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: AutoZone, Inc. began operations in 1979.
    sentences:
      - >-
        What types of products and markets does the company cater to in the
        semiconductor industry?
      - When did AutoZone, Inc. begin its operations?
      - >-
        How much did general and administrative expenses related to merger,
        acquisition, and other costs change from 2022 to 2023?
  - source_sentence: >-
      See Note 14 to the consolidated financial statements in Item 8 of this
      Annual regarding legal proceedings.
    sentences:
      - >-
        What is the source to find detailed information about legal proceedings
        in the Annual Report?
      - >-
        Where in the Annual Report can one find a description of certain legal
        matters and their impact on the company?
      - >-
        What strategic actions is Hershey taking to maintain its leadership in
        the U.S. confectionery market?
  - source_sentence: >-
      ICE Bonds focuses on increasing efficiency in fixed income markets by
      offering electronic markets that support trading protocols including
      click-to-trade, request for quotation (RFQ), and auctions.
    sentences:
      - What services does the ICE Bonds platform provide and what is its focus?
      - >-
        What was the percentage increase in the generic dispensing rate of the
        Health Services segment from 2022 to 2023?
      - >-
        How many shares of Class A common stock were repurchased and retired in
        2023, and what was the total cost including excise tax accruals?
  - source_sentence: >-
      Subject to various United States and foreign laws and regulations,
      including those related to intellectual property, data privacy and
      security, cybersecurity, tax, employment, competition and antitrust,
      anti-corruption, anti-bribery, and AI. Compliance with these laws has no
      current material adverse impact on capital expenditures, results of
      operations or competitive position.
    sentences:
      - >-
        How much did the total loans and lending commitments amount to as of
        December 2023?
      - What types of laws and regulations does the company need to comply with?
      - >-
        Where are the consolidated financial statements listed in the Annual
        Report on Form 10-K located?
  - 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:
      - >-
        How does Tesla reduce costs and promote renewable power at their
        Supercharger stations?
      - >-
        What is the primary method by which the company manages its cash, cash
        equivalents, and marketable securities?
      - >-
        What significant regulatory change did CMS make to the hierarchical
        condition category model in its version 28?
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: 0.8442857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8814285714285715
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9271428571428572
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6985714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2814285714285714
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17628571428571424
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09271428571428571
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6985714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8442857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8814285714285715
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9271428571428572
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8156553778675095
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7796054421768707
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7822282461868646
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.71
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8457142857142858
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8785714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9271428571428572
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.71
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2819047619047619
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17571428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09271428571428571
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.71
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8457142857142858
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8785714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9271428571428572
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8194766272347418
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7848673469387758
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7873446316370609
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7085714285714285
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8342857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8642857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9142857142857143
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7085714285714285
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27809523809523806
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17285714285714282
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09142857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7085714285714285
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8342857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8642857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9142857142857143
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8116052646620258
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.77881462585034
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7821002568762089
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.69
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8271428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.86
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.91
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.69
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2757142857142857
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.172
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09099999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.69
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8271428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.86
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.91
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8013750432226047
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7664954648526079
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7698726210622817
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6657142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.79
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8285714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8857142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6657142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2633333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1657142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08857142857142855
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6657142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.79
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8285714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8857142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7732501027431213
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7375017006802721
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7416822153678694
            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("dpokhrel/bge-base-financial-matryoshka")
# Run inference
sentences = [
    '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.',
    'What significant regulatory change did CMS make to the hierarchical condition category model in its version 28?',
    'What is the primary method by which the company manages its cash, cash equivalents, and marketable securities?',
]
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	0.8443
cosine_accuracy@5	0.8814
cosine_accuracy@10	0.9271
cosine_precision@1	0.6986
cosine_precision@3	0.2814
cosine_precision@5	0.1763
cosine_precision@10	0.0927
cosine_recall@1	0.6986
cosine_recall@3	0.8443
cosine_recall@5	0.8814
cosine_recall@10	0.9271
cosine_ndcg@10	0.8157
cosine_mrr@10	0.7796
cosine_map@100	0.7822

