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: >-
      The company hedges foreign currency exchange-based cash flow variability
      of certain fees using forward contracts designated as hedging instruments.
      It also holds short-term forward contracts to offset exposure to
      fluctuations in certain of its foreign currency denominated cash balances
      and intercompany financing arrangements, without designating these forward
      contracts as hedging instruments.
    sentences:
      - >-
        What was the total stockholders' equity at Amazon.com, Inc. as of
        December 31, 2021?
      - >-
        How does the company manage fluctuations in foreign currency exchange
        rates?
      - >-
        What are some of the potential consequences for Meta Platforms, Inc.
        from inquiries or investigations as noted in the provided text?
  - source_sentence: >-
      The Financial Statement Schedule is located on page S-1 of IBM’s 2023 Form
      10-K.
    sentences:
      - >-
        How is Hewlett Packard addressing competition in the enterprise IT
        infrastructure market?
      - >-
        Where in IBM’s 2023 Form 10-K can the Financial Statement Schedule be
        found?
      - What was Intuit's Net Income in fiscal year 2023?
  - source_sentence: Sales of DARZALEX in 2023 showed a 22.2% increase over the previous year.
    sentences:
      - >-
        How much did DARZALEX sales increase in 2023 compared to the previous
        year?
      - What strategic focus does Etsy have for its marketplace?
      - Since when has Mr. Goodarzi been the President and CEO of Intuit?
  - source_sentence: >-
      Chubb Limited further advanced their goal of greater product, customer,
      and geographical diversification with incremental purchases that led to a
      controlling majority interest in Huatai Insurance Group Co. Ltd, owning
      about 76.5 percent as of July 1, 2023.
    sentences:
      - >-
        What are the primary sources of revenue for Salesforce, Inc. as
        described in their consolidated financial statements?
      - >-
        What acquisitions did Hershey complete to expand its snacking portfolio,
        and when did these occur?
      - >-
        What percentage of the Huatai Insurance Group Co. Ltd does Chubb Limited
        own as of July 1, 2023?
  - source_sentence: >-
      The consolidated balance sheets of Visa Inc. as of September 30, 2023,
      list the total current assets at $33,532 million.
    sentences:
      - >-
        What was the total of Visa Inc.'s current assets as of September 30,
        2023?
      - >-
        What was Garmin Ltd.'s net income for the fiscal year ended December 30,
        2023?
      - >-
        By what percentage did online sales grow in fiscal 2022 compared to
        fiscal 2021?
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: 0.6885714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8285714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8671428571428571
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9128571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6885714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27619047619047615
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1734285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09128571428571426
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6885714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8285714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8671428571428571
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9128571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8022848173323525
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7666422902494329
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7696751281834099
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6928571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8228571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8642857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.91
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6928571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27428571428571424
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17285714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09099999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6928571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8228571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8642857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.91
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8016907244180009
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7668412698412699
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.770110214157224
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6871428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8185714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8628571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9014285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6871428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27285714285714285
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17257142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09014285714285712
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6871428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8185714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8628571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9014285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7962767797304091
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7623021541950112
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7656765331908582
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6742857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8057142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8528571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8942857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6742857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26857142857142857
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17057142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08942857142857143
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6742857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8057142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8528571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8942857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7861958176742697
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7513151927437639
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7548627394954026
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6428571428571429
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7971428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8185714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8685714285714285
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6428571428571429
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26571428571428574
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1637142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08685714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6428571428571429
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7971428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8185714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8685714285714285
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7590638034734002
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7236972789115643
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7282650681776726
            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("WaheedLone/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'The consolidated balance sheets of Visa Inc. as of September 30, 2023, list the total current assets at $33,532 million.',
    "What was the total of Visa Inc.'s current assets as of September 30, 2023?",
    "What was Garmin Ltd.'s net income for the fiscal year ended December 30, 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	0.6886
cosine_accuracy@3	0.8286
cosine_accuracy@5	0.8671
cosine_accuracy@10	0.9129
cosine_precision@1	0.6886
cosine_precision@3	0.2762
cosine_precision@5	0.1734
cosine_precision@10	0.0913
cosine_recall@1	0.6886
cosine_recall@3	0.8286
cosine_recall@5	0.8671
cosine_recall@10	0.9129
cosine_ndcg@10	0.8023
cosine_mrr@10	0.7666
cosine_map@100	0.7697

