README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:6300
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
  - source_sentence: >-
      The total lease payments for 2023 were initially valued at $1,008 million,
      but after incorporating $43 million for interest, the final amount totaled
      $1,051 million.
    sentences:
      - >-
        What percentage of Kenvue's shares did Johnson & Johnson own after the
        exchange offer on August 23, 2023?
      - >-
        What was the increase in total lease payments from the base amount to
        the final amount including interest in 2023?
      - >-
        What is the primary use of Global Business Services within Procter &
        Gamble?
  - source_sentence: >-
      We amortize software costs using the straight-line method over the
      expected life of the software, generally 3 to 7 years.
    sentences:
      - >-
        How often does the company issue standby letters of credit, performance
        or surety bonds, or other guarantees?
      - >-
        What is the amortization method used for software costs and what is
        their expected useful life range?
      - >-
        How are the translation adjustments of foreign entity operations
        recorded in financial statements?
  - source_sentence: >-
      In 2023, we continued to invest in our colleagues, building on a wide
      range of learning and development opportunities and enhancing our
      competitive benefits in key areas including holistic health and wellness,
      total compensation and flexibility. We conduct an annual Colleague
      Experience Survey to better understand our colleagues’ needs and overall
      experience at American Express.
    sentences:
      - How does American Express support employee development and well-being?
      - >-
        By what percentage did admissions revenues increase during the year
        ended December 31, 2023 compared to the prior year?
      - >-
        What is the maximum amount payable by the Corporation for most credit
        derivatives, and how is this measured in terms of credit risk
        management?
  - source_sentence: Prepaid expenses were $69,167 in 2022 and increased to $97,670 in 2023.
    sentences:
      - What functional responsibility does Mary E. Adcock have at Kroger?
      - >-
        What is Apple's approach to licenses for intellectual property owned by
        third parties used in its products and services?
      - How much did the prepaid expenses increase from 2022 to 2023?
  - source_sentence: Generated cash flows from operations of $4.5 billion.
    sentences:
      - How much did cash flows from operations amount to in 2022?
      - What was the overall turnover rate at the company in fiscal year 2023?
      - >-
        What are the expectations the company has for its employees in aligning
        with the Code of Conduct?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
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: 0.7014285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8242857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8671428571428571
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9071428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7014285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2747619047619047
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1734285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09071428571428569
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7014285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8242857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8671428571428571
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9071428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8052852140611453
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7727052154195015
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7763711302515639
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7114285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8242857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8642857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9085714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7114285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2747619047619047
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17285714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09085714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7114285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8242857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8642857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9085714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8098666238099614
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7784104308390026
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7819743643907353
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7014285714285714
            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: 0.8914285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7014285714285714
            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: 0.08914285714285713
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7014285714285714
            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: 0.8914285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8008524512077413
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7714569160997735
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7758614780389599
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6828571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8128571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8485714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8914285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6828571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.270952380952381
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16971428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08914285714285713
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6828571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8128571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8485714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8914285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7893688537538128
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.756581632653061
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7607042782514057
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6614285714285715
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7957142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8285714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8771428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6614285714285715
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2652380952380953
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1657142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0877142857142857
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6614285714285715
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7957142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8285714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8771428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7706919427250147
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.736583900226757
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7408800803327711
            name: Cosine Map@100

BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the json dataset. 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 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • json
• 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, 'architecture': '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("deter3/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Generated cash flows from operations of $4.5 billion.',
    'How much did cash flows from operations amount to in 2022?',
    'What was the overall turnover rate at the company in fiscal year 2023?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7518, 0.2425],
#         [0.7518, 1.0000, 0.2768],
#         [0.2425, 0.2768, 1.0000]])

Evaluation

Metrics

Information Retrieval

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 768
  }
  

Metric	Value
cosine_accuracy@1	0.7014
cosine_accuracy@3	0.8243
cosine_accuracy@5	0.8671
cosine_accuracy@10	0.9071
cosine_precision@1	0.7014
cosine_precision@3	0.2748
cosine_precision@5	0.1734
cosine_precision@10	0.0907
cosine_recall@1	0.7014
cosine_recall@3	0.8243
cosine_recall@5	0.8671
cosine_recall@10	0.9071
cosine_ndcg@10	0.8053
cosine_mrr@10	0.7727
cosine_map@100	0.7764

