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.78
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8114285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8628571428571429
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7647477473058039
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7335680272108841
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7387414091286255
            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("kenoc/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'For the year ended December 31, 2022, the free cash flow reported was -$11,569 million.',
    'What was the free cash flow reported for the year ended December 31, 2022?',
    'What was the amount of deferred net loss on derivatives included in accumulated other comprehensive income 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	0.7029	0.7071	0.7057	0.6843	0.6686
cosine_accuracy@3	0.8243	0.8229	0.8186	0.8086	0.78
cosine_accuracy@5	0.8586	0.8571	0.85	0.8443	0.8114
cosine_accuracy@10	0.8943	0.8929	0.8914	0.8843	0.8629
cosine_precision@1	0.7029	0.7071	0.7057	0.6843	0.6686
cosine_precision@3	0.2748	0.2743	0.2729	0.2695	0.26
cosine_precision@5	0.1717	0.1714	0.17	0.1689	0.1623
cosine_precision@10	0.0894	0.0893	0.0891	0.0884	0.0863
cosine_recall@1	0.7029	0.7071	0.7057	0.6843	0.6686
cosine_recall@3	0.8243	0.8229	0.8186	0.8086	0.78
cosine_recall@5	0.8586	0.8571	0.85	0.8443	0.8114
cosine_recall@10	0.8943	0.8929	0.8914	0.8843	0.8629
cosine_ndcg@10	0.8013	0.8021	0.7985	0.7852	0.7647
cosine_mrr@10	0.7713	0.7728	0.7688	0.7533	0.7336
cosine_map@100	0.7753	0.777	0.7729	0.7578	0.7387

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: 2 tokens
mean: 46.63 tokens
max: 439 tokens

min: 2 tokens
mean: 20.63 tokens
max: 45 tokens

	positive	anchor
type	string	string
details	min: 2 tokens mean: 46.63 tokens max: 439 tokens	min: 2 tokens mean: 20.63 tokens max: 45 tokens

Samples:

positive	anchor
`During fiscal year 2023, 276 billion payments and cash transactions with Visa’s brand were processed by Visa or other networks.`	`What significant milestone of transactions did Visa reach during fiscal year 2023?`
`The AMPTC for microinverters decreases by 25% each year beginning in 2030 and ending after 2032.`	`What is the trajectory of the AMPTC for microinverters starting in 2030?`
`Revenue increased in 2023 driven by increased volume and higher realized prices. The increase in revenue in 2023 was primarily driven by sales of Mounjaro®, Verzenio®, Jardiance®, as well as the sales of the rights for the olanzapine portfolio, including Zyprexa®, and for Baqsimi®, partially offset by the absence of revenue from COVID-19 antibodies and lower sales of Alimta® following the entry of multiple generics in the first half of 2022.`	`What factors contributed to the increase in revenue in 2023?`

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_train_batch_size: 16
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
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: 16
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: None
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
0.2030	10	1.1555	-	-	-	-	-
0.4061	20	0.7503	-	-	-	-	-
0.6091	30	0.4782	-	-	-	-	-
0.8122	40	0.3436	-	-	-	-	-
1.0	50	0.361	0.7942	0.7943	0.7905	0.7770	0.7428
1.2030	60	0.3078	-	-	-	-	-
1.4061	70	0.2375	-	-	-	-	-
1.6091	80	0.1683	-	-	-	-	-
1.8122	90	0.1412	-	-	-	-	-
2.0	100	0.1431	0.7994	0.8003	0.7980	0.7828	0.7577
2.2030	110	0.1308	-	-	-	-	-
2.4061	120	0.1188	-	-	-	-	-
2.6091	130	0.0952	-	-	-	-	-
2.8122	140	0.0806	-	-	-	-	-
3.0	150	0.0832	0.8019	0.8009	0.7983	0.7844	0.7660
3.2030	160	0.1044	-	-	-	-	-
3.4061	170	0.0984	-	-	-	-	-
3.6091	180	0.0838	-	-	-	-	-
3.8122	190	0.0768	-	-	-	-	-
3.934	196	-	0.8013	0.8021	0.7985	0.7852	0.7647

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.5.1+cu121
Accelerate: 1.2.1
Datasets: 2.19.2
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}
}