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-small-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: >-
      We offer dual motor powertrain vehicles, which use two electric motors to
      maximize traction and performance in an all-wheel-drive configuration, as
      well as vehicle powertrain technology featuring three electric motors for
      further increased performance in certain versions of Model S and Model X,
      Cybertruck, and the Tesla Semi.
    sentences:
      - What is the purpose of The Home Depot Foundation?
      - What are the features of the company's vehicle powertrain technology?
      - Where can public access the company's SEC filings?
  - source_sentence: >-
      The litigation requests a declaration that the IRA violates Janssen’s
      rights under the First Amendment and the Fifth Amendment to the
      Constitution.
    sentences:
      - >-
        What changes occurred in the valuation of equity warrants from 2021 to
        2023?
      - >-
        What constitutional rights does Janssen claim the Inflation Reduction
        Act violates?
      - >-
        What was the cash paid for amounts included in the measurement of
        operating lease liabilities for the years 2021, 2022, and 2023?
  - source_sentence: >-
      After-tax earnings of other energy businesses decreased $332 million
      (24.5%) in 2023 compared to 2022. The decline reflected lower earnings at
      Northern Powergrid due to unfavorable results at a natural gas exploration
      project, including the write-off of capitalized exploration costs and
      lower gas production volumes and prices, as well as from higher deferred
      income tax expense related to the enactment of the Energy Profits Levy
      income tax in the United Kingdom. The earnings decline was also
      attributable to lower earnings from renewable energy and retail services
      businesses. The decline in renewable energy and retail services earnings
      was primarily due to lower income tax benefits, higher operating expenses,
      lower solar and wind generation at owned projects and the impact of
      unfavorable changes in valuations of derivatives contracts, partially
      offset by debt extinguishment gains.
    sentences:
      - >-
        What were the reasons for the decline in after-tax earnings of other
        energy businesses in 2023?
      - >-
        What was the main reason for the increase in the company's valuation
        allowance during fiscal 2023?
      - >-
        What were the net purchase amounts of treasury shares for the years
        ended December 31, 2022, and 2023?
  - source_sentence: >-
      The Phase 3 OAKTREE trial of obeldesivir in non-hospitalized participants
      without risk factors for developing severe COVID-19 did not meet its
      primary endpoint of improvement in time to symptom alleviation.
      Obeldesivir was well-tolerated in this large study population.
    sentences:
      - How did the P&C combined ratios trend from 2021 to 2023?
      - >-
        What are some of the digital tools Walmart uses to improve associate
        productivity, engagement, and performance?
      - >-
        What was the result of the Phase 3 OAKTREE trial of obeldesivir
        conducted by Gilead?
  - source_sentence: >-
      The issuance of preferred stock could have the effect of restricting
      dividends on the Company’s common stock, diluting the voting power of its
      common stock, impairing the liquidation rights of its common stock, or
      delaying or preventing a change in control.
    sentences:
      - >-
        What is the impact of issuing preferred stock according to the Company's
        description?
      - For how long did Jeffrey P. Bezos serve as President at Amazon?
      - >-
        Where in an Annual Report on Form 10-K is 'Note 13 — Commitments and
        Contingencies — Litigation and Other Legal Matters' included?
pipeline_tag: sentence-similarity
model-index:
  - name: BGE small Financial Matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 384
          type: dim_384
        metrics:
          - type: cosine_accuracy@1
            value: 0.6642857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8242857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8614285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9085714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6642857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2747619047619047
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17228571428571426
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09085714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6642857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8242857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8614285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9085714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7905933695158355
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7523809523809522
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7562726267140966
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6657142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8242857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8628571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9114285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6657142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2747619047619047
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17257142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09114285714285712
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6657142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8242857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8628571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9114285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7919632560554437
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7534053287981859
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.756861587821826
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6528571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8071428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8485714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6528571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26904761904761904
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16971428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6528571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8071428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8485714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.778048727585675
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7388730158730156
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7424840237912022
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6357142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7757142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8128571428571428
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8585714285714285
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6357142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.25857142857142856
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16257142857142853
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08585714285714285
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6357142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7757142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8128571428571428
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8585714285714285
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7490553533476035
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7138038548752832
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7189504452927022
            name: Cosine Map@100

