metadata

base_model: BAAI/bge-base-en-v1.5
datasets: []
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: >-
      The sales contracts for Israel contain formulas that generally reflect an
      initial base price subject to price indexation, Brent-linked or other,
      over the life of the contract.
    sentences:
      - What was the change in HP's net deferred tax assets from 2022 to 2023?
      - What are the pricing mechanisms for crude oil sales contracts in Israel?
      - >-
        What was the total net income tax benefit HP received related to foreign
        tax audit matters?
  - source_sentence: >-
      The FCA imposes severe penalties for the knowing and improper retention of
      overpayments from government programs. In addition, the defendant must
      follow certain notification and repayment processes within 60 days of
      identifying and quantifying an overpayment.
    sentences:
      - What does Note 21 pertain to in this report?
      - >-
        What types of penalties does the FCA impose for the knowing and improper
        retention of overpayments from government payors?
      - >-
        What impact did discrete tax items have on the tax provision in 2023
        compared to 2022?
  - source_sentence: >-
      The expected long-term rate of return is evaluated on an annual basis. We
      consider a number of factors when setting assumptions with respect to the
      long-term rate of return, including current and expected asset allocation
      and historical and expected returns on the plan asset categories. Actual
      asset allocations are regularly reviewed and periodically rebalanced to
      the targeted allocations when considered appropriate.
    sentences:
      - How is the expected long-term rate of return on plan assets determined?
      - >-
        What is the accumulated benefit obligation for AT&T's pension plans as
        of December 31, 2023?
      - What is the management philosophy of Johnson & Johnson known as?
  - source_sentence: >-
      The functional currency of our foreign entities is the currency of the
      primary economic environment in which the entity operates.
    sentences:
      - >-
        By what percent did Other Income (Expense) change in 2023 compared to
        2022?
      - >-
        What are the Canadian class actions against Equifax seeking in relation
        to the 2017 cybersecurity incident?
      - What is the functional currency for a company's foreign entities?
  - source_sentence: >-
      Our products compete with other commercially available products based
      primarily on efficacy, safety, tolerability, acceptance by doctors, ease
      of patient compliance, ease of use, price, insurance and other
      reimbursement coverage, distribution and marketing.
    sentences:
      - >-
        What are the main factors influencing competition for the company's
        products?
      - >-
        What was the impact of restructuring charges in 2022 on the company and
        what changes occurred in 2023?
      - >-
        What are the penalties for non-compliance with Brazil's data protection
        laws?
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: 0.6985714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.83
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.88
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9257142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6985714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27666666666666667
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.176
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09257142857142854
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6985714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.83
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.88
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9257142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8141629079228132
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7782318594104309
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7807867705374557
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7014285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8328571428571429
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8857142857142857
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9228571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7014285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2776190476190476
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17714285714285713
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09228571428571428
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7014285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8328571428571429
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8857142857142857
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9228571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8133531244983723
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7781366213151925
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7808747462599953
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.84
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8714285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9085714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17428571428571427
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09085714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.84
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8714285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9085714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8077154994184018
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7749937641723353
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7785241448057054
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6942857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.82
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8557142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9028571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6942857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2733333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17114285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09028571428571427
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6942857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.82
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8557142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9028571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7990640908671799
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7658554421768706
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7697199109144424
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6614285714285715
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7842857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8271428571428572
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8885714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6614285714285715
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26142857142857145
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1654285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08885714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6614285714285715
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7842857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8271428571428572
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8885714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7730930913085324
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7365589569160996
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7404183138657333
            name: Cosine Map@100

BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. 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
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("felipehsilveira/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Our products compete with other commercially available products based primarily on efficacy, safety, tolerability, acceptance by doctors, ease of patient compliance, ease of use, price, insurance and other reimbursement coverage, distribution and marketing.',
    "What are the main factors influencing competition for the company's products?",
    'What was the impact of restructuring charges in 2022 on the company and what changes occurred in 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

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6986
cosine_accuracy@3	0.83
cosine_accuracy@5	0.88
cosine_accuracy@10	0.9257
cosine_precision@1	0.6986
cosine_precision@3	0.2767
cosine_precision@5	0.176
cosine_precision@10	0.0926
cosine_recall@1	0.6986
cosine_recall@3	0.83
cosine_recall@5	0.88
cosine_recall@10	0.9257
cosine_ndcg@10	0.8142
cosine_mrr@10	0.7782
cosine_map@100	0.7808

