metadata

language:
  - en
license: apache-2.0
library_name: sentence-transformers
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
datasets: []
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@10
widget:
  - source_sentence: >-
      The Gross Merchandise Sales (GMS) decreased by 1.2% in 2023 compared to
      2022.
    sentences:
      - What specific matters did the CFPB investigate concerning Equifax?
      - >-
        What was the percentage decline in GMS for the year ended December 31,
        2023 compared to 2022?
      - >-
        What percentage of eBay's 2023 net revenues were attributed to
        international markets?
  - source_sentence: >-
      Asset management and administration fees vary with changes in the balances
      of client assets due to market fluctuations and client activity.
    sentences:
      - >-
        Why was there a net outflow of cash in financing activities in fiscal
        2022?
      - >-
        How do asset management and administration fees vary at The Charles
        Schwab Corporation?
      - What are some key goals of the corporation related to climate change?
  - source_sentence: >-
      Operating profit margin was 19.3 percent in 2023, compared with 13.3
      percent in 2022.
    sentences:
      - What was the operating profit margin for 2023?
      - How do the studios compete in the entertainment industry?
      - >-
        What types of audio products does Garmin's Fusion and JL Audio brands
        offer?
  - source_sentence: >-
      Subsequent to 2023, on February 12, 2024, AbbVie borrowed $5.0 billion
      under the term loan credit agreement.
    sentences:
      - >-
        What percentage of U.S. dialysis patient service revenues in 2023 came
        from Medicare and Medicare Advantage plans?
      - >-
        What is Peloton Interactive, Inc. known for in the interactive fitness
        industry?
      - >-
        What was the purpose stated by AbbVie for borrowing $5.0 billion under
        the term loan credit agreement on February 12, 2024?
  - source_sentence: >-
      Chipotle retains an independent third-party compensation consultant each
      year to conduct a pay equity analysis of its U.S. and Canadian workforce,
      including factors of pay such as grade level, tenure in role, and external
      market conditions like geographic location, to ensure consistency and
      equitable treatment among employees.
    sentences:
      - How does Chipotle ensure pay equity among its employees?
      - >-
        How can one locate information on legal proceedings within the
        Consolidated Financial Statements?
      - >-
        What criteria did the independent audit use to assess the effectiveness
        of internal control over financial reporting at the company?
pipeline_tag: sentence-similarity
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: 0.8214285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8585714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6871428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27380952380952384
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1717142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6871428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8214285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8585714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7966931280955273
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7633656462585031
            name: Cosine Mrr@10
          - type: cosine_map@10
            value: 0.7633656462585034
            name: Cosine Map@10
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6857142857142857
            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: 0.9014285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6857142857142857
            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: 0.09014285714285712
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6857142857142857
            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: 0.9014285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7951662657569053
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.761045918367347
            name: Cosine Mrr@10
          - type: cosine_map@10
            value: 0.761045918367347
            name: Cosine Map@10
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6814285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8171428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8571428571428571
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8885714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6814285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2723809523809524
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17142857142857137
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08885714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6814285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8171428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8571428571428571
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8885714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7890567420578879
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7567375283446709
            name: Cosine Mrr@10
          - type: cosine_map@10
            value: 0.7567375283446711
            name: Cosine Map@10
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6571428571428571
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8071428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8457142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8742857142857143
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6571428571428571
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26904761904761904
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16914285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08742857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6571428571428571
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8071428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8457142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8742857142857143
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7723888716536037
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7390544217687071
            name: Cosine Mrr@10
          - type: cosine_map@10
            value: 0.7390544217687074
            name: Cosine Map@10
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6157142857142858
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7685714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8171428571428572
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8557142857142858
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6157142857142858
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2561904761904762
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1634285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08557142857142856
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6157142857142858
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7685714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8171428571428572
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8557142857142858
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7405386424360808
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7031672335600904
            name: Cosine Mrr@10
          - type: cosine_map@10
            value: 0.7031672335600907
            name: Cosine Map@10