BGE small Financial Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5. It maps sentences & paragraphs to a 384-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-small-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 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': 384, '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("haophancs/bge-small-financial-matryoshka")
# Run inference
sentences = [
    'The issuance of preferred stock could have the effect of restricting dividends on the Company’s common stock, diluting the voting power of its common stock, impairing the liquidation rights of its common stock, or delaying or preventing a change in control.',
    "What is the impact of issuing preferred stock according to the Company's description?",
    'For how long did Jeffrey P. Bezos serve as President at Amazon?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Dataset: dim_384
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6643
cosine_accuracy@3	0.8243
cosine_accuracy@5	0.8614
cosine_accuracy@10	0.9086
cosine_precision@1	0.6643
cosine_precision@3	0.2748
cosine_precision@5	0.1723
cosine_precision@10	0.0909
cosine_recall@1	0.6643
cosine_recall@3	0.8243
cosine_recall@5	0.8614
cosine_recall@10	0.9086
cosine_ndcg@10	0.7906
cosine_mrr@10	0.7524
cosine_map@100	0.7563

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6657
cosine_accuracy@3	0.8243
cosine_accuracy@5	0.8629
cosine_accuracy@10	0.9114
cosine_precision@1	0.6657
cosine_precision@3	0.2748
cosine_precision@5	0.1726
cosine_precision@10	0.0911
cosine_recall@1	0.6657
cosine_recall@3	0.8243
cosine_recall@5	0.8629
cosine_recall@10	0.9114
cosine_ndcg@10	0.792
cosine_mrr@10	0.7534
cosine_map@100	0.7569

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6529
cosine_accuracy@3	0.8071
cosine_accuracy@5	0.8486
cosine_accuracy@10	0.9
cosine_precision@1	0.6529
cosine_precision@3	0.269
cosine_precision@5	0.1697
cosine_precision@10	0.09
cosine_recall@1	0.6529
cosine_recall@3	0.8071
cosine_recall@5	0.8486
cosine_recall@10	0.9
cosine_ndcg@10	0.778
cosine_mrr@10	0.7389
cosine_map@100	0.7425

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6357
cosine_accuracy@3	0.7757
cosine_accuracy@5	0.8129
cosine_accuracy@10	0.8586
cosine_precision@1	0.6357
cosine_precision@3	0.2586
cosine_precision@5	0.1626
cosine_precision@10	0.0859
cosine_recall@1	0.6357
cosine_recall@3	0.7757
cosine_recall@5	0.8129
cosine_recall@10	0.8586
cosine_ndcg@10	0.7491
cosine_mrr@10	0.7138
cosine_map@100	0.719

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: 9 tokens
mean: 45.74 tokens
max: 512 tokens

min: 8 tokens
mean: 20.77 tokens
max: 43 tokens

	positive	anchor
type	string	string
details	min: 9 tokens mean: 45.74 tokens max: 512 tokens	min: 8 tokens mean: 20.77 tokens max: 43 tokens

Samples:

positive	anchor
`The company believes that trademarks have significant value for marketing products, e-commerce, stores, and business, with the possibility of indefinite renewal as long as the trademarks are in use.`	`What are the benefits of registering trademarks for the company's business?`
`The consolidated financial statements and accompanying notes listed in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included immediately following Part IV hereof and incorporated by reference herein.`	`How are the consolidated financial statements and accompanying notes incorporated into the Annual Report on Form 10-K?`
`During the year ended December 31, 2023, the Company repurchased and subsequently retired 2,029,894 shares of common stock from the open market at an average cost of $103.45 per share for a total of $210.0 million.`	`How many shares of common stock did the Company repurchase and subsequently retire during the year ended December 31, 2023?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        384,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        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_384_cosine_map@100	dim_64_cosine_map@100
0.8122	10	1.7741	-	-	-	-
0.9746	12	-	0.7042	0.7262	0.7327	0.6639
1.6244	20	0.7817	-	-	-	-
1.9492	24	-	0.7322	0.7477	0.7498	0.7136
2.4365	30	0.5816	-	-	-	-
2.9239	36	-	0.7387	0.7563	0.7549	0.7165
3.2487	40	0.5121	-	-	-	-
3.8985	48	-	0.7425	0.7569	0.7563	0.719

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.2
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.2.0+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}
}