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 512
  }
  

Metric	Value
cosine_accuracy@1	0.7114
cosine_accuracy@3	0.8243
cosine_accuracy@5	0.8643
cosine_accuracy@10	0.9086
cosine_precision@1	0.7114
cosine_precision@3	0.2748
cosine_precision@5	0.1729
cosine_precision@10	0.0909
cosine_recall@1	0.7114
cosine_recall@3	0.8243
cosine_recall@5	0.8643
cosine_recall@10	0.9086
cosine_ndcg@10	0.8099
cosine_mrr@10	0.7784
cosine_map@100	0.782

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 256
  }
  

Metric	Value
cosine_accuracy@1	0.7014
cosine_accuracy@3	0.8243
cosine_accuracy@5	0.8557
cosine_accuracy@10	0.8914
cosine_precision@1	0.7014
cosine_precision@3	0.2748
cosine_precision@5	0.1711
cosine_precision@10	0.0891
cosine_recall@1	0.7014
cosine_recall@3	0.8243
cosine_recall@5	0.8557
cosine_recall@10	0.8914
cosine_ndcg@10	0.8009
cosine_mrr@10	0.7715
cosine_map@100	0.7759

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 128
  }
  

Metric	Value
cosine_accuracy@1	0.6829
cosine_accuracy@3	0.8129
cosine_accuracy@5	0.8486
cosine_accuracy@10	0.8914
cosine_precision@1	0.6829
cosine_precision@3	0.271
cosine_precision@5	0.1697
cosine_precision@10	0.0891
cosine_recall@1	0.6829
cosine_recall@3	0.8129
cosine_recall@5	0.8486
cosine_recall@10	0.8914
cosine_ndcg@10	0.7894
cosine_mrr@10	0.7566
cosine_map@100	0.7607

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 64
  }
  

Metric	Value
cosine_accuracy@1	0.6614
cosine_accuracy@3	0.7957
cosine_accuracy@5	0.8286
cosine_accuracy@10	0.8771
cosine_precision@1	0.6614
cosine_precision@3	0.2652
cosine_precision@5	0.1657
cosine_precision@10	0.0877
cosine_recall@1	0.6614
cosine_recall@3	0.7957
cosine_recall@5	0.8286
cosine_recall@10	0.8771
cosine_ndcg@10	0.7707
cosine_mrr@10	0.7366
cosine_map@100	0.7409

Training Details

Training Dataset

json

• Dataset: json
• Size: 6,300 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 13 tokens mean: 45.95 tokens max: 248 tokens	min: 8 tokens mean: 20.43 tokens max: 41 tokens

• Samples:

  positive	anchor
  The Company's nominal par value per share was slightly reduced to USD $0.10, reflecting in the share capital, as of December 30, 2023.	What was the nominal par value per share of Garmin Ltd. in U.S. dollars as of December 30, 2023?
  Over the last several years, the number and potential significance of the litigation and investigations involving the company have increased, and there can be no assurance that this trend will not continue.	How has the litigation and investigation landscape changed for the company over recent years?
  As of January 31, 2023, assets located outside the Americas were 15 percent of total assets.	What percentage of Salesforce's total assets were located outside the Americas as of January 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
• 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
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.8122	10	1.5429	-	-	-	-	-
0.9746	12	-	0.7915	0.7927	0.7820	0.7722	0.7396
1.6244	20	0.6772	-	-	-	-	-
1.9492	24	-	0.8019	0.8041	0.7971	0.7835	0.7625
2.4365	30	0.5496	-	-	-	-	-
2.9239	36	-	0.8048	0.8070	0.8007	0.7879	0.7690
3.2487	40	0.4528	-	-	-	-	-
3.8985	48	-	0.8053	0.8099	0.8009	0.7894	0.7707

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 5.0.0
• Transformers: 4.41.2
• PyTorch: 2.1.2+cu121
• Accelerate: 1.8.1
• 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}
}