README.md

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 table indicates that 18,000 deferred shares were granted to
      non-employee directors in fiscal 2020, 15,000 in fiscal 2021, and 19,000
      in fiscal 2022.
    sentences:
      - >-
        What was the primary reason for the increased audit effort for PCC
        goodwill and indefinite-lived intangible assets?
      - >-
        How many deferred shares were granted to non-employee directors in
        fiscal 2020, 2021, and 2022?
      - >-
        What was the total intrinsic value of options exercised in fiscal year
        2023?
  - source_sentence: >-
      In Resource Masking Industries, we expect the profit impact from lower
      sales volume to be partially offset by favorable price realization.
    sentences:
      - >-
        By what percentage did Electronic Arts' operating income grow in the
        fiscal year ended March 31, 2023?
      - >-
        What impact is expected on Resource Industries' profit due to lower
        sales volume?
      - >-
        How are IBM’s 2023 Annual Report to Stockholders' financial statements
        made a part of Form 10-K?
  - source_sentence: >-
      The actuarial gain during the year ended December 31, 2022 was primarily
      related to increases in the discount rate assumptions.
    sentences:
      - >-
        What was the primary reason for the actuarial gain during the year ended
        December 31, 2022?
      - How much did Ford's total assets amount to by December 31, 2023?
      - >-
        What was the remaining available amount of the share repurchase
        authorization as of January 29, 2023?
  - source_sentence: >-
      Returned $1.7 billion to shareholders through share repurchases and
      dividend payments.
    sentences:
      - >-
        What was the carrying amount of investments without readily determinable
        fair values as of December 31, 2023?
      - >-
        What are the significant inputs to the valuation of Goldman Sachs'
        unsecured short- and long-term borrowings?
      - >-
        How much did the company return to shareholders through share
        repurchases and dividend payments in 2022?
  - source_sentence: >-
      The remaining amount available for borrowing under the Revolving Credit
      Facility as of December 31, 2023, was $2,245.2 million.
    sentences:
      - >-
        What was the total amount available for borrowing under the Revolving
        Credit Facility at Iron Mountain as of December 31, 2023?
      - >-
        What type of information is included in Note 13 of the Annual Report on
        Form 10-K?
      - >-
        How much did local currency revenue increase in Latin America in 2023
        compared to 2022?
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: 0.6828571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8242857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8557142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9057142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6828571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2747619047619047
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17114285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09057142857142855
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6828571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8242857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8557142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9057142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7963610970343802
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7612930839002267
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7648513048205645
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.68
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8157142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8542857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.68
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27190476190476187
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17085714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.68
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8157142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8542857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7911616934987842
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7562284580498863
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.760087172570928
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.68
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8114285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8485714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8971428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.68
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2704761904761905
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16971428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0897142857142857
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.68
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8114285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8485714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8971428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7888581850866868
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7542278911564625
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7579536807505182
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6571428571428571
            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: 0.6571428571428571
            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: 0.08857142857142856
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6571428571428571
            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: 0.7703812626851927
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.733632653061224
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7378840513025602
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.62
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.77
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8028571428571428
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.85
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.62
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.25666666666666665
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16057142857142856
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.085
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.62
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.77
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8028571428571428
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.85
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.73777886683529
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7016190476190474
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7073607864232172
            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("moritzglnr/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'The remaining amount available for borrowing under the Revolving Credit Facility as of December 31, 2023, was $2,245.2 million.',
    'What was the total amount available for borrowing under the Revolving Credit Facility at Iron Mountain as of December 31, 2023?',
    'What type of information is included in Note 13 of the Annual Report on Form 10-K?',
]
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	0.6829
cosine_accuracy@3	0.8243
cosine_accuracy@5	0.8557
cosine_accuracy@10	0.9057
cosine_precision@1	0.6829
cosine_precision@3	0.2748
cosine_precision@5	0.1711
cosine_precision@10	0.0906
cosine_recall@1	0.6829
cosine_recall@3	0.8243
cosine_recall@5	0.8557
cosine_recall@10	0.9057
cosine_ndcg@10	0.7964
cosine_mrr@10	0.7613
cosine_map@100	0.7649

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.68
cosine_accuracy@3	0.8157
cosine_accuracy@5	0.8543
cosine_accuracy@10	0.9
cosine_precision@1	0.68
cosine_precision@3	0.2719
cosine_precision@5	0.1709
cosine_precision@10	0.09
cosine_recall@1	0.68
cosine_recall@3	0.8157
cosine_recall@5	0.8543
cosine_recall@10	0.9
cosine_ndcg@10	0.7912
cosine_mrr@10	0.7562
cosine_map@100	0.7601

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.68
cosine_accuracy@3	0.8114
cosine_accuracy@5	0.8486
cosine_accuracy@10	0.8971
cosine_precision@1	0.68
cosine_precision@3	0.2705
cosine_precision@5	0.1697
cosine_precision@10	0.0897
cosine_recall@1	0.68
cosine_recall@3	0.8114
cosine_recall@5	0.8486
cosine_recall@10	0.8971
cosine_ndcg@10	0.7889
cosine_mrr@10	0.7542
cosine_map@100	0.758

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6571
cosine_accuracy@3	0.79
cosine_accuracy@5	0.8286
cosine_accuracy@10	0.8857
cosine_precision@1	0.6571
cosine_precision@3	0.2633
cosine_precision@5	0.1657
cosine_precision@10	0.0886
cosine_recall@1	0.6571
cosine_recall@3	0.79
cosine_recall@5	0.8286
cosine_recall@10	0.8857
cosine_ndcg@10	0.7704
cosine_mrr@10	0.7336
cosine_map@100	0.7379

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.62
cosine_accuracy@3	0.77
cosine_accuracy@5	0.8029
cosine_accuracy@10	0.85
cosine_precision@1	0.62
cosine_precision@3	0.2567
cosine_precision@5	0.1606
cosine_precision@10	0.085
cosine_recall@1	0.62
cosine_recall@3	0.77
cosine_recall@5	0.8029
cosine_recall@10	0.85
cosine_ndcg@10	0.7378
cosine_mrr@10	0.7016
cosine_map@100	0.7074

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: 2 tokens mean: 46.27 tokens max: 326 tokens	min: 2 tokens mean: 20.87 tokens max: 51 tokens

• Samples:

  positive	anchor
  We utilize a full yield curve approach in the estimation of service and interest costs by applying the specific spot rates along the yield curve used in the determination of the benefit obligation to the relevant projected cash flows. This approach provides a more precise measurement of service and interest costs by improving the correlation between the projected cash flows to the corresponding spot rates along the yield curve. This approach does not affect the measurement of our pension and other post-retirement benefit liabilities but generally results in lower benefit expense in periods when the yield curve is upward sloping.	How does the use of a full yield curve approach in estimating pension costs affect the measurement of liabilities and expenses?
  Ending	8,134
  The company's capital expenditures for 2024 are expected to be approximately $5.7 billion.	How much does the company expect to spend on capital expenditures in 2024?

• 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
• 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
• 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	1.5661	-	-	-	-	-
0.9746	12	-	0.7151	0.7378	0.7443	0.6680	0.7546
1.6244	20	0.6602	-	-	-	-	-
1.9492	24	-	0.7326	0.7533	0.7564	0.7037	0.7640
2.4365	30	0.4675	-	-	-	-	-
2.9239	36	-	0.7384	0.7575	0.7601	0.7086	0.7643
3.2487	40	0.3891	-	-	-	-	-
3.8985	48	-	0.7379	0.758	0.7601	0.7074	0.7649

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.1.2+cu121
• Accelerate: 0.32.1
• 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}
}