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: >-
      ITEM 7. MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND
      RESULTS OF OPERATIONS The following discussion and analysis should be read
      in conjunction with the consolidated financial statements and the related
      notes included elsewhere in this Annual Report on Form 10-K. For further
      discussion of our products and services, technology and competitive
      strengths, refer to Item 1- Business.
    sentences:
      - >-
        What was the total net automotive cash provided by investing activities
        in 2023?
      - >-
        What is the purpose of the Management's Discussion and Analysis of
        Financial Condition and Results of Operations section in the Annual
        Report on Form 10-K?
      - >-
        What are the components included in the management discussion and
        analysis of financial condition and results of operations?
  - source_sentence: >-
      Kroger is committed to maintaining a net total debt to adjusted EBITDA
      ratio target range of 2.30 to 2.50.
    sentences:
      - >-
        What was the remaining available amount of the share repurchase
        authorization as of January 29, 2023?
      - >-
        What range does Kroger aim for its net total debt to adjusted EBITDA
        ratio?
      - >-
        What was the starting wage for all entry-level positions in the U.S. as
        of September 2023?
  - source_sentence: Google Cloud operating income of $1.7 billion for 2023.
    sentences:
      - What was the operating income for Google Cloud in 2023?
      - What types of products are offered in Garmin's Fitness segment?
      - What was the net sales of the company in fiscal 2022?
  - source_sentence: >-
      The effective income tax rate for Alphabet Inc. at the end of the year
      2023 was 13.9%.
    sentences:
      - >-
        What was the percentage change in Compute & Networking revenue from
        fiscal year 2022 to 2023?
      - >-
        What factors primarily contributed to the increase in non-interest
        revenues across all revenue categories?
      - >-
        What was the effective income tax rate for Alphabet Inc. at the end of
        the year 2023?
  - source_sentence: >-
      State legislation increasingly requires PBMs to conduct audits of network
      pharmacies regarding claims submitted for payment. Non-compliance could
      prevent the recoupment of overpaid amounts, potentially causing financial
      and legal repercussions.
    sentences:
      - >-
        What are the potential consequences for a company if its PBMs fail to
        comply with pharmacy audit regulations?
      - >-
        What pages do the Consolidated Financial Statements and their
        accompanying Notes and reports appear on in the document?
      - >-
        What are the primary services provided by the company under the Xfinity,
        Comcast Business, and Sky brands?
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: 0.6785714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8342857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.88
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9085714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6785714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2780952380952381
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.176
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09085714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6785714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8342857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.88
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9085714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7995179593313807
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7638202947845802
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7674168947978975
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6685714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8271428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8685714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9128571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6685714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2757142857142857
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1737142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09128571428571428
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6685714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8271428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8685714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9128571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7954721927324272
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7574353741496596
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7606771546726785
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6728571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8142857142857143
            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: 0.6728571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2714285714285714
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17285714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09042857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6728571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8142857142857143
            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: 0.7916203877025221
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7552613378684805
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7590698804335085
            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: 0.8114285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.85
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8885714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6528571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2704761904761904
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16999999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08885714285714286
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6528571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8114285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.85
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8885714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7754227314755763
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.738630385487528
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7431237490151862
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6157142857142858
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7614285714285715
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.81
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8642857142857143
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6157142857142858
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2538095238095238
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16199999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08642857142857142
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6157142857142858
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7614285714285715
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.81
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8642857142857143
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7413954849024657
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.701954648526077
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.707051130510896
            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("gauravsirola/bge-base-financial-matryoshka-v1")
# Run inference
sentences = [
    'State legislation increasingly requires PBMs to conduct audits of network pharmacies regarding claims submitted for payment. Non-compliance could prevent the recoupment of overpaid amounts, potentially causing financial and legal repercussions.',
    'What are the potential consequences for a company if its PBMs fail to comply with pharmacy audit regulations?',
    'What pages do the Consolidated Financial Statements and their accompanying Notes and reports appear on in the document?',
]
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	0.6786
cosine_accuracy@3	0.8343
cosine_accuracy@5	0.88
cosine_accuracy@10	0.9086
cosine_precision@1	0.6786
cosine_precision@3	0.2781
cosine_precision@5	0.176
cosine_precision@10	0.0909
cosine_recall@1	0.6786
cosine_recall@3	0.8343
cosine_recall@5	0.88
cosine_recall@10	0.9086
cosine_ndcg@10	0.7995
cosine_mrr@10	0.7638
cosine_map@100	0.7674

