metadata

base_model: BAAI/bge-base-en-v1.5
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: >-
      Chevron regularly conducts employee surveys throughout the year to assess
      the health of the company’s culture, allowing them to gain insights into
      employee well-being.
    sentences:
      - >-
        What was the net cash provided by operating activities for the year
        ended December 31, 2023?
      - >-
        How often does Chevron conduct employee surveys to assess the health of
        its culture?
      - >-
        What were the total future minimum lease payments for Comcast's
        operating leases as of December 31, 2023?
  - source_sentence: >-
      Gross margin for the fiscal year decreased 250 basis points to 43.5%
      primarily driven by higher product costs, higher markdowns and unfavorable
      changes in foreign currency exchange rates, partially offset by strategic
      pricing actions.
    sentences:
      - >-
        How does the company maintain high standards of product quality and
        safety?
      - >-
        What were the main factors that negatively impacted NIKE's gross margin
        in fiscal 2023?
      - >-
        What was the growth rate of Visa Inc.'s commercial payments volume
        internationally between 2021 and 2022?
  - source_sentence: >-
      Mr. Teter holds a B.S. degree in Mechanical Engineering from the
      University of California at Davis and a J.D. degree from Stanford Law
      School.
    sentences:
      - What degrees does Timothy S. Teter hold and from which institutions?
      - >-
        What regulations are in place in Europe regarding interactions between
        pharmaceutical companies and physicians?
      - >-
        What economic factors particularly affected Garmin's consumer behavior
        in 2023?
  - source_sentence: >-
      Our Office of Diversity, Equity and Inclusion supports our focus on
      associate diversity, supplier diversity, and engagement with our
      communities.
    sentences:
      - >-
        What are the three segments of alcohol ready-to-drink beverages the
        company is focusing on?
      - How much net cash was provided by operating activities in 2023?
      - >-
        What is the focus of The Home Depot's Office of Diversity, Equity and
        Inclusion?
  - source_sentence: >-
      Net cash used in financing activities totaled $2,614 in 2023, compared to
      $4,283 in 2022.
    sentences:
      - >-
        What was the net cash used in financing activities in 2023 and how does
        it compare to 2022?
      - >-
        What are Chipotle's key strategies for business growth as discussed in
        their strategy?
      - >-
        What are the primary regulatory authorities that supervise and regulate
        JPMorgan Chase in the U.S.?
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: 0.6971428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.82
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8685714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9057142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6971428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2733333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1737142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09057142857142855
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6971428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.82
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8685714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9057142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.803607128355984
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.770687641723356
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.77485834386751
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6957142857142857
            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: 0.9042857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6957142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2742857142857143
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17285714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0904285714285714
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6957142857142857
            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: 0.9042857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.802840202489837
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7701360544217687
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7744106258164117
            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: 0.8528571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8985714285714286
            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: 0.17057142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08985714285714284
            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: 0.8528571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8985714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.795190594370522
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7619773242630383
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7664081914180308
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6685714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8128571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8428571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8942857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6685714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27095238095238094
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16857142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08942857142857143
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6685714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8128571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8428571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8942857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7840862792892018
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7486655328798184
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7527149388922518
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6471428571428571
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7828571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8242857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8685714285714285
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6471428571428571
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26095238095238094
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16485714285714284
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08685714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6471428571428571
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7828571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8242857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8685714285714285
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7601900384958588
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.725268707482993
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7302983967510448
            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 tokens
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("revtestuser/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Net cash used in financing activities totaled $2,614 in 2023, compared to $4,283 in 2022.',
    'What was the net cash used in financing activities in 2023 and how does it compare to 2022?',
    "What are Chipotle's key strategies for business growth as discussed in their strategy?",
]
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	0.6971
cosine_accuracy@3	0.82
cosine_accuracy@5	0.8686
cosine_accuracy@10	0.9057
cosine_precision@1	0.6971
cosine_precision@3	0.2733
cosine_precision@5	0.1737
cosine_precision@10	0.0906
cosine_recall@1	0.6971
cosine_recall@3	0.82
cosine_recall@5	0.8686
cosine_recall@10	0.9057
cosine_ndcg@10	0.8036
cosine_mrr@10	0.7707
cosine_map@100	0.7749

