metadata

base_model: BAAI/bge-base-en-v1.5
language:
  - en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:6300
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      In the Annual Report on Form 10-K, the consolidated financial statements
      are included immediately following Part IV and incorporated by reference.
    sentences:
      - >-
        What movies contributed to higher revenue in 2023 compared to the
        previous year?
      - How are the financial statements incorporated in the 10-K report?
      - >-
        What was the ending store count for the Family Dollar segment after the
        fiscal year ended January 28, 2023?
  - source_sentence: >-
      Readers are cautioned not to place undue reliance on forward-looking
      statements, which speak only as of the date they are made. We undertake no
      obligation to update or revise publicly any forward-looking statements,
      whether because of new information, future events, or otherwise.
    sentences:
      - >-
        What impact did the IRS deadline extension in 2023 have on Intuit's
        fiscal results?
      - >-
        What risks are associated with relying on forward-looking statements
        according to the provided text?
      - >-
        What were the total minimum lease payments and their net amounts after
        imputed interest for operating and finance leases as of January 31,
        2023?
  - source_sentence: >-
      CMS made significant changes to the structure of the hierarchical
      condition category model in version 28, which may impact risk adjustment
      factor scores for a larger percentage of Medicare Advantage beneficiaries
      and could result in changes to beneficiary RAF scores with or without a
      change in the patient’s health status.
    sentences:
      - >-
        What significant regulatory change did CMS make to the hierarchical
        condition category model in its version 28?
      - >-
        Which section of IBM’s 2023 Annual Report is reserved for Financial
        Statements and Supplementary Data?
      - What strategic goals are set for the Printing segment at HP Inc.?
  - source_sentence: >-
      In December 2023, the FCA published a consultation proposing to revise the
      U.K. commodity derivatives framework. The FSMA 2023 reformed the U.K.’s
      commodity derivatives regulatory regime including revoking the MIFID II
      position limit requirements and transferring the powers to set position
      limits and controls from the FCA to the operator of trading venues. The
      FCA proposal requires U.K. trading venues to set position limits for
      critical and related contracts, to establish accountability thresholds and
      to report enhanced position data.
    sentences:
      - >-
        What was the percentage increase in revenues from aviation services in
        2023 compared to 2022?
      - >-
        What was the impairment loss recognized by the Company due to TDA
        integration and restructuring efforts for the year ending December 31,
        2023?
      - >-
        What changes did the FCA propose in its December 2023 consultation
        regarding the U.K. commodity derivatives framework?
  - source_sentence: >-
      Operating cash flow provides the primary source of cash to fund operating
      needs and capital expenditures.
    sentences:
      - >-
        What is the primary source of cash used by the company to fund operating
        needs and capital expenditures?
      - >-
        What kinds of products and services does the Company provide under the
        AARP Program?
      - >-
        What was the total assets under supervision (AUS) for all categories
        combined in 2023?
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: 0.7128571428571429
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8385714285714285
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8657142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9128571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7128571428571429
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27952380952380956
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17314285714285713
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09128571428571428
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7128571428571429
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8385714285714285
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8657142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9128571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8160752408699454
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7850544217687072
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7883813094771759
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7085714285714285
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8314285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8571428571428571
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.91
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7085714285714285
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27714285714285714
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1714285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.091
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7085714285714285
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8314285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8571428571428571
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.91
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.810046642542136
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7782335600907029
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7817400926898996
            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: 0.8214285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8614285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8957142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7057142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2738095238095238
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17228571428571426
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08957142857142855
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7057142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8214285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8614285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8957142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.803237369609097
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7734654195011333
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7778038646628423
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6871428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8085714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8428571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8942857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6871428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2695238095238095
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16857142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08942857142857143
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6871428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8085714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8428571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8942857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7913904723614839
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7585782312925171
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.762610071156596
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.66
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7714285714285715
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8085714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8714285714285714
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.66
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2571428571428571
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1617142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08714285714285713
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.66
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7714285714285715
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8085714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8714285714285714
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7614379134484182
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7269172335600907
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7319569628864667
            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 tokens
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("ValentinaKim/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Operating cash flow provides the primary source of cash to fund operating needs and capital expenditures.',
    'What is the primary source of cash used by the company to fund operating needs and capital expenditures?',
    'What kinds of products and services does the Company provide under the AARP Program?',
]
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