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.71
cosine_accuracy@3	0.8457
cosine_accuracy@5	0.8786
cosine_accuracy@10	0.9271
cosine_precision@1	0.71
cosine_precision@3	0.2819
cosine_precision@5	0.1757
cosine_precision@10	0.0927
cosine_recall@1	0.71
cosine_recall@3	0.8457
cosine_recall@5	0.8786
cosine_recall@10	0.9271
cosine_ndcg@10	0.8195
cosine_mrr@10	0.7849
cosine_map@100	0.7873

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7086
cosine_accuracy@3	0.8343
cosine_accuracy@5	0.8643
cosine_accuracy@10	0.9143
cosine_precision@1	0.7086
cosine_precision@3	0.2781
cosine_precision@5	0.1729
cosine_precision@10	0.0914
cosine_recall@1	0.7086
cosine_recall@3	0.8343
cosine_recall@5	0.8643
cosine_recall@10	0.9143
cosine_ndcg@10	0.8116
cosine_mrr@10	0.7788
cosine_map@100	0.7821

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.69
cosine_accuracy@3	0.8271
cosine_accuracy@5	0.86
cosine_accuracy@10	0.91
cosine_precision@1	0.69
cosine_precision@3	0.2757
cosine_precision@5	0.172
cosine_precision@10	0.091
cosine_recall@1	0.69
cosine_recall@3	0.8271
cosine_recall@5	0.86
cosine_recall@10	0.91
cosine_ndcg@10	0.8014
cosine_mrr@10	0.7665
cosine_map@100	0.7699

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6657
cosine_accuracy@3	0.79
cosine_accuracy@5	0.8286
cosine_accuracy@10	0.8857
cosine_precision@1	0.6657
cosine_precision@3	0.2633
cosine_precision@5	0.1657
cosine_precision@10	0.0886
cosine_recall@1	0.6657
cosine_recall@3	0.79
cosine_recall@5	0.8286
cosine_recall@10	0.8857
cosine_ndcg@10	0.7733
cosine_mrr@10	0.7375
cosine_map@100	0.7417

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: 10 tokens
mean: 46.37 tokens
max: 248 tokens

min: 7 tokens
mean: 20.57 tokens
max: 51 tokens

	positive	anchor
type	string	string
details	min: 10 tokens mean: 46.37 tokens max: 248 tokens	min: 7 tokens mean: 20.57 tokens max: 51 tokens

Samples:

positive	anchor
`Scenario analysis is used to quantify the impact of a specified event, including how the event impacts multiple risk factors simultaneously. For example, for sovereign stress testing, it calculates potential exposure related to sovereign positions as well as the corresponding debt, equity, and currency exposures that may be impacted by sovereign distress.`	`How does Goldman Sachs utilize scenario analysis in its risk management strategy?`
`The company is involved in various other legal proceedings incidental to the conduct of our business, including, but not limited to, claims and allegations related to wage and hour violations, unlawful termination, employment practices, product liability, privacy and cybersecurity, environmental matters, and intellectual property rights or regulatory compliance.`	`What types of legal proceedings is the company currently involved in?`
`In 2023, $505 million was utilized for common stock repurchases.`	`How much cash was utilized for common stock repurchases in the year ended December 31, 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: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
half_precision_backend: cpu_amp
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: cpu_amp
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: 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	1.5241	-	-	-	-	-
0.9746	12	-	0.7486	0.7656	0.7662	0.7108	0.7679
1.6244	20	0.658	-	-	-	-	-
1.9492	24	-	0.7656	0.7793	0.7843	0.7348	0.7798
2.4365	30	0.4743	-	-	-	-	-
2.9239	36	-	0.7683	0.7814	0.7859	0.7400	0.7812
3.2487	40	0.4241	-	-	-	-	-
3.8985	48	-	0.7699	0.7821	0.7873	0.7417	0.7822

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.5
Sentence Transformers: 3.0.1
Transformers: 4.43.4
PyTorch: 2.4.0.dev20240607+cu118
Accelerate: 0.32.0
Datasets: 2.20.0
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}
}