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bge-base-finetuned-financial

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:6300
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
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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@100
widget:
  - source_sentence: >-
      FedEx supports the mental health and well-being of its employees and their
      household members by providing 24/7 confidential counseling services and
      frequently communicating with employees on how to access these resources,
      with an increased focus on mental health resources in recent years.
    sentences:
      - >-
        What are some of the key elements that management considers when making
        critical accounting estimates for Garmin?
      - >-
        How does FedEx support the mental health and well-being of its employees
        and their household members?
      - >-
        What was AbbVie's strategy for achieving its financial performance in
        2023?
  - source_sentence: >-
      Our tax returns are routinely audited and settlements of issues raised in
      these audits sometimes affect our tax provisions.
    sentences:
      - >-
        What was the total long-term debt, including the current portion, for
        AbbVie as of December 31, 2023?
      - How are tax returns affecting the company's tax provisions when audited?
      - >-
        What are the effective dates for the main provisions and additional data
        collection and reporting requirements of the final rule impacting AENB's
        compliance obligations?
  - source_sentence: >-
      In 2023, Machinery, Energy & Transportation held cash and cash equivalents
      amounting to $6,106 million, compared to $6,042 million in 2022.
    sentences:
      - >-
        How much cash and cash equivalents did Machinery, Energy &
        Transportation hold in 2023 compared to 2022?
      - >-
        As of the report's date, how does the company view the necessity of
        disclosing pending legal proceedings?
      - >-
        What strategies does the company use to mitigate increasing shipping
        costs?
  - source_sentence: >-
      As of December 31, 2023, the total amortized cost, net of valuation
      allowance, for non-U.S. government securities amounted to $14,516 million.
    sentences:
      - How did the combined ratio change from 2022 to 2023?
      - >-
        What changes occurred in the valuation of equity warrants from 2021 to
        2023?
      - >-
        What was the total amortized cost, net of valuation allowance, for
        non-U.S. government securities as of December 31, 2023?
  - source_sentence: Personal Systems net revenue was $35,684 million for the fiscal year 2023.
    sentences:
      - >-
        What was the total net revenue for the Personal Systems segment in the
        fiscal year 2023?
      - >-
        What are the revised maximum leverage ratios under the Senior Credit
        Facilities for the periods specified and in connection with certain
        material acquisitions?
      - >-
        What was the total net sales for the Dollar Tree segment in the year
        ended January 28, 2023?
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: 0.7071428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8285714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8657142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9042857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7071428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27619047619047615
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17314285714285713
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09042857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7071428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8285714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8657142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9042857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8089576129709927
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7781173469387753
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7818167550402533
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8357142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8671428571428571
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9114285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2785714285714286
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1734285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09114285714285712
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8357142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8671428571428571
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9114285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8092516903954083
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7763032879818597
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7797147792125239
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7028571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8357142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8628571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9014285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7028571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2785714285714286
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17257142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09014285714285714
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7028571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8357142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8628571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9014285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8068517806127258
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7762273242630382
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7800735216126475
            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: 0.8171428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8457142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8971428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.69
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2723809523809524
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16914285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0897142857142857
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.69
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8171428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8457142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8971428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7940646861464341
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7611541950113375
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7650200641460506
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6428571428571429
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7785714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.82
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.86
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6428571428571429
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2595238095238095
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16399999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.086
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6428571428571429
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7785714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.82
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.86
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7522449699920628
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7175958049886619
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7226733508592172
            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. Dataset - philschmid/finanical-rag-embedding-dataset

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

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("Nishanth7803/bge-base-finetuned-financial")
# Run inference
sentences = [
    'Personal Systems net revenue was $35,684 million for the fiscal year 2023.',
    'What was the total net revenue for the Personal Systems segment in the fiscal year 2023?',
    'What are the revised maximum leverage ratios under the Senior Credit Facilities for the periods specified and in connection with certain material acquisitions?',
]
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

Metric Value
cosine_accuracy@1 0.7071
cosine_accuracy@3 0.8286
cosine_accuracy@5 0.8657
cosine_accuracy@10 0.9043
cosine_precision@1 0.7071
cosine_precision@3 0.2762
cosine_precision@5 0.1731
cosine_precision@10 0.0904
cosine_recall@1 0.7071
cosine_recall@3 0.8286
cosine_recall@5 0.8657
cosine_recall@10 0.9043
cosine_ndcg@10 0.809
cosine_mrr@10 0.7781
cosine_map@100 0.7818

