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bge-base-financial-matryoshka

anushettypsl / bge-base-financial-matryoshka

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Total runs: 79
24-hour runs: 0
7-day runs: 25
30-day runs: 7
Model's Last Updated: November 27 2024
sentence-similarity

Introduction of bge-base-financial-matryoshka

Model Details of bge-base-financial-matryoshka

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 tokens
  • Similarity Function: Cosine Similarity
  • Training Dataset:
    • json
  • Language: en
  • License: apache-2.0
Model Sources
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("anushettypsl/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Management assessed the effectiveness of the company’s internal control over financial reporting as of December 31, 2023. In making this assessment, we used the criteria set forth by the Committee of Sponsoring Organizations of the Treadway Commission (COSO) in Internal Control—Integrated Framework (2013).',
    'What criteria did Caterpillar Inc. use to assess the effectiveness of its internal control over financial reporting as of December 31, 2023?',
    'What are the primary components of U.S. sales volumes for Ford?',
]
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
Metric Value
cosine_accuracy@1 0.6914
cosine_accuracy@3 0.8229
cosine_accuracy@5 0.86
cosine_accuracy@10 0.9071
cosine_precision@1 0.6914
cosine_precision@3 0.2743
cosine_precision@5 0.172
cosine_precision@10 0.0907
cosine_recall@1 0.6914
cosine_recall@3 0.8229
cosine_recall@5 0.86
cosine_recall@10 0.9071
cosine_ndcg@10 0.8
cosine_mrr@10 0.7657
cosine_map@100 0.7691
Information Retrieval
Metric Value
cosine_accuracy@1 0.6857
cosine_accuracy@3 0.8186
cosine_accuracy@5 0.8643
cosine_accuracy@10 0.91
cosine_precision@1 0.6857
cosine_precision@3 0.2729
cosine_precision@5 0.1729
cosine_precision@10 0.091
cosine_recall@1 0.6857
cosine_recall@3 0.8186
cosine_recall@5 0.8643
cosine_recall@10 0.91
cosine_ndcg@10 0.7973
cosine_mrr@10 0.7613
cosine_map@100 0.7643
Information Retrieval
Metric Value
cosine_accuracy@1 0.6871
cosine_accuracy@3 0.8157
cosine_accuracy@5 0.8557
cosine_accuracy@10 0.8957
cosine_precision@1 0.6871
cosine_precision@3 0.2719
cosine_precision@5 0.1711
cosine_precision@10 0.0896
cosine_recall@1 0.6871
cosine_recall@3 0.8157
cosine_recall@5 0.8557
cosine_recall@10 0.8957
cosine_ndcg@10 0.7922
cosine_mrr@10 0.7589
cosine_map@100 0.7627
Information Retrieval
Metric Value
cosine_accuracy@1 0.6671
cosine_accuracy@3 0.8043
cosine_accuracy@5 0.84
cosine_accuracy@10 0.8771
cosine_precision@1 0.6671
cosine_precision@3 0.2681
cosine_precision@5 0.168
cosine_precision@10 0.0877
cosine_recall@1 0.6671
cosine_recall@3 0.8043
cosine_recall@5 0.84
cosine_recall@10 0.8771
cosine_ndcg@10 0.7739
cosine_mrr@10 0.7405
cosine_map@100 0.7449
Information Retrieval
Metric Value
cosine_accuracy@1 0.64
cosine_accuracy@3 0.7814
cosine_accuracy@5 0.8186
cosine_accuracy@10 0.8586
cosine_precision@1 0.64
cosine_precision@3 0.2605
cosine_precision@5 0.1637
cosine_precision@10 0.0859
cosine_recall@1 0.64
cosine_recall@3 0.7814
cosine_recall@5 0.8186
cosine_recall@10 0.8586
cosine_ndcg@10 0.7512
cosine_mrr@10 0.7165
cosine_map@100 0.7214
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: 8 tokens
    • mean: 44.33 tokens
    • max: 289 tokens
    • min: 9 tokens
    • mean: 20.43 tokens
    • max: 46 tokens
  • Samples:
    positive anchor
    The Company defines fair value as the price received to transfer an asset or paid to transfer a liability in an orderly transaction between market participants at the measurement date. In accordance with ASC 820, Fair Value Measurements and Disclosures, the Company uses the fair value hierarchy which prioritizes the inputs used to measure fair value. The hierarchy gives the highest priority to unadjusted quoted prices in active markets for identical assets or liabilities (Level 1), observable inputs other than quoted prices (Level 2), and unobservable inputs (Level 3). What is the role of Level 1, Level 2, and Level 3 inputs in the fair value hierarchy according to ASC 820?
    In the event of conversion of the Notes, if shares are delivered to the Company under the Capped Call Transactions, they will offset the dilutive effect of the shares that the Company would issue under the Notes. What happens to the dilutive effect of shares issued under the Notes if shares are delivered to the Company under the Capped Call Transactions during the conversion?
    Marketing expenses increased $48.8 million to $759.2 million in the year ended December 31, 2023 compared to the year ended December 31, 2022. How much did the marketing expenses increase in the year ended December 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
  • batch_sampler : no_duplicates
  • multi_dataset_batch_sampler : proportional
Training Logs
Epoch Step Training Loss dim_768_cosine_map@100 dim_512_cosine_map@100 dim_256_cosine_map@100 dim_128_cosine_map@100 dim_64_cosine_map@100
0.8122 10 1.5602 - - - - -
0.9746 12 - 0.7540 0.7541 0.7492 0.7279 0.6920
1.6244 20 0.6618 - - - - -
1.9492 24 - 0.7653 0.7624 0.7579 0.7422 0.7204
2.4365 30 0.4579 - - - - -
2.9239 36 - 0.7682 0.7646 0.7626 0.7441 0.7229
3.2487 40 0.3997 - - - - -
3.8985 48 - 0.7691 0.7643 0.7627 0.7449 0.7214
  • The bold row denotes the saved checkpoint.
Framework Versions
  • Python: 3.10.12
  • Sentence Transformers: 3.2.0
  • Transformers: 4.41.2
  • PyTorch: 2.2.0a0+6a974be
  • Accelerate: 0.27.0
  • 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}
}

Runs of anushettypsl bge-base-financial-matryoshka on huggingface.co

79
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3-day runs
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