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shivamsharma1967/bge-base-financial-matryoshka

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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Model Card

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}) 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:

bash

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("shivamsharma1967/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Table of Contents\nAMAZON.COM, INC.\nCONSOLIDATED STATEMENTS OF OPERATIONS\n(in millions, except per share data)\n \n \nYear Ended December 31,\n \n2015\n \n2016\n \n2017\nNet product sales\n$\n79,268 $\n94,665 $\n118,573\nNet service sales\n27,738 \n41,322 \n59,293\nTotal net sales\n107,006 \n135,987 \n177,866\nOperating expenses:\n \n \n \nCost of sales\n71,651 \n88,265 \n111,934\nFulfillment\n13,410 \n17,619 \n25,249\nMarketing\n5,254 \n7,233 \n10,069\nTechnology and content\n12,540 \n16,085 \n22,620\nGeneral and administrative\n1,747 \n2,432 \n3,674\nOther operating expense, net\n171 \n167 \n214\nTotal operating expenses\n104,773 \n131,801 \n173,760\nOperating income\n2,233 \n4,186 \n4,106\nInterest income\n50 \n100 \n202\nInterest expense\n(459) \n(484) \n(848)\nOther income (expense), net\n(256) \n90 \n346\nTotal non-operating income (expense)\n(665) \n(294) \n(300)\nIncome before income taxes\n1,568 \n3,892 \n3,806\nProvision for income taxes\n(950) \n(1,425) \n(769)\nEquity-method investment activity, net of tax\n(22) \n(96) \n(4)\nNet income\n$\n596 $\n2,371 $\n3,033\nBasic earnings per share\n$\n1.28 $\n5.01 $\n6.32\nDiluted earnings per share\n$\n1.25 $\n4.90 $\n6.15\nWeighted-average shares used in computation of earnings per share:\n \n \n \nBasic\n467 \n474 \n480\nDiluted\n477 \n484 \n493\nSee accompanying notes to consolidated financial statements.\n38\nTable of Contents\nAMAZON.COM, INC.\nCONSOLIDATED STATEMENTS OF OPERATIONS\n(in millions, except per share data)\n \n \nYear Ended December 31,\n \n2015\n \n2016\n \n2017\nNet product sales\n$\n79,268 $\n94,665 $\n118,573\nNet service sales\n27,738 \n41,322 \n59,293\nTotal net sales\n107,006 \n135,987 \n177,866\nOperating expenses:\n \n \n \nCost of sales\n71,651 \n88,265 \n111,934\nFulfillment\n13,410 \n17,619 \n25,249\nMarketing\n5,254 \n7,233 \n10,069\nTechnology and content\n12,540 \n16,085 \n22,620\nGeneral and administrative\n1,747 \n2,432 \n3,674\nOther operating expense, net\n171 \n167 \n214\nTotal operating expenses\n104,773 \n131,801 \n173,760\nOperating income\n2,233 \n4,186 \n4,106\nInterest income\n50 \n100 \n202\nInterest expense\n(459) \n(484) \n(848)\nOther income (expense), net\n(256) \n90 \n346\nTotal non-operating income (expense)\n(665) \n(294) \n(300)\nIncome before income taxes\n1,568 \n3,892 \n3,806\nProvision for income taxes\n(950) \n(1,425) \n(769)\nEquity-method investment activity, net of tax\n(22) \n(96) \n(4)\nNet income\n$\n596 $\n2,371 $\n3,033\nBasic earnings per share\n$\n1.28 $\n5.01 $\n6.32\nDiluted earnings per share\n$\n1.25 $\n4.90 $\n6.15\nWeighted-average shares used in computation of earnings per share:\n \n \n \nBasic\n467 \n474 \n480\nDiluted\n477 \n484 \n493\nSee accompanying notes to consolidated financial statements.\n38',
    "What is Amazon's year-over-year change in revenue from FY2016 to FY2017 (in units of percents and round to one decimal place)? Calculate what was asked by utilizing the line items clearly shown in the statement of income.",
    'What is the FY2018 - FY2020 3 year average of capex as a % of revenue for MGM Resorts? Answer in units of percents and round to one decimal place. Please utilize information provided primarily within the statement of cash flows and the statement of income.',
]
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]

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Evaluation

Metrics

Information Retrieval

—Datasets: dim_768, dim_512, dim_256, dim_128 and dim_64
—Evaluated with <code>InformationRetrievalEvaluator</code>

Metric	dim_768	dim_512	dim_256	dim_128	dim_64
cosine_accuracy@1	0.4	0.2667	0.2	0.2	0.2667
cosine_accuracy@3	0.4667	0.4667	0.4	0.3333	0.2667
cosine_accuracy@5	0.5333	0.5333	0.4	0.4	0.3333
cosine_accuracy@10	0.6667	0.6667	0.6	0.5333	0.4667
cosine_precision@1	0.4	0.2667	0.2	0.2	0.2667
cosine_precision@3	0.1556	0.1556	0.1333	0.1111	0.0889
cosine_precision@5	0.1067	0.1067	0.08	0.08	0.0667
cosine_precision@10	0.0667	0.0667	0.06	0.0533	0.0467
cosine_recall@1	0.4	0.2667	0.2	0.2	0.2667
cosine_recall@3	0.4667	0.4667	0.4	0.3333	0.2667
cosine_recall@5	0.5333	0.5333	0.4	0.4	0.3333
cosine_recall@10	0.6667	0.6667	0.6	0.5333	0.4667
cosine_ndcg@10	0.5029	0.4537	0.374	0.346	0.3413
cosine_mrr@10	0.4541	0.3874	0.3051	0.2883	0.304
cosine_map@100	0.467	0.4024	0.3253	0.306	0.322

