metadata

language:
  - en
license: apache-2.0
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:6300
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
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
widget:
  - source_sentence: >-
      Forward-looking statements may appear throughout this report, including
      without limitation, the following sections: “Management's Discussion and
      Analysis,” “Risk Factors” and "Notes 4, 8 and 13 to the Consolidated
      Financial Statements."
    sentences:
      - >-
        How does a one-year adjustment in the 2023 expected retirement age for
        U.S. plans affect income before income taxes?
      - >-
        Which sections of the report might contain forward-looking statements
        according to the text?
      - >-
        What was the allowance for loan and lease losses at Bank of America as
        of December 31, 2022?
  - source_sentence: Interest income | $ | 267 | | | $ | 29 | | $ | 238 | | 821 | %
    sentences:
      - >-
        What are the key risks and uncertainties mentioned that could impact the
        validity of DaVita's forward-looking statements?
      - >-
        How did the interest income change in fiscal year 2023 compared to the
        previous year?
      - >-
        What are some of the main competitive factors in the interactive
        entertainment industry?
  - source_sentence: >-
      Veklury received U.S. Food and Drug Administration (FDA) and European
      Commission (EC) approval to treat COVID-19 in patients with mild to severe
      hepatic impairment and those with severe renal impairment, including those
      on dialysis.
    sentences:
      - What significant regulatory approvals did Gilead's Veklury receive?
      - >-
        What type of information is included under the caption "Legal
        Proceedings" in an Annual Report on Form 10-K?
      - >-
        What was the cash change related to changes in operating assets and
        liabilities, including working capital, in 2022?
  - source_sentence: >-
      The net value of property, plant, and equipment for the consolidated group
      increased from $12,028 million in 2022 to $12,680 million in 2023.
    sentences:
      - >-
        What steps does the company plan to take next after discussing data with
        regulators and key opinion leaders?
      - >-
        How does the company manage fluctuations in foreign currency exchange
        rates?
      - >-
        What was the increase in property, plant, and equipment net value from
        2022 to 2023 for the consolidated group?
  - source_sentence: >-
      The effective duration of our total AFS and HTM investments securities as
      of December 31, 2023 is approximately 3.9 years.
    sentences:
      - >-
        What are the effective durations of the total Available-for-Sale (AFS)
        and Held-to-Maturity (HTM) investment securities as of December 31,
        2023?
      - >-
        What was the net unit growth percentage for Hilton in the year ended
        December 31, 2023?
      - What does goodwill represent in accounting?
pipeline_tag: sentence-similarity
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: 0.7285714285714285
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8485714285714285
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8885714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9214285714285714
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7285714285714285
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28285714285714286
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17771428571428569
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09214285714285712
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7285714285714285
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8485714285714285
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8885714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9214285714285714
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8274202252845575
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7969903628117911
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7998523047098398
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.72
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8442857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8785714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.92
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.72
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2814285714285714
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17571428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09199999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.72
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8442857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8785714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.92
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8213589464095679
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7896825396825394
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7926726035572866
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7214285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8385714285714285
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8742857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9128571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7214285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27952380952380956
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17485714285714282
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09128571428571428
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7214285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8385714285714285
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8742857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9128571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8190844047519252
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7888673469387758
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7921199469128796
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6971428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8328571428571429
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8671428571428571
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9057142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6971428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2776190476190476
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1734285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09057142857142855
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6971428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8328571428571429
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8671428571428571
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9057142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8054254319689889
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7729421768707481
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.776216648701894
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6614285714285715
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7985714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8442857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8814285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6614285714285715
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26619047619047614
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16885714285714284
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08814285714285712
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6614285714285715
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7985714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8442857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8814285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7728992637054746
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.737815759637188
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7417951294330247
            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("Hritikmore/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'The effective duration of our total AFS and HTM investments securities as of December 31, 2023 is approximately 3.9 years.',
    'What are the effective durations of the total Available-for-Sale (AFS) and Held-to-Maturity (HTM) investment securities as of December 31, 2023?',
    'What was the net unit growth percentage for Hilton in the year ended December 31, 2023?',
]
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	0.7286
cosine_accuracy@3	0.8486
cosine_accuracy@5	0.8886
cosine_accuracy@10	0.9214
cosine_precision@1	0.7286
cosine_precision@3	0.2829
cosine_precision@5	0.1777
cosine_precision@10	0.0921
cosine_recall@1	0.7286
cosine_recall@3	0.8486
cosine_recall@5	0.8886
cosine_recall@10	0.9214
cosine_ndcg@10	0.8274
cosine_mrr@10	0.797
cosine_map@100	0.7999

