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: >-
      Capital expenditures, which primarily reflected investments in technical
      infrastructure, were $32.3 billion for the year ended December 31, 2023.
    sentences:
      - >-
        Where can you find the consolidated financial statements in the Annual
        Report on Form 10-K?
      - What were the total capital expenditures for Alphabet Inc. in 2023?
      - >-
        How did Chevron's development strategy in the Permian Basin contribute
        to its productivity?
  - source_sentence: >-
      You can identify forward-looking statements by the use of forward-looking
      terminology including “believes,” “expects,” “may,” “will,” “should,”
      “seeks,” “intends,” “plans,” “pro forma,” “estimates,” “anticipates,” or
      the negative of these words and phrases, other variations of these words
      and phrases or comparable terminology.
    sentences:
      - What does the forward-looking terminology in financial documents imply?
      - >-
        What seasons have higher domestic advertising revenue and what
        influences these patterns?
      - >-
        What is the role of Bank of America Corporation's management in relation
        to internal control over financial reporting?
  - source_sentence: >-
      For the year ended December 31, 2023, we recorded $3.6 million of foreign
      currency transaction losses.
    sentences:
      - >-
        What was the total foreign currency transaction loss recorded for the
        year ended December 31, 2023?
      - >-
        What credit ratings were assigned to the company by Standard & Poor’s
        and Moody’s at the end of 2022?
      - >-
        What are The Home Depot's strategies for increasing diversity, equity,
        and inclusion?
  - source_sentence: >-
      Gross margin contraction of 310 basis points primarily due to higher
      product costs, reflecting higher input costs and inbound freight and
      logistics costs and product mix, lower margins in NIKE Direct due to
      higher promotional activity and a lower mix of full-price sales.
    sentences:
      - What fiduciary duties might a company have under ERISA?
      - >-
        What were the significant contributors to the gross margin contraction
        and by how many basis points did it contract?
      - >-
        What typical reimbursement methods are used in the company's contracts
        with hospitals for inpatient and outpatient services?
  - source_sentence: >-
      As of December 31, 2023, we employed about 113,200 full-time persons of
      whom approximately 62,400 were located outside the United States. In the
      United States, we employed approximately 50,800 full-time persons.
    sentences:
      - >-
        What types of categories did eBay focus on in 2023, and how did they
        contribute to the company's gross merchandise volume?
      - >-
        What challenges do solar power system owners face with traditional
        string inverters?
      - >-
        How many full-time employees were employed by the company as of December
        31, 2023, and how are they distributed geographically?
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 384
          type: dim_384
        metrics:
          - type: cosine_accuracy@1
            value: 0.7157142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8485714285714285
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8742857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9171428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7157142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28285714285714286
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17485714285714282
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09171428571428569
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7157142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8485714285714285
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8742857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9171428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8198819637056249
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7885175736961447
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7918328646013278
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7157142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8485714285714285
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8785714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9142857142857143
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7157142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28285714285714286
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17571428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09142857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7157142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8485714285714285
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8785714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9142857142857143
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8187635355625659
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7878270975056689
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7911673353002208
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.7057142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8371428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8642857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9085714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7057142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27904761904761904
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17285714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09085714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7057142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8371428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8642857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9085714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8090255333396114
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.777143424036281
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7807082191352167
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6728571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8128571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.85
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8814285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6728571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27095238095238094
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16999999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08814285714285712
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6728571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8128571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.85
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8814285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.782934506961568
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7507721088435368
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7551335288460688
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 32
          type: dim_32
        metrics:
          - type: cosine_accuracy@1
            value: 0.5957142857142858
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7414285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7828571428571428
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8314285714285714
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5957142857142858
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2471428571428571
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.15657142857142856
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08314285714285713
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.5957142857142858
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7414285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.7828571428571428
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8314285714285714
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7158751864189645
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6787687074829931
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6839925227099907
            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("moresearch/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'As of December 31, 2023, we employed about 113,200 full-time persons of whom approximately 62,400 were located outside the United States. In the United States, we employed approximately 50,800 full-time persons.',
    'How many full-time employees were employed by the company as of December 31, 2023, and how are they distributed geographically?',
    'What challenges do solar power system owners face with traditional string inverters?',
]
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_384, dim_256, dim_128, dim_64 and dim_32
Evaluated with InformationRetrievalEvaluator

