README.md

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: >-
      The company hedges foreign currency exchange-based cash flow variability
      of certain fees using forward contracts designated as hedging instruments.
      It also holds short-term forward contracts to offset exposure to
      fluctuations in certain of its foreign currency denominated cash balances
      and intercompany financing arrangements, without designating these forward
      contracts as hedging instruments.
    sentences:
      - >-
        What was the total stockholders' equity at Amazon.com, Inc. as of
        December 31, 2021?
      - >-
        How does the company manage fluctuations in foreign currency exchange
        rates?
      - >-
        What are some of the potential consequences for Meta Platforms, Inc.
        from inquiries or investigations as noted in the provided text?
  - source_sentence: >-
      The Financial Statement Schedule is located on page S-1 of IBM’s 2023 Form
      10-K.
    sentences:
      - >-
        How is Hewlett Packard addressing competition in the enterprise IT
        infrastructure market?
      - >-
        Where in IBM’s 2023 Form 10-K can the Financial Statement Schedule be
        found?
      - What was Intuit's Net Income in fiscal year 2023?
  - source_sentence: Sales of DARZALEX in 2023 showed a 22.2% increase over the previous year.
    sentences:
      - >-
        How much did DARZALEX sales increase in 2023 compared to the previous
        year?
      - What strategic focus does Etsy have for its marketplace?
      - Since when has Mr. Goodarzi been the President and CEO of Intuit?
  - source_sentence: >-
      Chubb Limited further advanced their goal of greater product, customer,
      and geographical diversification with incremental purchases that led to a
      controlling majority interest in Huatai Insurance Group Co. Ltd, owning
      about 76.5 percent as of July 1, 2023.
    sentences:
      - >-
        What are the primary sources of revenue for Salesforce, Inc. as
        described in their consolidated financial statements?
      - >-
        What acquisitions did Hershey complete to expand its snacking portfolio,
        and when did these occur?
      - >-
        What percentage of the Huatai Insurance Group Co. Ltd does Chubb Limited
        own as of July 1, 2023?
  - source_sentence: >-
      The consolidated balance sheets of Visa Inc. as of September 30, 2023,
      list the total current assets at $33,532 million.
    sentences:
      - >-
        What was the total of Visa Inc.'s current assets as of September 30,
        2023?
      - >-
        What was Garmin Ltd.'s net income for the fiscal year ended December 30,
        2023?
      - >-
        By what percentage did online sales grow in fiscal 2022 compared to
        fiscal 2021?
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: 0.6885714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8285714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8671428571428571
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9128571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6885714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27619047619047615
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1734285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09128571428571426
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6885714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8285714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8671428571428571
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9128571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8022848173323525
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7666422902494329
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7696751281834099
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6928571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8228571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8642857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.91
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6928571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27428571428571424
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17285714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09099999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6928571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8228571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8642857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.91
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8016907244180009
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7668412698412699
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.770110214157224
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6871428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8185714285714286
            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: 0.6871428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27285714285714285
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17257142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09014285714285712
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6871428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8185714285714286
            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: 0.7962767797304091
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7623021541950112
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7656765331908582
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6742857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8057142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8528571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8942857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6742857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26857142857142857
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17057142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08942857142857143
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6742857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8057142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8528571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8942857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7861958176742697
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7513151927437639
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7548627394954026
            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: 0.7971428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8185714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8685714285714285
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6428571428571429
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26571428571428574
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1637142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08685714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6428571428571429
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7971428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8185714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8685714285714285
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7590638034734002
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7236972789115643
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7282650681776726
            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("WaheedLone/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'The consolidated balance sheets of Visa Inc. as of September 30, 2023, list the total current assets at $33,532 million.',
    "What was the total of Visa Inc.'s current assets as of September 30, 2023?",
    "What was Garmin Ltd.'s net income for the fiscal year ended December 30, 2023?",
]
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	0.6886
cosine_accuracy@3	0.8286
cosine_accuracy@5	0.8671
cosine_accuracy@10	0.9129
cosine_precision@1	0.6886
cosine_precision@3	0.2762
cosine_precision@5	0.1734
cosine_precision@10	0.0913
cosine_recall@1	0.6886
cosine_recall@3	0.8286
cosine_recall@5	0.8671
cosine_recall@10	0.9129
cosine_ndcg@10	0.8023
cosine_mrr@10	0.7666
cosine_map@100	0.7697

