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: The two patent families both expire in the United States in 2029.
    sentences:
      - >-
        What method is used to record amortization and costs for owned content
        that is predominantly monetized on an individual basis?
      - >-
        What year do the patent families related to DARZALEX expire in the
        United States?
      - >-
        What was the primary reason for the net cash used in investing
        activities in 2022?
  - source_sentence: >-
      In October 2020, Fortis Advisors LLC filed a complaint against Ethicon
      Inc. and others in Delaware's Court of Chancery. The lawsuit alleges
      breach of contract and fraud related to Ethicon's acquisition of Auris
      Health Inc. in 2019. The case underwent a partial dismissal in December
      2021, and as of January 2024, the trial's decision is pending.
    sentences:
      - >-
        What types of payment rates are used for dialysis treatments and
        associated pharmaceuticals?
      - >-
        What legal claims does Fortis Advisors LLC allege against Ethicon Inc.
        in the lawsuit related to the acquisition of Auris Health Inc.?
      - >-
        What were the key components of the acquisition deal between ICE and
        Black Knight completed on September 5, 2023?
  - source_sentence: >-
      Net cash provided by operating activities was $712.2 million and $223.7
      million for the year ended December 31, 2023 and 2022, respectively. The
      increase was primarily driven by timing of payments to vendors and timing
      of the receipt of payments from our customers, as well as an increase in
      interest income.
    sentences:
      - >-
        What caused the increase in net cash provided by operating activities
        between 2022 and 2023?
      - >-
        How long did Joanne D. Smith serve as the Vice President - Marketing at
        Delta?
      - >-
        How does the management experience of Mr. Robert G. Goldstein benefit
        the company?
  - source_sentence: >-
      We believe that, to varying degrees, our trademarks, trade names,
      copyrights, proprietary processes, trade secrets, trade dress, domain
      names and similar intellectual property add significant value to our
      business
    sentences:
      - >-
        What were the net interest expense on pre-acquisition-related debt and
        the cost associated with the extinguishment of senior notes for 2022 as
        part of non-GAAP adjustments?
      - >-
        How did the fluctuation in foreign currency exchange rates impact the
        consolidated net operating revenues in 2023?
      - >-
        What does the company believe adds significant value to its business
        regarding intellectual property?
  - source_sentence: >-
      The consolidated financial statements are incorporated by reference in the
      Annual Report on Form 10-K, indicating they are treated as part of the
      document for legal and reporting purposes.
    sentences:
      - >-
        What does it mean for financial statements to be incorporated by
        reference?
      - What is contained within the pages 163-309 of the financial section?
      - >-
        What were the key business segments of The Goldman Sachs Group, Inc. as
        reported in their 2023 financial disclosures?
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: 0.7014285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8271428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8714285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9028571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7014285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2757142857142857
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17428571428571427
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09028571428571427
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7014285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8271428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8714285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9028571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8043195367351605
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7724552154195008
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7766441682397275
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8328571428571429
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8685714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9042857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2776190476190476
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17371428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09042857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8328571428571429
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8685714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9042857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.804097602951568
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.771829365079365
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7756860707173107
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8214285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8557142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.89
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27380952380952384
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17114285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08899999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8214285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8557142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.89
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7977242461477416
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7678412698412698
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7726663884946474
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6785714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8257142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8528571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8857142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6785714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2752380952380953
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17057142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08857142857142856
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6785714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8257142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8528571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8857142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7864311013349103
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.754115079365079
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7585731100549844
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6642857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7828571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8157142857142857
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8642857142857143
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6642857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26095238095238094
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16314285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08642857142857142
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6642857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7828571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8157142857142857
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8642857142857143
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7634746514041137
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7313633786848066
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7360563668571922
            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("Yohhei/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'The consolidated financial statements are incorporated by reference in the Annual Report on Form 10-K, indicating they are treated as part of the document for legal and reporting purposes.',
    'What does it mean for financial statements to be incorporated by reference?',
    'What is contained within the pages 163-309 of the financial section?',
]
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	0.7014
cosine_accuracy@3	0.8271
cosine_accuracy@5	0.8714
cosine_accuracy@10	0.9029
cosine_precision@1	0.7014
cosine_precision@3	0.2757
cosine_precision@5	0.1743
cosine_precision@10	0.0903
cosine_recall@1	0.7014
cosine_recall@3	0.8271
cosine_recall@5	0.8714
cosine_recall@10	0.9029
cosine_ndcg@10	0.8043
cosine_mrr@10	0.7725
cosine_map@100	0.7766