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6957
cosine_accuracy@3	0.8229
cosine_accuracy@5	0.8643
cosine_accuracy@10	0.9043
cosine_precision@1	0.6957
cosine_precision@3	0.2743
cosine_precision@5	0.1729
cosine_precision@10	0.0904
cosine_recall@1	0.6957
cosine_recall@3	0.8229
cosine_recall@5	0.8643
cosine_recall@10	0.9043
cosine_ndcg@10	0.8028
cosine_mrr@10	0.7701
cosine_map@100	0.7744

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6871
cosine_accuracy@3	0.8186
cosine_accuracy@5	0.8529
cosine_accuracy@10	0.8986
cosine_precision@1	0.6871
cosine_precision@3	0.2729
cosine_precision@5	0.1706
cosine_precision@10	0.0899
cosine_recall@1	0.6871
cosine_recall@3	0.8186
cosine_recall@5	0.8529
cosine_recall@10	0.8986
cosine_ndcg@10	0.7952
cosine_mrr@10	0.762
cosine_map@100	0.7664

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6686
cosine_accuracy@3	0.8129
cosine_accuracy@5	0.8429
cosine_accuracy@10	0.8943
cosine_precision@1	0.6686
cosine_precision@3	0.271
cosine_precision@5	0.1686
cosine_precision@10	0.0894
cosine_recall@1	0.6686
cosine_recall@3	0.8129
cosine_recall@5	0.8429
cosine_recall@10	0.8943
cosine_ndcg@10	0.7841
cosine_mrr@10	0.7487
cosine_map@100	0.7527

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6471
cosine_accuracy@3	0.7829
cosine_accuracy@5	0.8243
cosine_accuracy@10	0.8686
cosine_precision@1	0.6471
cosine_precision@3	0.261
cosine_precision@5	0.1649
cosine_precision@10	0.0869
cosine_recall@1	0.6471
cosine_recall@3	0.7829
cosine_recall@5	0.8243
cosine_recall@10	0.8686
cosine_ndcg@10	0.7602
cosine_mrr@10	0.7253
cosine_map@100	0.7303

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: 8 tokens
mean: 44.91 tokens
max: 246 tokens

min: 8 tokens
mean: 20.43 tokens
max: 43 tokens

	positive	anchor
type	string	string
details	min: 8 tokens mean: 44.91 tokens max: 246 tokens	min: 8 tokens mean: 20.43 tokens max: 43 tokens

Samples:

positive	anchor
`Certain provisions of the final rule become effective on April 1, 2024, but the majority of the final rule’s operative provisions (including the revisions to the definition of “limited purpose bank”) become effective on January 1, 2026, with additional data collection and reporting requirements becoming effective on January 1, 2027.`	`What are the effective dates for the main provisions and additional data collection and reporting requirements of the final rule impacting AENB's compliance obligations?`
`Our total revenue for 2023 was $134.90 billion, an increase of 16% compared to 2022.`	`What was the total revenue for the year 2023 and the percentage increase from 2022?`
`As of December 31, 2023, our domestic Chief Medical Officer leads a team of 22 nephrologists in our physician leadership team as part of our domestic Office of the Chief Medical Officer.`	`How many physicians are part of the domestic Office of the Chief Medical Officer at DaVita as of December 31, 2023?`

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
fp16: 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
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: True
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
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	1.6288	-	-	-	-	-
0.9746	12	-	0.7384	0.7485	0.7508	0.7013	0.7561
1.6244	20	0.6896	-	-	-	-	-
1.9492	24	-	0.7499	0.7621	0.7676	0.7220	0.7704
2.4365	30	0.4965	-	-	-	-	-
2.9239	36	-	0.7529	0.7669	0.7739	0.7302	0.7754
3.2487	40	0.415	-	-	-	-	-
3.8985	48	-	0.7527	0.7664	0.7744	0.7303	0.7749

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.1.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.34.2
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}
}