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.72
cosine_accuracy@3	0.8443
cosine_accuracy@5	0.8786
cosine_accuracy@10	0.92
cosine_precision@1	0.72
cosine_precision@3	0.2814
cosine_precision@5	0.1757
cosine_precision@10	0.092
cosine_recall@1	0.72
cosine_recall@3	0.8443
cosine_recall@5	0.8786
cosine_recall@10	0.92
cosine_ndcg@10	0.8214
cosine_mrr@10	0.7897
cosine_map@100	0.7927

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7214
cosine_accuracy@3	0.8386
cosine_accuracy@5	0.8743
cosine_accuracy@10	0.9129
cosine_precision@1	0.7214
cosine_precision@3	0.2795
cosine_precision@5	0.1749
cosine_precision@10	0.0913
cosine_recall@1	0.7214
cosine_recall@3	0.8386
cosine_recall@5	0.8743
cosine_recall@10	0.9129
cosine_ndcg@10	0.8191
cosine_mrr@10	0.7889
cosine_map@100	0.7921

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6971
cosine_accuracy@3	0.8329
cosine_accuracy@5	0.8671
cosine_accuracy@10	0.9057
cosine_precision@1	0.6971
cosine_precision@3	0.2776
cosine_precision@5	0.1734
cosine_precision@10	0.0906
cosine_recall@1	0.6971
cosine_recall@3	0.8329
cosine_recall@5	0.8671
cosine_recall@10	0.9057
cosine_ndcg@10	0.8054
cosine_mrr@10	0.7729
cosine_map@100	0.7762

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6614
cosine_accuracy@3	0.7986
cosine_accuracy@5	0.8443
cosine_accuracy@10	0.8814
cosine_precision@1	0.6614
cosine_precision@3	0.2662
cosine_precision@5	0.1689
cosine_precision@10	0.0881
cosine_recall@1	0.6614
cosine_recall@3	0.7986
cosine_recall@5	0.8443
cosine_recall@10	0.8814
cosine_ndcg@10	0.7729
cosine_mrr@10	0.7378
cosine_map@100	0.7418

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: 2 tokens
mean: 45.87 tokens
max: 272 tokens

min: 2 tokens
mean: 20.43 tokens
max: 41 tokens

	positive	anchor
type	string	string
details	min: 2 tokens mean: 45.87 tokens max: 272 tokens	min: 2 tokens mean: 20.43 tokens max: 41 tokens

Samples:

positive	anchor
`Significant judgment is required in evaluating our tax positions and during the ordinary course of business, there are many transactions and calculations for which the ultimate tax settlement is uncertain. As a result, we recognize the effect of this uncertainty on our tax attributes or taxes payable based on our estimates of the eventual outcome.`	`Why might the company's tax settlements vary?`
`OPSUMIT is used for the treatment of pediatric pulmonary arterial hypertension.`	`What medical condition does OPSUMIT treat?`
`Tangible equity ratios and tangible book value per share of common stock are non-GAAP financial measures. For more information on these ratios and corresponding reconciliations to GAAP financial measures, see Supplemental Financial Data and Non-GAAP Reconciliations.`	`What is the tangible equity ratio considered according to standard financial measures?`

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
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 2
lr_scheduler_type: cosine
warmup_ratio: 0.1
tf32: False
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: 8
per_device_eval_batch_size: 8
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: 2
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: 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: {'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
0.2030	10	0.7168	-	-	-	-	-
0.4061	20	0.3345	-	-	-	-	-
0.6091	30	0.2234	-	-	-	-	-
0.8122	40	0.2126	-	-	-	-	-
0.9949	49	-	0.7796	0.7844	0.7905	0.7293	0.7973
1.0152	50	0.2301	-	-	-	-	-
1.2183	60	0.1595	-	-	-	-	-
1.4213	70	0.1082	-	-	-	-	-
1.6244	80	0.0911	-	-	-	-	-
1.8274	90	0.1068	-	-	-	-	-
1.9898	98	-	0.7762	0.7921	0.7927	0.7418	0.7999

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
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}
}