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("Sailesh9999/bge-base-financial-matryoshka_3")
# Run inference
sentences = [
    'Chipotle retains an independent third-party compensation consultant each year to conduct a pay equity analysis of its U.S. and Canadian workforce, including factors of pay such as grade level, tenure in role, and external market conditions like geographic location, to ensure consistency and equitable treatment among employees.',
    'How does Chipotle ensure pay equity among its employees?',
    'How can one locate information on legal proceedings within the Consolidated Financial Statements?',
]
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	0.6871
cosine_accuracy@3	0.8214
cosine_accuracy@5	0.8586
cosine_accuracy@10	0.9
cosine_precision@1	0.6871
cosine_precision@3	0.2738
cosine_precision@5	0.1717
cosine_precision@10	0.09
cosine_recall@1	0.6871
cosine_recall@3	0.8214
cosine_recall@5	0.8586
cosine_recall@10	0.9
cosine_ndcg@10	0.7967
cosine_mrr@10	0.7634
cosine_map@10	0.7634

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6857
cosine_accuracy@3	0.82
cosine_accuracy@5	0.8557
cosine_accuracy@10	0.9014
cosine_precision@1	0.6857
cosine_precision@3	0.2733
cosine_precision@5	0.1711
cosine_precision@10	0.0901
cosine_recall@1	0.6857
cosine_recall@3	0.82
cosine_recall@5	0.8557
cosine_recall@10	0.9014
cosine_ndcg@10	0.7952
cosine_mrr@10	0.761
cosine_map@10	0.761

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6814
cosine_accuracy@3	0.8171
cosine_accuracy@5	0.8571
cosine_accuracy@10	0.8886
cosine_precision@1	0.6814
cosine_precision@3	0.2724
cosine_precision@5	0.1714
cosine_precision@10	0.0889
cosine_recall@1	0.6814
cosine_recall@3	0.8171
cosine_recall@5	0.8571
cosine_recall@10	0.8886
cosine_ndcg@10	0.7891
cosine_mrr@10	0.7567
cosine_map@10	0.7567

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6571
cosine_accuracy@3	0.8071
cosine_accuracy@5	0.8457
cosine_accuracy@10	0.8743
cosine_precision@1	0.6571
cosine_precision@3	0.269
cosine_precision@5	0.1691
cosine_precision@10	0.0874
cosine_recall@1	0.6571
cosine_recall@3	0.8071
cosine_recall@5	0.8457
cosine_recall@10	0.8743
cosine_ndcg@10	0.7724
cosine_mrr@10	0.7391
cosine_map@10	0.7391

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6157
cosine_accuracy@3	0.7686
cosine_accuracy@5	0.8171
cosine_accuracy@10	0.8557
cosine_precision@1	0.6157
cosine_precision@3	0.2562
cosine_precision@5	0.1634
cosine_precision@10	0.0856
cosine_recall@1	0.6157
cosine_recall@3	0.7686
cosine_recall@5	0.8171
cosine_recall@10	0.8557
cosine_ndcg@10	0.7405
cosine_mrr@10	0.7032
cosine_map@10	0.7032

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: 7 tokens
mean: 46.55 tokens
max: 439 tokens

min: 9 tokens
mean: 20.43 tokens
max: 46 tokens

	positive	anchor
type	string	string
details	min: 7 tokens mean: 46.55 tokens max: 439 tokens	min: 9 tokens mean: 20.43 tokens max: 46 tokens

Samples:

positive	anchor
`Americas`	$
`Item 1 Business typically includes detailed information about the organization's operations, the nature of the business, and its strategic direction.`	`What is the title of the section that potentially discusses the operations or nature of a business in a document?`
`Operating expenses as a percentage of total revenues decreased to 15.3% in 2023 compared to 15.9% in 2022.`	`What was the operating expenses as a percentage of total revenues 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: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 1e-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
learning_rate: 1e-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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@10	dim_256_cosine_map@10	dim_512_cosine_map@10	dim_64_cosine_map@10	dim_768_cosine_map@10
0.8122	10	1.7427	-	-	-	-	-
0.9746	12	-	0.7118	0.7377	0.7411	0.6774	0.7440
1.6244	20	0.9354	-	-	-	-	-
1.9492	24	-	0.7353	0.7544	0.7562	0.7008	0.7632
2.4365	30	0.674	-	-	-	-	-
2.9239	36	-	0.7382	0.7569	0.7612	0.7018	0.7625
3.2487	40	0.5862	-	-	-	-	-
3.8985	48	-	0.7391	0.7567	0.761	0.7032	0.7634

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.9.18
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.29.3
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}
}