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: >-
      Total net additions to property and equipment for AWS in 2023 amounted to
      $24,843 million.
    sentences:
      - >-
        What technological feature helps protect digital transactions in the
        Visa Token Service?
      - >-
        What was the total net addition to property and equipment for AWS in the
        year 2023?
      - >-
        By what proportion did net cash used in financing activities increase
        from 2022 to 2023?
  - source_sentence: >-
      Leases generally contain one or more of the following options, which the
      Company can exercise at the end of the initial term: (a) renew the lease
      for a defined number of years at the then-fair market rental rate or rate
      stipulated in the lease agreement; (b) purchase the property at the
      then-fair market value or purchase price stated in the agreement; or (c) a
      right of first refusal in the event of a third-party offer.
    sentences:
      - >-
        What are the requirements for health insurers and group health plans in
        providing cost estimates to consumers?
      - >-
        What options does the company have at the end of the lease term for
        their leased properties?
      - >-
        How much did the company incur in intangible amortization costs related
        to the eOne acquisition in 2022?
  - source_sentence: >-
      We recorded an acquisition termination cost of $1.35 billion in fiscal
      year 2023 reflecting the write-off of the prepayment provided at signing.
    sentences:
      - >-
        How much did NVIDIA record as an acquisition termination cost in fiscal
        year 2023 related to the Arm Share Purchase Agreement?
      - >-
        What is included in the consolidated financial statements and
        accompanying notes mentioned in Part IV, Item 15(a)(1) of the Annual
        Report on Form 10-K?
      - >-
        What risks are associated with projecting the effectiveness of internal
        controls into future periods as mentioned?
  - source_sentence: Item 8 is labeled as Financial Statements and Supplementary Data.
    sentences:
      - >-
        What was the percentage of trading days in 2023 where trading-related
        revenue was recorded as positive?
      - >-
        How is the discount rate for the Family Dollar goodwill impairment
        evaluation determined?
      - What is the title of Item 8 in the financial document?
  - source_sentence: >-
      Details about legal proceedings are included in Part II, Item 8,
      "Financial Statements and Supplementary Data" of the Annual Report on Form
      10-K, under the caption "Legal Proceedings".
    sentences:
      - >-
        Where can details about legal proceedings be located in an Annual Report
        on Form 10-K?
      - >-
        How many stores did AutoZone operate in the United States as of August
        26, 2023?
      - >-
        In the context of Hewlett Packard Enterprise's recent financial
        discussions, what factors are expected to impact their operational costs
        and revenue growth moving forward?
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: 0.7071428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8414285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.88
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9314285714285714
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7071428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28047619047619043
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.176
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09314285714285712
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7071428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8414285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.88
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9314285714285714
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8207437059171859
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7853486394557823
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7881907906804949
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6957142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8385714285714285
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8757142857142857
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.93
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6957142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2795238095238095
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17514285714285713
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09299999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6957142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8385714285714285
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8757142857142857
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.93
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8149439460863356
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7780714285714285
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.781021025356189
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6885714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.83
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8742857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9142857142857143
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6885714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27666666666666667
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17485714285714282
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09142857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6885714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.83
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8742857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9142857142857143
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8060991379418679
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7710873015873015
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7751792513774886
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6771428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8214285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8614285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9142857142857143
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6771428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2738095238095238
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17228571428571426
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09142857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6771428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8214285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8614285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9142857142857143
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7979494993398927
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7605890022675734
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7639633810343436
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6557142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7871428571428571
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8271428571428572
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8714285714285714
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6557142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2623809523809524
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1654285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08714285714285713
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6557142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7871428571428571
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8271428571428572
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8714285714285714
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7664083634078753
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7326604308390022
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7375736792740525
            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("dustyatx/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Details about legal proceedings are included in Part II, Item 8, "Financial Statements and Supplementary Data" of the Annual Report on Form 10-K, under the caption "Legal Proceedings".',
    'Where can details about legal proceedings be located in an Annual Report on Form 10-K?',
    'How many stores did AutoZone operate in the United States as of August 26, 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	0.7071
cosine_accuracy@3	0.8414
cosine_accuracy@5	0.88
cosine_accuracy@10	0.9314
cosine_precision@1	0.7071
cosine_precision@3	0.2805
cosine_precision@5	0.176
cosine_precision@10	0.0931
cosine_recall@1	0.7071
cosine_recall@3	0.8414
cosine_recall@5	0.88
cosine_recall@10	0.9314
cosine_ndcg@10	0.8207
cosine_mrr@10	0.7853
cosine_map@100	0.7882

