README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:1K<n<10K
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
  - source_sentence: What is the Path to Pro program related to?
    sentences:
      - What types of programs are developed to upskill manufacturing employees?
      - What was the overall turnover rate at the company in fiscal year 2023?
      - >-
        What was the net interest revenue of The Charles Schwab Corporation in
        2023?
  - source_sentence: What types of businesses does HPE serve?
    sentences:
      - What types of industries does TTI service?
      - What interest rates are applicable to the notes issued in April 2022?
      - >-
        The total unrealized losses on U.S. Treasury securities amounted to $134
        million.
  - source_sentence: What is the title of Item 6 in the text?
    sentences:
      - Item 6—Reserved
      - The operating income for the year 2023 was reported as -$74.3 million.
      - >-
        Commission revenues at Schwab experienced a 10% decrease from 2022 to
        2023.
  - source_sentence: How is Dynamics' revenue mainly driven?
    sentences:
      - >-
        What are the primary sources of revenue for the company mentioned in the
        text?
      - How many new stores did the company open in Mexico during fiscal 2022?
      - >-
        Legal proceedings are discussed in Item 3 of the Annual Report on Form
        10-K.
  - source_sentence: What is basic earnings per share based on?
    sentences:
      - How is basic net income per share calculated?
      - How did NIKE's fiscal 2023 revenue compare to its fiscal 2022 revenue?
      - What types of vessels are included in Chevron's operated marine fleet?
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: 0.6828571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.82
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8628571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.91
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6828571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2733333333333334
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17257142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.091
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6828571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.82
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8628571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.91
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7970675337008412
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7608446712018138
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7643786819951583
            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: 0.8185714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8657142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9114285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.68
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27285714285714285
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17314285714285713
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09114285714285712
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.68
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8185714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8657142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9114285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7954799079266202
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7583633786848069
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7618248215296402
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6785714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.81
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8571428571428571
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9142857142857143
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6785714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1714285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09142857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6785714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.81
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8571428571428571
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9142857142857143
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7954881703263427
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7577579365079364
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7606385177656011
            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: 0.7957142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8471428571428572
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6571428571428571
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2652380952380952
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16942857142857143
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6571428571428571
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7957142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8471428571428572
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7770789777970544
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7379240362811791
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7420186535175607
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6342857142857142
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7642857142857142
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8057142857142857
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8657142857142858
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6342857142857142
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.25476190476190474
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16114285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08657142857142856
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6342857142857142
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7642857142857142
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8057142857142857
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8657142857142858
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7466817341215128
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.70906462585034
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7141559106614794
            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("xiaofengzi/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'What is basic earnings per share based on?',
    'How is basic net income per share calculated?',
    "How did NIKE's fiscal 2023 revenue compare to its fiscal 2022 revenue?",
]
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	0.82
cosine_accuracy@5	0.8629
cosine_accuracy@10	0.91
cosine_precision@1	0.6829
cosine_precision@3	0.2733
cosine_precision@5	0.1726
cosine_precision@10	0.091
cosine_recall@1	0.6829
cosine_recall@3	0.82
cosine_recall@5	0.8629
cosine_recall@10	0.91
cosine_ndcg@10	0.7971
cosine_mrr@10	0.7608
cosine_map@100	0.7644

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.68
cosine_accuracy@3	0.8186
cosine_accuracy@5	0.8657
cosine_accuracy@10	0.9114
cosine_precision@1	0.68
cosine_precision@3	0.2729
cosine_precision@5	0.1731
cosine_precision@10	0.0911
cosine_recall@1	0.68
cosine_recall@3	0.8186
cosine_recall@5	0.8657
cosine_recall@10	0.9114
cosine_ndcg@10	0.7955
cosine_mrr@10	0.7584
cosine_map@100	0.7618

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6786
cosine_accuracy@3	0.81
cosine_accuracy@5	0.8571
cosine_accuracy@10	0.9143
cosine_precision@1	0.6786
cosine_precision@3	0.27
cosine_precision@5	0.1714
cosine_precision@10	0.0914
cosine_recall@1	0.6786
cosine_recall@3	0.81
cosine_recall@5	0.8571
cosine_recall@10	0.9143
cosine_ndcg@10	0.7955
cosine_mrr@10	0.7578
cosine_map@100	0.7606

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6571
cosine_accuracy@3	0.7957
cosine_accuracy@5	0.8471
cosine_accuracy@10	0.9
cosine_precision@1	0.6571
cosine_precision@3	0.2652
cosine_precision@5	0.1694
cosine_precision@10	0.09
cosine_recall@1	0.6571
cosine_recall@3	0.7957
cosine_recall@5	0.8471
cosine_recall@10	0.9
cosine_ndcg@10	0.7771
cosine_mrr@10	0.7379
cosine_map@100	0.742

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6343
cosine_accuracy@3	0.7643
cosine_accuracy@5	0.8057
cosine_accuracy@10	0.8657
cosine_precision@1	0.6343
cosine_precision@3	0.2548
cosine_precision@5	0.1611
cosine_precision@10	0.0866
cosine_recall@1	0.6343
cosine_recall@3	0.7643
cosine_recall@5	0.8057
cosine_recall@10	0.8657
cosine_ndcg@10	0.7467
cosine_mrr@10	0.7091
cosine_map@100	0.7142

Training Details

Training Dataset

Unnamed Dataset

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

  	anchor	positive
  type	string	string
  details	min: 7 tokens mean: 20.44 tokens max: 51 tokens	min: 6 tokens mean: 47.22 tokens max: 512 tokens

• Samples:

  anchor	positive
  How did the Energy & Transportation segment's sales and profit change in 2023?	Energy & Transportation's total sales were $28.001 billion in 2023, an increase of $4.249 billion, or 18... and profit was $4.936 billion in 2023, an increase of $1.627 billion, or 49 percent...
  In which segments were acquisitions made in 2022?	During 2022, acquisitions occurred in Workforce Solutions and USIS operating segments, and the International segment.
  What are the contents found on pages 163 to 309 in the document?	The Consolidated Financial Statements, together with the Notes thereto and the report thereon dated February 16, 2024, of PricewaterhouseCoopers LLP, appear on pages 163–309.

• 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	1.5644	-	-	-	-	-
0.9746	12	-	0.7186	0.7399	0.7414	0.6757	0.7445
1.6244	20	0.6502	-	-	-	-	-
1.9492	24	-	0.7379	0.7544	0.7573	0.7069	0.7600
2.4365	30	0.434	-	-	-	-	-
2.9239	36	-	0.7426	0.7614	0.7616	0.7134	0.7634
3.2487	40	0.3627	-	-	-	-	-
3.8985	48	-	0.7420	0.7606	0.7618	0.7142	0.7644

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.4
• Sentence Transformers: 3.0.0
• Transformers: 4.41.2
• PyTorch: 2.6.0+cu118
• Accelerate: 1.6.0
• 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}
}