metadata

base_model: BAAI/bge-base-en-v1.5
datasets: []
language:
  - en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:6300
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      As of December 31, 2023, deferred revenues for unsatisfied performance
      obligations consisted of $769 million related to Hilton Honors that will
      be recognized as revenue over approximately the next two years.
    sentences:
      - How many shares of common stock were issued in both 2022 and 2023?
      - >-
        What is the projected timeline for recognizing revenue from deferred
        revenues related to Hilton Honors as of December 31, 2023?
      - >-
        What acquisitions did CVS Health Corporation complete in 2023 to enhance
        their care delivery strategy?
  - source_sentence: >-
      If a good or service does not qualify as distinct, it is combined with the
      other non-distinct goods or services within the arrangement and these
      combined goods or services are treated as a single performance obligation
      for accounting purposes. The arrangement's transaction price is then
      allocated to each performance obligation based on the relative standalone
      selling price of each performance obligation.
    sentences:
      - >-
        What does the summary table indicate about the company's activities at
        the end of 2023?
      - >-
        What governs the treatment of goods or services that are not distinct
        within a contractual arrangement?
      - >-
        What is the basis for the Company to determine the Standalone Selling
        Price (SSP) for each distinct performance obligation in contracts with
        multiple performance obligations?
  - source_sentence: >-
      As of January 2023, the maximum daily borrowing capacity under the
      commercial paper program was approximately $2.75 billion.
    sentences:
      - >-
        What is the maximum daily borrowing capacity under the commercial paper
        program as of January 2023?
      - When does the Company's fiscal year end?
      - How much cash did acquisition activities use in 2023?
  - source_sentence: >-
      Federal Home Loan Bank borrowings had an interest rate of 4.59% in 2022,
      which increased to 5.14% in 2023.
    sentences:
      - >-
        By what percentage did the company's capital expenditures increase in
        fiscal 2023 compared to fiscal 2022?
      - >-
        What is the significance of Note 13 in the context of legal proceedings
        described in the Annual Report on Form 10-K?
      - >-
        How much did the Federal Home Loan Bank borrowings increase in terms of
        interest rates from 2022 to 2023?
  - source_sentence: >-
      The design of the Annual Report, with the consolidated financial
      statements placed immediately after Part IV, enhances the integration of
      financial data by maintaining a coherent structure.
    sentences:
      - >-
        How does the structure of the Annual Report on Form 10-K facilitate the
        integration of the consolidated financial statements?
      - Where can one find the Glossary of Terms and Acronyms in Item 8?
      - >-
        What part of the annual report contains the consolidated financial
        statements and accompanying notes?
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: 0.6957142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8171428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8628571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6957142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2723809523809524
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17257142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08999999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6957142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8171428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8628571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7971144469297426
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7641831065759639
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7681728985040082
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6942857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.81
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8514285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6942857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17028571428571426
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6942857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.81
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8514285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7951260604161544
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7617998866213151
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7658003405075238
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7014285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7971428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.85
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8885714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7014285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26571428571428574
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16999999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08885714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7014285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7971428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.85
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8885714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.793266992460996
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7629580498866213
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7678096436855835
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6957142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8014285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8357142857142857
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8842857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6957142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2671428571428571
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16714285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08842857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6957142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8014285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8357142857142857
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8842857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.787378246207931
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7566984126984126
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7613545312565108
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6571428571428571
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7871428571428571
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8285714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8757142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6571428571428571
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2623809523809524
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1657142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08757142857142856
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6571428571428571
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7871428571428571
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8285714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8757142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7655516319615892
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7303951247165531
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7349875161463472
            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("rbhatia46/bge-base-financial-nvidia-matryoshka")
# Run inference
sentences = [
    'The design of the Annual Report, with the consolidated financial statements placed immediately after Part IV, enhances the integration of financial data by maintaining a coherent structure.',
    'How does the structure of the Annual Report on Form 10-K facilitate the integration of the consolidated financial statements?',
    'Where can one find the Glossary of Terms and Acronyms in Item 8?',
]
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	0.6957
cosine_accuracy@3	0.8171
cosine_accuracy@5	0.8629
cosine_accuracy@10	0.9
cosine_precision@1	0.6957
cosine_precision@3	0.2724
cosine_precision@5	0.1726
cosine_precision@10	0.09
cosine_recall@1	0.6957
cosine_recall@3	0.8171
cosine_recall@5	0.8629
cosine_recall@10	0.9
cosine_ndcg@10	0.7971
cosine_mrr@10	0.7642
cosine_map@100	0.7682