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6957
cosine_accuracy@3	0.8386
cosine_accuracy@5	0.8757
cosine_accuracy@10	0.93
cosine_precision@1	0.6957
cosine_precision@3	0.2795
cosine_precision@5	0.1751
cosine_precision@10	0.093
cosine_recall@1	0.6957
cosine_recall@3	0.8386
cosine_recall@5	0.8757
cosine_recall@10	0.93
cosine_ndcg@10	0.8149
cosine_mrr@10	0.7781
cosine_map@100	0.781

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6886
cosine_accuracy@3	0.83
cosine_accuracy@5	0.8743
cosine_accuracy@10	0.9143
cosine_precision@1	0.6886
cosine_precision@3	0.2767
cosine_precision@5	0.1749
cosine_precision@10	0.0914
cosine_recall@1	0.6886
cosine_recall@3	0.83
cosine_recall@5	0.8743
cosine_recall@10	0.9143
cosine_ndcg@10	0.8061
cosine_mrr@10	0.7711
cosine_map@100	0.7752

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6771
cosine_accuracy@3	0.8214
cosine_accuracy@5	0.8614
cosine_accuracy@10	0.9143
cosine_precision@1	0.6771
cosine_precision@3	0.2738
cosine_precision@5	0.1723
cosine_precision@10	0.0914
cosine_recall@1	0.6771
cosine_recall@3	0.8214
cosine_recall@5	0.8614
cosine_recall@10	0.9143
cosine_ndcg@10	0.7979
cosine_mrr@10	0.7606
cosine_map@100	0.764

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6557
cosine_accuracy@3	0.7871
cosine_accuracy@5	0.8271
cosine_accuracy@10	0.8714
cosine_precision@1	0.6557
cosine_precision@3	0.2624
cosine_precision@5	0.1654
cosine_precision@10	0.0871
cosine_recall@1	0.6557
cosine_recall@3	0.7871
cosine_recall@5	0.8271
cosine_recall@10	0.8714
cosine_ndcg@10	0.7664
cosine_mrr@10	0.7327
cosine_map@100	0.7376

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: 8 tokens
mean: 45.94 tokens
max: 512 tokens

min: 7 tokens
mean: 20.7 tokens
max: 42 tokens

	positive	anchor
type	string	string
details	min: 8 tokens mean: 45.94 tokens max: 512 tokens	min: 7 tokens mean: 20.7 tokens max: 42 tokens

Samples:

positive	anchor
`The company must continuously strengthen its capabilities in marketing and innovation to compete in a digital environment and maintain brand loyalty and marketallability. In addition, it is increasing its investments in e-commerce to support retail and meal delivery services, offering more package sizes that are fit-for-purpose for online sales and shifting more consumer and trade promotions to digital.`	`What strategies is the company employing to enhance its competitiveness in a digital environment?`
`Fedflowing expanded or relocated its hub and linehaul network, FedEx Ground also introduced new safety technologies, set new driver standards, and made operational enhancements for safer handling of heavy items.`	`What specific changes has FedEx Ground made for vehicle and driver safety?`
`The debt financing, which is being provided by a syndicate of Chinese financial institutions, contains certain covenants and a maximum borrowing limit of ¥29.7 billion RMB (approximately $4.2 billion).`	`What is the maximum borrowing limit of the debt financing provided by the syndicate of Chinese financial institutions for Universal Beijing Resort?`

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
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: 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
eval_on_start: False
eval_use_gather_object: 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	1.5212	-	-	-	-	-
0.9746	12	-	0.7439	0.7556	0.7670	0.7142	0.7717
1.6244	20	0.6418	-	-	-	-	-
1.9492	24	-	0.7592	0.7743	0.7787	0.7331	0.7839
2.4365	30	0.4411	-	-	-	-	-
2.9239	36	-	0.7623	0.7757	0.7816	0.7365	0.7902
3.2487	40	0.3917	-	-	-	-	-
3.8985	48	-	0.764	0.7752	0.781	0.7376	0.7882

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.9
Sentence Transformers: 3.0.1
Transformers: 4.44.2
PyTorch: 2.4.0+cu121
Accelerate: 0.33.0
Datasets: 2.21.0
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}
}