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6929
cosine_accuracy@3	0.8229
cosine_accuracy@5	0.8643
cosine_accuracy@10	0.91
cosine_precision@1	0.6929
cosine_precision@3	0.2743
cosine_precision@5	0.1729
cosine_precision@10	0.091
cosine_recall@1	0.6929
cosine_recall@3	0.8229
cosine_recall@5	0.8643
cosine_recall@10	0.91
cosine_ndcg@10	0.8017
cosine_mrr@10	0.7668
cosine_map@100	0.7701

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6871
cosine_accuracy@3	0.8186
cosine_accuracy@5	0.8629
cosine_accuracy@10	0.9014
cosine_precision@1	0.6871
cosine_precision@3	0.2729
cosine_precision@5	0.1726
cosine_precision@10	0.0901
cosine_recall@1	0.6871
cosine_recall@3	0.8186
cosine_recall@5	0.8629
cosine_recall@10	0.9014
cosine_ndcg@10	0.7963
cosine_mrr@10	0.7623
cosine_map@100	0.7657

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6743
cosine_accuracy@3	0.8057
cosine_accuracy@5	0.8529
cosine_accuracy@10	0.8943
cosine_precision@1	0.6743
cosine_precision@3	0.2686
cosine_precision@5	0.1706
cosine_precision@10	0.0894
cosine_recall@1	0.6743
cosine_recall@3	0.8057
cosine_recall@5	0.8529
cosine_recall@10	0.8943
cosine_ndcg@10	0.7862
cosine_mrr@10	0.7513
cosine_map@100	0.7549

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6429
cosine_accuracy@3	0.7971
cosine_accuracy@5	0.8186
cosine_accuracy@10	0.8686
cosine_precision@1	0.6429
cosine_precision@3	0.2657
cosine_precision@5	0.1637
cosine_precision@10	0.0869
cosine_recall@1	0.6429
cosine_recall@3	0.7971
cosine_recall@5	0.8186
cosine_recall@10	0.8686
cosine_ndcg@10	0.7591
cosine_mrr@10	0.7237
cosine_map@100	0.7283

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: 45.17 tokens
max: 260 tokens

min: 7 tokens
mean: 20.38 tokens
max: 40 tokens

	positive	anchor
type	string	string
details	min: 6 tokens mean: 45.17 tokens max: 260 tokens	min: 7 tokens mean: 20.38 tokens max: 40 tokens

Samples:

positive	anchor
`Net revenue for fiscal year 2023 increased by $435 million compared to fiscal year 2022.`	`How did the net revenue for fiscal year 2023 compare to fiscal year 2022?`
`Adjusted Free Cash Flow is defined as operating cash flow less capital spending and excluding payments for the transitional tax resulting from the U.S. Tax Act.`	`How is Adjusted Free Cash Flow defined in the text?`
`During 2023, the Company’s net sales through its direct and indirect distribution channels accounted for 37% and 63%, respectively, of total net sales.`	`During 2023, what percentage of the Company’s net sales came from direct sales channels?`

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
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: False
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	1.6399	-	-	-	-	-
0.9746	12	-	0.7441	0.7580	0.7543	0.7068	0.7632
1.6244	20	0.6475	-	-	-	-	-
1.9492	24	-	0.7530	0.7653	0.7672	0.7244	0.7708
2.4365	30	0.4494	-	-	-	-	-
2.9239	36	-	0.7548	0.7653	0.7683	0.7297	0.7679
3.2487	40	0.4089	-	-	-	-	-
3.8985	48	-	0.7549	0.7657	0.7701	0.7283	0.7697

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}
}