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7014
cosine_accuracy@3	0.8329
cosine_accuracy@5	0.8857
cosine_accuracy@10	0.9229
cosine_precision@1	0.7014
cosine_precision@3	0.2776
cosine_precision@5	0.1771
cosine_precision@10	0.0923
cosine_recall@1	0.7014
cosine_recall@3	0.8329
cosine_recall@5	0.8857
cosine_recall@10	0.9229
cosine_ndcg@10	0.8134
cosine_mrr@10	0.7781
cosine_map@100	0.7809

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7
cosine_accuracy@3	0.84
cosine_accuracy@5	0.8714
cosine_accuracy@10	0.9086
cosine_precision@1	0.7
cosine_precision@3	0.28
cosine_precision@5	0.1743
cosine_precision@10	0.0909
cosine_recall@1	0.7
cosine_recall@3	0.84
cosine_recall@5	0.8714
cosine_recall@10	0.9086
cosine_ndcg@10	0.8077
cosine_mrr@10	0.775
cosine_map@100	0.7785

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6943
cosine_accuracy@3	0.82
cosine_accuracy@5	0.8557
cosine_accuracy@10	0.9029
cosine_precision@1	0.6943
cosine_precision@3	0.2733
cosine_precision@5	0.1711
cosine_precision@10	0.0903
cosine_recall@1	0.6943
cosine_recall@3	0.82
cosine_recall@5	0.8557
cosine_recall@10	0.9029
cosine_ndcg@10	0.7991
cosine_mrr@10	0.7659
cosine_map@100	0.7697

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6614
cosine_accuracy@3	0.7843
cosine_accuracy@5	0.8271
cosine_accuracy@10	0.8886
cosine_precision@1	0.6614
cosine_precision@3	0.2614
cosine_precision@5	0.1654
cosine_precision@10	0.0889
cosine_recall@1	0.6614
cosine_recall@3	0.7843
cosine_recall@5	0.8271
cosine_recall@10	0.8886
cosine_ndcg@10	0.7731
cosine_mrr@10	0.7366
cosine_map@100	0.7404

Training Details

Training Dataset

Unnamed Dataset

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: 45.44 tokens
max: 301 tokens

min: 7 tokens
mean: 20.3 tokens
max: 51 tokens

	positive	anchor
type	string	string
details	min: 6 tokens mean: 45.44 tokens max: 301 tokens	min: 7 tokens mean: 20.3 tokens max: 51 tokens

Samples:

positive	anchor
`The Centers for Medicare & Medicaid Services issued a final rule in October 2023 for the calendar year 2024, estimating a productivity-adjusted market basket increase of 2.1% in average reimbursement to ESRD facilities.`	`What is the projected impact on average reimbursement to ESRD facilities in 2024 due to the final rule issued by CMS?`
`Company Adjusted EBIT Margin is derived by dividing the Company adjusted EBIT by Company revenue, which is a non-GAAP measure useful for evaluating the company's operating results.`	`How is the Company Adjusted EBIT Margin calculated?`
`The provision for credit losses was $4 million for the year ended December 31, 202 serviLists of account holders responsible for and the state of the economy, our credit standards, our risk assessments, and the judgment of our employees responsible for granting credit.`	`What factors influence the provision for credit losses at Las Vegas Sands Corp.?`

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: 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}
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
eval_on_start: False
eval_use_gather_object: 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
0.8122	10	1.5176	-	-	-	-	-
0.9746	12	-	0.7500	0.7642	0.7680	0.7079	0.7708
1.6244	20	0.6868	-	-	-	-	-
1.9492	24	-	0.7657	0.7746	0.7784	0.7323	0.7816
2.4365	30	0.4738	-	-	-	-	-
2.9239	36	-	0.7691	0.7780	0.7790	0.7402	0.7796
3.2487	40	0.3934	-	-	-	-	-
3.8985	48	-	0.7697	0.7785	0.7809	0.7404	0.7808

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.9
Sentence Transformers: 3.0.1
Transformers: 4.44.2
PyTorch: 2.4.0+cu121
Accelerate: 0.33.0
Datasets: 2.21.0
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}
}