Information Retrieval

Metric Value
cosine_accuracy@1 0.7
cosine_accuracy@3 0.8357
cosine_accuracy@5 0.8671
cosine_accuracy@10 0.9114
cosine_precision@1 0.7
cosine_precision@3 0.2786
cosine_precision@5 0.1734
cosine_precision@10 0.0911
cosine_recall@1 0.7
cosine_recall@3 0.8357
cosine_recall@5 0.8671
cosine_recall@10 0.9114
cosine_ndcg@10 0.8093
cosine_mrr@10 0.7763
cosine_map@100 0.7797

Information Retrieval

Metric Value
cosine_accuracy@1 0.7029
cosine_accuracy@3 0.8357
cosine_accuracy@5 0.8629
cosine_accuracy@10 0.9014
cosine_precision@1 0.7029
cosine_precision@3 0.2786
cosine_precision@5 0.1726
cosine_precision@10 0.0901
cosine_recall@1 0.7029
cosine_recall@3 0.8357
cosine_recall@5 0.8629
cosine_recall@10 0.9014
cosine_ndcg@10 0.8069
cosine_mrr@10 0.7762
cosine_map@100 0.7801

Information Retrieval

Metric Value
cosine_accuracy@1 0.69
cosine_accuracy@3 0.8171
cosine_accuracy@5 0.8457
cosine_accuracy@10 0.8971
cosine_precision@1 0.69
cosine_precision@3 0.2724
cosine_precision@5 0.1691
cosine_precision@10 0.0897
cosine_recall@1 0.69
cosine_recall@3 0.8171
cosine_recall@5 0.8457
cosine_recall@10 0.8971
cosine_ndcg@10 0.7941
cosine_mrr@10 0.7612
cosine_map@100 0.765

Information Retrieval

Metric Value
cosine_accuracy@1 0.6429
cosine_accuracy@3 0.7786
cosine_accuracy@5 0.82
cosine_accuracy@10 0.86
cosine_precision@1 0.6429
cosine_precision@3 0.2595
cosine_precision@5 0.164
cosine_precision@10 0.086
cosine_recall@1 0.6429
cosine_recall@3 0.7786
cosine_recall@5 0.82
cosine_recall@10 0.86
cosine_ndcg@10 0.7522
cosine_mrr@10 0.7176
cosine_map@100 0.7227

Training Details

Training Dataset

philschmid/finanical-rag-embedding-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: 8 tokens
    • mean: 46.23 tokens
    • max: 289 tokens
    • min: 7 tokens
    • mean: 20.38 tokens
    • max: 41 tokens
  • Samples:
    positive anchor
    In addition, most group health plans and issuers of group or individual health insurance coverage are required to disclose personalized pricing information to their participants, beneficiaries, and enrollees through an online consumer tool, by phone, or in paper form, upon request. Cost estimates must be provided in real-time based on cost-sharing information that is accurate at the time of the request. What are the requirements for health insurers and group health plans in providing cost estimates to consumers?
    Gross profit energy generation and storage segment $
    In addition, eBay authenticates eligible luxury and collectible items in five categories through “Authenticity Guarantee”, an independent authentication service available in the United States, the United Kingdom, Germany, Australia and Canada. What does eBay's Authenticity Guarantee service offer?
  • 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
  • fp16: 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: 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: True
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: True
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch_fused
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: 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
0.8122 10 1.5914 - - - - -
0.9746 12 - 0.7520 0.7713 0.7706 0.6969 0.7753
1.6244 20 0.6901 - - - - -
1.9492 24 - 0.7616 0.7821 0.7799 0.7173 0.7795
2.4365 30 0.4967 - - - - -
2.9239 36 - 0.7643 0.7815 0.7801 0.7219 0.7817
3.2487 40 0.3894 - - - - -
3.8985 48 - 0.765 0.7801 0.7797 0.7227 0.7818
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.0.1
  • Transformers: 4.41.2
  • PyTorch: 2.3.0+cu121
  • Accelerate: 0.31.0
  • Datasets: 2.19.2
  • 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}
}