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6686
cosine_accuracy@3	0.8271
cosine_accuracy@5	0.8686
cosine_accuracy@10	0.9129
cosine_precision@1	0.6686
cosine_precision@3	0.2757
cosine_precision@5	0.1737
cosine_precision@10	0.0913
cosine_recall@1	0.6686
cosine_recall@3	0.8271
cosine_recall@5	0.8686
cosine_recall@10	0.9129
cosine_ndcg@10	0.7955
cosine_mrr@10	0.7574
cosine_map@100	0.7607

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6729
cosine_accuracy@3	0.8143
cosine_accuracy@5	0.8643
cosine_accuracy@10	0.9043
cosine_precision@1	0.6729
cosine_precision@3	0.2714
cosine_precision@5	0.1729
cosine_precision@10	0.0904
cosine_recall@1	0.6729
cosine_recall@3	0.8143
cosine_recall@5	0.8643
cosine_recall@10	0.9043
cosine_ndcg@10	0.7916
cosine_mrr@10	0.7553
cosine_map@100	0.7591

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6529
cosine_accuracy@3	0.8114
cosine_accuracy@5	0.85
cosine_accuracy@10	0.8886
cosine_precision@1	0.6529
cosine_precision@3	0.2705
cosine_precision@5	0.17
cosine_precision@10	0.0889
cosine_recall@1	0.6529
cosine_recall@3	0.8114
cosine_recall@5	0.85
cosine_recall@10	0.8886
cosine_ndcg@10	0.7754
cosine_mrr@10	0.7386
cosine_map@100	0.7431

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6157
cosine_accuracy@3	0.7614
cosine_accuracy@5	0.81
cosine_accuracy@10	0.8643
cosine_precision@1	0.6157
cosine_precision@3	0.2538
cosine_precision@5	0.162
cosine_precision@10	0.0864
cosine_recall@1	0.6157
cosine_recall@3	0.7614
cosine_recall@5	0.81
cosine_recall@10	0.8643
cosine_ndcg@10	0.7414
cosine_mrr@10	0.702
cosine_map@100	0.7071

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: 7 tokens
mean: 44.73 tokens
max: 301 tokens

min: 8 tokens
mean: 20.57 tokens
max: 41 tokens

	positive	anchor
type	string	string
details	min: 7 tokens mean: 44.73 tokens max: 301 tokens	min: 8 tokens mean: 20.57 tokens max: 41 tokens

Samples:

positive	anchor
`Net loss was $396.6 million and $973.6 million during the years ended December 31, 2023, and December 31, 2022, respectively.`	`What was the net loss for the year ended December 31, 2022?`
`Under the 2023 IDA agreement, the service fee on client cash deposits held at the TD Depository Institutions remains at 15 basis points, as it was in the 2019 IDA agreement.`	`How much is the service fee on client cash deposits held at the TD Depository Institutions under the 2023 IDA agreement?`
`The total shareholders’ deficit is listed as $7,994.8 million in the latest financial statement.`	`What is the total shareholder's deficit according to the latest financial statement?`

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	1.5585	-	-	-	-	-
0.9746	12	-	0.7207	0.7441	0.7510	0.6857	0.7493
1.6244	20	0.6691	-	-	-	-	-
1.9492	24	-	0.7392	0.7564	0.7601	0.7006	0.7661
2.4365	30	0.4702	-	-	-	-	-
2.9239	36	-	0.7430	0.7600	0.7619	0.7065	0.7685
3.2487	40	0.407	-	-	-	-	-
3.8985	48	-	0.7431	0.7591	0.7607	0.7071	0.7674

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.6
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.31.0
Datasets: 2.19.1
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}