Metric	Value
cosine_accuracy@1	0.7129
cosine_accuracy@3	0.8386
cosine_accuracy@5	0.8657
cosine_accuracy@10	0.9129
cosine_precision@1	0.7129
cosine_precision@3	0.2795
cosine_precision@5	0.1731
cosine_precision@10	0.0913
cosine_recall@1	0.7129
cosine_recall@3	0.8386
cosine_recall@5	0.8657
cosine_recall@10	0.9129
cosine_ndcg@10	0.8161
cosine_mrr@10	0.7851
cosine_map@100	0.7884

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7086
cosine_accuracy@3	0.8314
cosine_accuracy@5	0.8571
cosine_accuracy@10	0.91
cosine_precision@1	0.7086
cosine_precision@3	0.2771
cosine_precision@5	0.1714
cosine_precision@10	0.091
cosine_recall@1	0.7086
cosine_recall@3	0.8314
cosine_recall@5	0.8571
cosine_recall@10	0.91
cosine_ndcg@10	0.81
cosine_mrr@10	0.7782
cosine_map@100	0.7817

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7057
cosine_accuracy@3	0.8214
cosine_accuracy@5	0.8614
cosine_accuracy@10	0.8957
cosine_precision@1	0.7057
cosine_precision@3	0.2738
cosine_precision@5	0.1723
cosine_precision@10	0.0896
cosine_recall@1	0.7057
cosine_recall@3	0.8214
cosine_recall@5	0.8614
cosine_recall@10	0.8957
cosine_ndcg@10	0.8032
cosine_mrr@10	0.7735
cosine_map@100	0.7778

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6871
cosine_accuracy@3	0.8086
cosine_accuracy@5	0.8429
cosine_accuracy@10	0.8943
cosine_precision@1	0.6871
cosine_precision@3	0.2695
cosine_precision@5	0.1686
cosine_precision@10	0.0894
cosine_recall@1	0.6871
cosine_recall@3	0.8086
cosine_recall@5	0.8429
cosine_recall@10	0.8943
cosine_ndcg@10	0.7914
cosine_mrr@10	0.7586
cosine_map@100	0.7626

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.66
cosine_accuracy@3	0.7714
cosine_accuracy@5	0.8086
cosine_accuracy@10	0.8714
cosine_precision@1	0.66
cosine_precision@3	0.2571
cosine_precision@5	0.1617
cosine_precision@10	0.0871
cosine_recall@1	0.66
cosine_recall@3	0.7714
cosine_recall@5	0.8086
cosine_recall@10	0.8714
cosine_ndcg@10	0.7614
cosine_mrr@10	0.7269
cosine_map@100	0.732

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: 45.81 tokens
max: 439 tokens

min: 7 tokens
mean: 20.26 tokens
max: 43 tokens

	positive	anchor
type	string	string
details	min: 6 tokens mean: 45.81 tokens max: 439 tokens	min: 7 tokens mean: 20.26 tokens max: 43 tokens

Samples:

positive	anchor
`For the year ended December 31, 2023, Alphabet Inc. reported a net cash provided by operating activities of $101,746 million.`	`What was the net cash provided by operating activities for Alphabet Inc. in 2023?`
`Our History In 2000, ICE was founded with the idea of transforming energy markets by creating a network that removed barriers and provided greater transparency, efficiency and access.`	`When was Intercontinental Exchange, Inc. founded, and what was its initial focus?`
`Item 8. Financial Statements and Supplementary Data The index to Financial Statements and Supplementary Data is presented`	`What is presented in Item 8 according to Financial Statements and Supplementary Data?`

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
gradient_accumulation_steps: 32
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
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: 16
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 32
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
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: False
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: 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
batch_eval_metrics: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.9746	6	-	0.7258	0.7501	0.7513	0.6860	0.7589
1.6244	10	1.4436	-	-	-	-	-
1.9492	12	-	0.7494	0.7733	0.7800	0.7187	0.7827
2.9239	18	-	0.7601	0.7796	0.7813	0.7312	0.7897
3.2487	20	0.6159	-	-	-	-	-
3.8985	24	-	0.7626	0.7778	0.7817	0.732	0.7884

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.1.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.34.2
Datasets: 2.19.1
Tokenizers: 0.19.1

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}
}