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6943
cosine_accuracy@3	0.81
cosine_accuracy@5	0.8514
cosine_accuracy@10	0.9
cosine_precision@1	0.6943
cosine_precision@3	0.27
cosine_precision@5	0.1703
cosine_precision@10	0.09
cosine_recall@1	0.6943
cosine_recall@3	0.81
cosine_recall@5	0.8514
cosine_recall@10	0.9
cosine_ndcg@10	0.7951
cosine_mrr@10	0.7618
cosine_map@100	0.7658

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7014
cosine_accuracy@3	0.7971
cosine_accuracy@5	0.85
cosine_accuracy@10	0.8886
cosine_precision@1	0.7014
cosine_precision@3	0.2657
cosine_precision@5	0.17
cosine_precision@10	0.0889
cosine_recall@1	0.7014
cosine_recall@3	0.7971
cosine_recall@5	0.85
cosine_recall@10	0.8886
cosine_ndcg@10	0.7933
cosine_mrr@10	0.763
cosine_map@100	0.7678

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6957
cosine_accuracy@3	0.8014
cosine_accuracy@5	0.8357
cosine_accuracy@10	0.8843
cosine_precision@1	0.6957
cosine_precision@3	0.2671
cosine_precision@5	0.1671
cosine_precision@10	0.0884
cosine_recall@1	0.6957
cosine_recall@3	0.8014
cosine_recall@5	0.8357
cosine_recall@10	0.8843
cosine_ndcg@10	0.7874
cosine_mrr@10	0.7567
cosine_map@100	0.7614

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6571
cosine_accuracy@3	0.7871
cosine_accuracy@5	0.8286
cosine_accuracy@10	0.8757
cosine_precision@1	0.6571
cosine_precision@3	0.2624
cosine_precision@5	0.1657
cosine_precision@10	0.0876
cosine_recall@1	0.6571
cosine_recall@3	0.7871
cosine_recall@5	0.8286
cosine_recall@10	0.8757
cosine_ndcg@10	0.7656
cosine_mrr@10	0.7304
cosine_map@100	0.735

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: 45.53 tokens
max: 222 tokens

min: 8 tokens
mean: 20.3 tokens
max: 45 tokens

	positive	anchor
type	string	string
details	min: 6 tokens mean: 45.53 tokens max: 222 tokens	min: 8 tokens mean: 20.3 tokens max: 45 tokens

Samples:

positive	anchor
`Acquisition activity used cash of $765 million in 2023, primarily related to a Beauty acquisition.`	`How much cash did acquisition activities use in 2023?`
`In a financial report, Part IV Item 15 includes Exhibits and Financial Statement Schedules as mentioned.`	`What content can be expected under Part IV Item 15 in a financial report?`
`we had more than 8.3 million fiber consumer wireline broadband customers, adding 1.1 million during the year.`	`How many fiber consumer wireline broadband customers did the company have at the end of the year?`

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	1.5751	-	-	-	-	-
0.9746	12	-	-	-	-	-	0.7580
0.8122	10	0.6362	-	-	-	-	-
0.9746	12	-	0.7503	0.7576	0.7653	0.7282	0.7638
1.6244	20	0.4426	-	-	-	-	-
1.9492	24	-	0.7544	0.7662	0.7640	0.7311	0.7676
2.4365	30	0.3217	-	-	-	-	-
2.9239	36	-	0.7608	0.7684	0.7662	0.7341	0.7686
3.2487	40	0.2761	-	-	-	-	-
3.8985	48	-	0.7614	0.7678	0.7658	0.735	0.7682

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.6
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}
}