Metric	dim_384	dim_256	dim_128	dim_64	dim_32
cosine_accuracy@1	0.7157	0.7157	0.7057	0.6729	0.5957
cosine_accuracy@3	0.8486	0.8486	0.8371	0.8129	0.7414
cosine_accuracy@5	0.8743	0.8786	0.8643	0.85	0.7829
cosine_accuracy@10	0.9171	0.9143	0.9086	0.8814	0.8314
cosine_precision@1	0.7157	0.7157	0.7057	0.6729	0.5957
cosine_precision@3	0.2829	0.2829	0.279	0.271	0.2471
cosine_precision@5	0.1749	0.1757	0.1729	0.17	0.1566
cosine_precision@10	0.0917	0.0914	0.0909	0.0881	0.0831
cosine_recall@1	0.7157	0.7157	0.7057	0.6729	0.5957
cosine_recall@3	0.8486	0.8486	0.8371	0.8129	0.7414
cosine_recall@5	0.8743	0.8786	0.8643	0.85	0.7829
cosine_recall@10	0.9171	0.9143	0.9086	0.8814	0.8314
cosine_ndcg@10	0.8199	0.8188	0.809	0.7829	0.7159
cosine_mrr@10	0.7885	0.7878	0.7771	0.7508	0.6788
cosine_map@100	0.7918	0.7912	0.7807	0.7551	0.684

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: 7 tokens
mean: 45.87 tokens
max: 288 tokens

min: 2 tokens
mean: 20.32 tokens
max: 41 tokens

	positive	anchor
type	string	string
details	min: 7 tokens mean: 45.87 tokens max: 288 tokens	min: 2 tokens mean: 20.32 tokens max: 41 tokens

Samples:

positive	anchor
`The company maintains a revolving credit facility that, unless extended, terminates on July 6, 2026.`	`What is the expiration date of the company's revolving credit facility, unless extended?`
The management of Bank of America Corporation is responsible for establishing and maintaining adequate internal control over financial reporting. The Corporation’s internal control over financial reporting is designed to provide reasonable assurance about the reliability of financial reporting and the preparation of financial statements in accordance with accounting principles generally accepted in the United States of America. Management's responsibilities include maintaining records that, in reasonable detail, accurately and fairly reflect the transactions and dispositions of the assets of the Corporation; ensuring that transactions are recorded as necessary for the preparation of financial statements; and preventing or detecting unauthorized acquisition, use, or disposition of the Corporation’s assets that could have a material effect on the financial statements.	`What is the role of Bank of America Corporation's management in relation to internal control over financial reporting?`
`In 2020, Gilead implemented multiple programs to train managers on inclusion and diversity topics and created strategies and initiatives focused on attracting, developing and retaining diverse talent and driving an inclusive culture in our workplace.`	`What initiatives has Gilead undertaken to promote workplace diversity?`

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: False
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: False
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
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_384_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10	dim_32_cosine_ndcg@10
0.8122	10	1.5733	-	-	-	-	-
0.9746	12	-	0.8075	0.8045	0.7876	0.7643	0.6844
1.6244	20	0.6549	-	-	-	-	-
1.9492	24	-	0.8188	0.8169	0.8035	0.7789	0.7107
2.4365	30	0.4373	-	-	-	-	-
2.9239	36	-	0.8210	0.8183	0.8079	0.7835	0.7161
3.2487	40	0.3951	-	-	-	-	-
3.8985	48	-	0.8199	0.8188	0.809	0.7829	0.7159

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.46.3
PyTorch: 2.5.1+cu124
Accelerate: 1.1.1
Datasets: 3.1.0
Tokenizers: 0.20.3

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}
}