TatvaRA/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 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:

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("TatvaRA/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'In which part and item of the Annual Report on Form 10-K can the consolidated financial statements be found?',
    'The consolidated financial statements and accompanying notes listed in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included elsewhere in this Annual Report on For... 10-K.',
    'Our Consumer Wireline business unit offers AT&T Internet Air, which is a fixed wireless access product that provides home internet services delivered over our 5G wireless network where available.',
]
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 with these parameters:

  {
      "truncate_dim": 768
  }
  

Metric	Value
cosine_accuracy@1	0.7114
cosine_accuracy@3	0.8371
cosine_accuracy@5	0.87
cosine_accuracy@10	0.9057
cosine_precision@1	0.7114
cosine_precision@3	0.279
cosine_precision@5	0.174
cosine_precision@10	0.0906
cosine_recall@1	0.7114
cosine_recall@3	0.8371
cosine_recall@5	0.87
cosine_recall@10	0.9057
cosine_ndcg@10	0.8111
cosine_mrr@10	0.7805
cosine_map@100	0.7842

Information Retrieval

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

  {
      "truncate_dim": 512
  }
  

Metric	Value
cosine_accuracy@1	0.7157
cosine_accuracy@3	0.83
cosine_accuracy@5	0.87
cosine_accuracy@10	0.9071
cosine_precision@1	0.7157
cosine_precision@3	0.2767
cosine_precision@5	0.174
cosine_precision@10	0.0907
cosine_recall@1	0.7157
cosine_recall@3	0.83
cosine_recall@5	0.87
cosine_recall@10	0.9071
cosine_ndcg@10	0.8116
cosine_mrr@10	0.781
cosine_map@100	0.7845

Information Retrieval

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

  {
      "truncate_dim": 256
  }
  

Metric	Value
cosine_accuracy@1	0.7129
cosine_accuracy@3	0.8214
cosine_accuracy@5	0.86
cosine_accuracy@10	0.9043
cosine_precision@1	0.7129
cosine_precision@3	0.2738
cosine_precision@5	0.172
cosine_precision@10	0.0904
cosine_recall@1	0.7129
cosine_recall@3	0.8214
cosine_recall@5	0.86
cosine_recall@10	0.9043
cosine_ndcg@10	0.8072
cosine_mrr@10	0.7762
cosine_map@100	0.7797

Information Retrieval

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

  {
      "truncate_dim": 128
  }
  

Metric	Value
cosine_accuracy@1	0.71
cosine_accuracy@3	0.81
cosine_accuracy@5	0.8443
cosine_accuracy@10	0.8986
cosine_precision@1	0.71
cosine_precision@3	0.27
cosine_precision@5	0.1689
cosine_precision@10	0.0899
cosine_recall@1	0.71
cosine_recall@3	0.81
cosine_recall@5	0.8443
cosine_recall@10	0.8986
cosine_ndcg@10	0.8013
cosine_mrr@10	0.7706
cosine_map@100	0.7744

Information Retrieval

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

  {
      "truncate_dim": 64
  }
  

Metric	Value
cosine_accuracy@1	0.6686
cosine_accuracy@3	0.78
cosine_accuracy@5	0.8257
cosine_accuracy@10	0.8757
cosine_precision@1	0.6686
cosine_precision@3	0.26
cosine_precision@5	0.1651
cosine_precision@10	0.0876
cosine_recall@1	0.6686
cosine_recall@3	0.78
cosine_recall@5	0.8257
cosine_recall@10	0.8757
cosine_ndcg@10	0.7698
cosine_mrr@10	0.7363
cosine_map@100	0.7409

Training Details

Training Dataset

json

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

  	anchor	positive
  type	string	string
  details	min: 9 tokens mean: 20.16 tokens max: 51 tokens	min: 4 tokens mean: 45.99 tokens max: 281 tokens

• Samples:

  anchor	positive
  What percentage of total revenues did STELARA account for in fiscal 2023 for the Company?	Sales of the Company’s largest product, STELARA (ustekinumab), accounted for approximately 12.8% of the Company's total revenues for fiscal 2023.
  What is the effective date for the new accounting standard ASU No. 2022-04 regarding liabilities in supplier finance programs?	In September 2022, the FASB issued ASU No. 2022-04, “Liabilities—Supplier Finance Programs (Topic 405-50) - Disclosure of Supplier Finance Program Obligations,” which is effective for fiscal years beginning after December 15, 2022, including interim periods within those fiscal years.
  What was the pre-tax net favorable prior period development for 2022 and what factors contributed to it?	The pre-tax net favorable prior period development for 2022 was $876 million. Adverse development factors like molestation claims, primarily reviver statute-related compromising $155 million, and $113 million related to legacy asbestos and environmental exposures significantly influenced this outcome.

• 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
• fp16: 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: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• 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

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	1.6789	-	-	-	-	-
0.9746	12	-	0.7976	0.8019	0.7944	0.7781	0.7387
1.6244	20	0.6377	-	-	-	-	-
1.9492	24	-	0.8071	0.8080	0.8016	0.7940	0.7594
2.4365	30	0.5295	-	-	-	-	-
2.9239	36	-	0.8110	0.8122	0.8067	0.8000	0.7697
3.2487	40	0.4367	-	-	-	-	-
3.8985	48	-	0.8111	0.8116	0.8072	0.8013	0.7698

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.11.12
• Sentence Transformers: 4.1.0
• Transformers: 4.41.2
• PyTorch: 2.1.2+cu121
• Accelerate: 1.5.2
• 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}
}