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Training Details

Training Dataset

json

—Dataset: json
—Size: 135 training samples
—Columns: <code>positive</code> and <code>anchor</code>
—Approximate statistics based on the first 135 samples: | | positive | anchor | |:--------|:--------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 359 tokens</li><li>mean: 508.73 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 39.7 tokens</li><li>max: 175 tokens</li></ul> |
—Samples: | positive | anchor | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Twelve Months Ended June 30, 2022 Twelve Months Ended June 30, 2023 ($ million) EBITDA EBIT Net Income EPS (Diluted US cents)(1) EBITDA EBIT Net Income EPS (Diluted US cents)(1) Net income attributable to Amcor 805 805 805 52.9 1,048 1,048 1,048 70.5 Net income attributable to non-controlling interests 10 10 10 10 Tax expense 300 300 193 193 Interest expense, net 135 135 259 259 Depreciation and amortization 579 569 EBITDA, EBIT, Net income and EPS 1,829 1,250 805 52.9 2,080 1,510 1,048 70.5 2019 Bemis Integration Plan 37 37 37 2.5 Net loss on disposals(2) 10 10 10 0.7 Impact of hyperinflation 16 16 16 1.0 24 24 24 1.9 Property and other losses, net(3) 13 13 13 0.8 2 2 2 0.1 Russia-Ukraine conflict impacts(4) 200 200 200 13.2 (90) (90) (90) (6.0) Pension settlements 8...</code> | <code>What Was AMCOR's Adjusted Non GAAP EBITDA for FY 2023</code> | | <code>SQUARE,INC. CONSOLIDATEDBALANCESHEETS (In thousands, except share and per share data) December31, 2016 2015 Assets Currentassets: Cashandcashequivalents $ 452,030 $ 461,329 Short-terminvestments 59,901 Restrictedcash 22,131 13,537 Settlementsreceivable 321,102 142,727 Customerfundsheld 43,574 9,446 Loansheldforsale 42,144 604 Merchantcashadvancereceivable,net 4,212 36,473 Othercurrentassets 56,331 41,447 Totalcurrentassets 1,001,425 705,563 Propertyandequipment,net 88,328 87,222 Goodwill 57,173 56,699 Acquiredintangibleassets,net 19,292 26,776 Long-terminvestments 27,366 Restrictedcash 14,584 14,686 Otherassets 3,194 3,826 Totalassets $ 1,211,362 $ 894,772 LiabilitiesandStockholdersEquity Currentliabilities: Accountspayable $ 12,602 $ 18,869 Customerspayable 388,058 215,365 Customerfundsobligation 43,574 9,446 Accruedtransactionlosses 20,064 17,176 Accruedexpenses 39,543 44,401 Othercurrentliabilities 73,623 28,945 Totalcurrentliabilities 577,464 33...</code> | <code>Considering the data in the balance sheet, what is Block's (formerly known as Square) FY2016 working capital ratio? Define working capital ratio as total current assets divided by total current liabilities. Round your answer to two decimal places.</code> | | <code>Consolidated Balance Sheets Verizon Communications Inc. and Subsidiaries (dollars in millions, except per share amounts) At December 31, 2022 2021 Assets Current assets Cash and cash equivalents $ 2,605 $ 2,921 Accounts receivable 25,332 24,742 Less Allowance for credit losses 826 896 Accounts receivable, net 24,506 23,846 Inventories 2,388 3,055 Prepaid expenses and other 8,358 6,906 Total current assets 37,857 36,728 Property, plant and equipment 307,689 289,897 Less Accumulated depreciation 200,255 190,201 Property, plant and equipment, net 107,434 99,696 Investments in unconsolidated businesses 1,071 1,061 Wireless licenses 149,796 147,619 Goodwill 28,671 28,603 Other intangible assets, net 11,461 11,677 Operating lease right-of-use assets 26,130 27,883 Other assets 17,260 13,329 Total assets $ 379,680 $ 366,596 Liabilities and Equity Current liabilities Debt maturing within o...</code> | <code>Does Verizon have a reasonably healthy liquidity profile based on its quick ratio for FY 2022? If the quick ratio is not relevant to measure liquidity, please state that and explain why.</code> |
—Loss: <code>MatryoshkaLoss</code> with these parameters:

json

  {
      "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: 16
—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: False
—load_best_model_at_end: True
—optim: adamwtorchfused
—batch_sampler: no_duplicates

All Hyperparameters

<details><summary>Click to expand</summary>

—overwrite_output_dir: False
—do_predict: False
—eval_strategy: epoch
—prediction_loss_only: True
—per_device_train_batch_size: 16
—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
—torch_empty_cache_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: False
—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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—deepspeed: None
—label_smoothing_factor: 0.0
—optim: adamwtorchfused
—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: None
—hub_always_push: False
—gradient_checkpointing: False
—gradient_checkpointing_kwargs: None
—include_inputs_for_metrics: False
—include_for_metrics: []
—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
—eval_on_start: False
—use_liger_kernel: False
—eval_use_gather_object: False
—average_tokens_across_devices: False
—prompts: None
—batch_sampler: no_duplicates
—multi_dataset_batch_sampler: proportional

</details>

Training Logs

Epoch	Step	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	0	0.5029	0.4537	0.374	0.346	0.3413

—The bold row denotes the saved checkpoint.

Framework Versions

—Python: 3.11.11
—Sentence Transformers: 3.4.1
—Transformers: 4.48.3
—PyTorch: 2.5.1+cu124
—Accelerate: 1.3.0
—Datasets: 3.3.2
—Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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

bibtex

@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}
}

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