metadata

language:
  - en
license: apache-2.0
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
widget:
  - source_sentence: >-
      As of the end of 2023, Hilton's development pipeline included projects in
      118 countries and territories.
    sentences:
      - >-
        What was the total net income attributed to AT&T common stockholders in
        2023?
      - >-
        How many countries and territories did Hilton's development pipeline
        encompass as of the end of 2023?
      - >-
        What caused the increase in Medicare receivables in 2023 compared to
        2022?
  - source_sentence: >-
      Alex G. Balazs was appointed as the Executive Vice President and Chief
      Technology Officer effective September 5, 2023.
    sentences:
      - What page of IBM's Form 10-K contains the Financial Statement Schedule?
      - >-
        When was Alex G. Balazs appointed as the Executive Vice President and
        Chief Technology Officer?
      - >-
        How much were the valuation allowances provided for deferred tax assets
        related to loss carryforwards as of December 31, 2023?
  - source_sentence: >-
      HP's global wellness program emphasizes five pillars of wellness:
      physical, financial, emotional, life balance, and social/community.
    sentences:
      - >-
        What are the five pillars of wellness emphasized in HP's global wellness
        program?
      - >-
        What was the fair value of money market mutual funds measured at as of
        January 31, 2023 and how was it categorized in the fair value hierarchy?
      - >-
        What amount was authorized for future share repurchases by the company
        as of October 31, 2023?
  - source_sentence: >-
      Item 3, titled 'Legal Proceedings' in a 10-K filing, directs to Note 16
      where specific information is further detailed in Item 8 of Part II.
    sentences:
      - >-
        What was the grant date fair value of options vested for HP in fiscal
        years 2023, 2022, and 2021?
      - >-
        What is the balance at the end of the year for Comcast's Total Equity in
        2023?
      - What is indicated by Item 3, 'Legal Proceedings', in a 10-K filing?
  - source_sentence: >-
      During 2023, we received approximately $220 of cash collateral, on a net
      basis.
    sentences:
      - How much cash collateral did AT&T receive on a net basis during 2023?
      - >-
        What percentage of FedEx Corporation's consolidated revenues did jet
        fuel costs represent in 2023?
      - >-
        What measures has Bank of America taken to streamline its organizational
        structure?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
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: 0.7128571428571429
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8428571428571429
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8842857142857142
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.92
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7128571428571429
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28095238095238095
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17685714285714288
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09199999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7128571428571429
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8428571428571429
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8842857142857142
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.92
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8195233962517928
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7870022675736963
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7905145024165581
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7157142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8457142857142858
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8814285714285715
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9228571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7157142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2819047619047619
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1762857142857143
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09228571428571428
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7157142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8457142857142858
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8814285714285715
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9228571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.821183673183428
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7884829931972789
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7916656681436871
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7114285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8414285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8842857142857142
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9157142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7114285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28047619047619043
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17685714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09157142857142858
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7114285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8414285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8842857142857142
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9157142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8157881706696753
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7834812925170066
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7870779881453726
            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: 0.82
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8685714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9057142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6957142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2733333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1737142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09057142857142857
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6957142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.82
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8685714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9057142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8018105093606251
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7683497732426302
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7722509873826792
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6528571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7942857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8314285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8757142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6528571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26476190476190475
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1662857142857143
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08757142857142856
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6528571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7942857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8314285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8757142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7667522193115596
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7315833333333331
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7359673420065519
            name: Cosine Map@100

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("sud-962081/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'During 2023, we received approximately $220 of cash collateral, on a net basis.',
    'How much cash collateral did AT&T receive on a net basis during 2023?',
    "What percentage of FedEx Corporation's consolidated revenues did jet fuel costs represent in 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

Datasets: dim_768, dim_512, dim_256, dim_128 and dim_64
Evaluated with InformationRetrievalEvaluator

Metric	dim_768	dim_512	dim_256	dim_128	dim_64
cosine_accuracy@1	0.7129	0.7157	0.7114	0.6957	0.6529
cosine_accuracy@3	0.8429	0.8457	0.8414	0.82	0.7943
cosine_accuracy@5	0.8843	0.8814	0.8843	0.8686	0.8314
cosine_accuracy@10	0.92	0.9229	0.9157	0.9057	0.8757
cosine_precision@1	0.7129	0.7157	0.7114	0.6957	0.6529
cosine_precision@3	0.281	0.2819	0.2805	0.2733	0.2648
cosine_precision@5	0.1769	0.1763	0.1769	0.1737	0.1663
cosine_precision@10	0.092	0.0923	0.0916	0.0906	0.0876
cosine_recall@1	0.7129	0.7157	0.7114	0.6957	0.6529
cosine_recall@3	0.8429	0.8457	0.8414	0.82	0.7943
cosine_recall@5	0.8843	0.8814	0.8843	0.8686	0.8314
cosine_recall@10	0.92	0.9229	0.9157	0.9057	0.8757
cosine_ndcg@10	0.8195	0.8212	0.8158	0.8018	0.7668
cosine_mrr@10	0.787	0.7885	0.7835	0.7683	0.7316
cosine_map@100	0.7905	0.7917	0.7871	0.7723	0.736

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: 4 tokens
mean: 44.47 tokens
max: 260 tokens

min: 9 tokens
mean: 20.17 tokens
max: 43 tokens

	positive	anchor
type	string	string
details	min: 4 tokens mean: 44.47 tokens max: 260 tokens	min: 9 tokens mean: 20.17 tokens max: 43 tokens

Samples:

positive	anchor
`SmartFlex benefits and the 'Best of Both' work model at The Hershey Company allow employees to balance professional and personal demands through flexible work arrangements.`	`How does The Hershey Company ensure flexibility and work-life balance for its employees?`
`In February 2024, our Board authorized an additional $2.0 billion stock repurchase program, with no expiration from the date of authorization.`	`What amount was authorized for common stock repurchase by the company's Board in February 2024?`
`Beginning in 2025, the first GM EVs will be constructed using the North American Charging Standard (NACS) hardware.`	`What significant change is set for General Motors' EVs starting in 2025 regarding charging hardware?`

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
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: 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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: 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
1.0	7	-	0.8036	0.8049	0.7942	0.7726	0.7375
1.4848	10	2.2028	-	-	-	-	-
2.0	14	-	0.8169	0.8173	0.8127	0.8000	0.7602
2.9697	20	0.9836	-	-	-	-	-
3.0	21	-	0.8187	0.8214	0.8142	0.8017	0.7658
3.4848	24	-	0.8195	0.8212	0.8158	0.8018	0.7668

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.47.0
PyTorch: 2.5.1+cu121
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

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}
}