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6929
cosine_accuracy@3	0.8229
cosine_accuracy@5	0.8643
cosine_accuracy@10	0.91
cosine_precision@1	0.6929
cosine_precision@3	0.2743
cosine_precision@5	0.1729
cosine_precision@10	0.091
cosine_recall@1	0.6929
cosine_recall@3	0.8229
cosine_recall@5	0.8643
cosine_recall@10	0.91
cosine_ndcg@10	0.8017
cosine_mrr@10	0.7668
cosine_map@100	0.7701

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6871
cosine_accuracy@3	0.8186
cosine_accuracy@5	0.8629
cosine_accuracy@10	0.9014
cosine_precision@1	0.6871
cosine_precision@3	0.2729
cosine_precision@5	0.1726
cosine_precision@10	0.0901
cosine_recall@1	0.6871
cosine_recall@3	0.8186
cosine_recall@5	0.8629
cosine_recall@10	0.9014
cosine_ndcg@10	0.7963
cosine_mrr@10	0.7623
cosine_map@100	0.7657

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6743
cosine_accuracy@3	0.8057
cosine_accuracy@5	0.8529
cosine_accuracy@10	0.8943
cosine_precision@1	0.6743
cosine_precision@3	0.2686
cosine_precision@5	0.1706
cosine_precision@10	0.0894
cosine_recall@1	0.6743
cosine_recall@3	0.8057
cosine_recall@5	0.8529
cosine_recall@10	0.8943
cosine_ndcg@10	0.7862
cosine_mrr@10	0.7513
cosine_map@100	0.7549

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6429
cosine_accuracy@3	0.7971
cosine_accuracy@5	0.8186
cosine_accuracy@10	0.8686
cosine_precision@1	0.6429
cosine_precision@3	0.2657
cosine_precision@5	0.1637
cosine_precision@10	0.0869
cosine_recall@1	0.6429
cosine_recall@3	0.7971
cosine_recall@5	0.8186
cosine_recall@10	0.8686
cosine_ndcg@10	0.7591
cosine_mrr@10	0.7237
cosine_map@100	0.7283

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: 6 tokens mean: 45.17 tokens max: 260 tokens	min: 7 tokens mean: 20.38 tokens max: 40 tokens

• Samples:

  positive	anchor
  Net revenue for fiscal year 2023 increased by $435 million compared to fiscal year 2022.	How did the net revenue for fiscal year 2023 compare to fiscal year 2022?
  Adjusted Free Cash Flow is defined as operating cash flow less capital spending and excluding payments for the transitional tax resulting from the U.S. Tax Act.	How is Adjusted Free Cash Flow defined in the text?
  During 2023, the Company’s net sales through its direct and indirect distribution channels accounted for 37% and 63%, respectively, of total net sales.	During 2023, what percentage of the Company’s net sales came from direct sales channels?

• 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
• 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: False
• 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	1.6399	-	-	-	-	-
0.9746	12	-	0.7441	0.7580	0.7543	0.7068	0.7632
1.6244	20	0.6475	-	-	-	-	-
1.9492	24	-	0.7530	0.7653	0.7672	0.7244	0.7708
2.4365	30	0.4494	-	-	-	-	-
2.9239	36	-	0.7548	0.7653	0.7683	0.7297	0.7679
3.2487	40	0.4089	-	-	-	-	-
3.8985	48	-	0.7549	0.7657	0.7701	0.7283	0.7697

• 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: 0.31.0
• 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}
}