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7
cosine_accuracy@3	0.8329
cosine_accuracy@5	0.8686
cosine_accuracy@10	0.9043
cosine_precision@1	0.7
cosine_precision@3	0.2776
cosine_precision@5	0.1737
cosine_precision@10	0.0904
cosine_recall@1	0.7
cosine_recall@3	0.8329
cosine_recall@5	0.8686
cosine_recall@10	0.9043
cosine_ndcg@10	0.8041
cosine_mrr@10	0.7718
cosine_map@100	0.7757

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7
cosine_accuracy@3	0.8214
cosine_accuracy@5	0.8557
cosine_accuracy@10	0.89
cosine_precision@1	0.7
cosine_precision@3	0.2738
cosine_precision@5	0.1711
cosine_precision@10	0.089
cosine_recall@1	0.7
cosine_recall@3	0.8214
cosine_recall@5	0.8557
cosine_recall@10	0.89
cosine_ndcg@10	0.7977
cosine_mrr@10	0.7678
cosine_map@100	0.7727

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6786
cosine_accuracy@3	0.8257
cosine_accuracy@5	0.8529
cosine_accuracy@10	0.8857
cosine_precision@1	0.6786
cosine_precision@3	0.2752
cosine_precision@5	0.1706
cosine_precision@10	0.0886
cosine_recall@1	0.6786
cosine_recall@3	0.8257
cosine_recall@5	0.8529
cosine_recall@10	0.8857
cosine_ndcg@10	0.7864
cosine_mrr@10	0.7541
cosine_map@100	0.7586

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6643
cosine_accuracy@3	0.7829
cosine_accuracy@5	0.8157
cosine_accuracy@10	0.8643
cosine_precision@1	0.6643
cosine_precision@3	0.261
cosine_precision@5	0.1631
cosine_precision@10	0.0864
cosine_recall@1	0.6643
cosine_recall@3	0.7829
cosine_recall@5	0.8157
cosine_recall@10	0.8643
cosine_ndcg@10	0.7635
cosine_mrr@10	0.7314
cosine_map@100	0.7361

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: 45.16 tokens
max: 512 tokens

min: 7 tokens
mean: 20.44 tokens
max: 45 tokens

	positive	anchor
type	string	string
details	min: 8 tokens mean: 45.16 tokens max: 512 tokens	min: 7 tokens mean: 20.44 tokens max: 45 tokens

Samples:

positive	anchor
`Highlights during fiscal year 2023 include the following: We generated $18,085 million of cash from operations.`	`What was the amount of cash generated from operations by the company in fiscal year 2023?`
`U.S. government and agency securities`	$
`For assets under development, assets are grouped and assessed for impairment by estimating the undiscounted cash flows, which include remaining construction costs, over the asset's remaining useful life. If cash flows do not exceed the carrying amount, impairment based on fair value versus carrying value is considered.`	`How is the impairment of assets assessed for projects still under development?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: True
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.8122	10	1.5313	-	-	-	-	-
0.9746	12	-	0.7416	0.7521	0.7554	0.7079	0.7609
1.6244	20	0.6553	-	-	-	-	-
1.9492	24	-	0.7549	0.7693	0.7732	0.7318	0.7716
2.4365	30	0.445	-	-	-	-	-
2.9239	36	-	0.7565	0.7738	0.7746	0.7367	0.7763
3.2487	40	0.3917	-	-	-	-	-
3.8985	48	-	0.7586	0.7727	0.7757	0.7361	0.7766

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.8.10
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.32.0
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}
}