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 8 in IBM's 2023 Annual Report to Stockholders details the Financial
      Statements and Supplementary Data, which are included on pages 44 through
      121.
    sentences:
      - What was the amount gained from the disposal of assets in 2022?
      - >-
        What section of IBM's Annual Report for 2023 contains the Financial
        Statements and Supplementary Data?
      - >-
        What were the cash outflows for capital expenditures in 2023 and 2022
        respectively?
  - source_sentence: >-
      For the fiscal year ended March 31, 2023, Electronic Arts reported a gross
      margin of 75.9 percent, an increase of 2.5 percentage points from the
      previous year.
    sentences:
      - >-
        How did investment banking revenues at Goldman Sachs change in 2023
        compared to 2022, and what factors contributed to this change?
      - >-
        What was the gross margin percentage for Electronic Arts in the fiscal
        year ending March 31, 2023?
      - >-
        What were the risk-free interest rates for the fiscal years 2021, 2022,
        and 2023?
  - source_sentence: >-
      Cash, cash equivalents, and restricted cash at the beginning of the period
      totaled $7,013 for a company.
    sentences:
      - >-
        What was the amount of cash, cash equivalents, and restricted cash at
        the beginning of the period for the company?
      - >-
        What is the impact of the new $1.25 price point on Dollar Tree’s sales
        units and profitability?
      - >-
        What was the total amount attributed to Goodwill in the acquisition of
        Nuance Communications, Inc. as reported by the company?
  - source_sentence: >-
      generate our mall revenue primarily from leases with tenants through base
      minimum rents, overage rents and reimbursements for common area
      maintenance (CAM) and other expenditures.
    sentences:
      - How does Visa facilitate financial inclusion with their prepaid cards?
      - >-
        What are the main objectives of the economic sanctions imposed by the
        United States and other international bodies?
      - >-
        What revenue sources does Shoppes at Venetian primarily rely on from its
        tenants?
  - source_sentence: >-
      For the fiscal year ended August 26, 2023, we reported net sales of $17.5
      billion compared with $16.3 billion for the year ended August 27, 2022, a
      7.4% increase from fiscal 2022. This growth was driven primarily by a
      domestic same store sales increase of 3.4% and net sales of $327.8 million
      from new domestic and international stores.
    sentences:
      - >-
        What drove the 7.4% increase in AutoZone's net sales for fiscal 2023
        compared to fiscal 2022?
      - >-
        What percentage of HP's external U.S. hires in fiscal year 2023 were
        racially or ethnically diverse?
      - >-
        How much did GameStop Corp's valuation allowances increase during fiscal
        2022?
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: 0.6985714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8271428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8628571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8985714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6985714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2757142857142857
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17257142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08985714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6985714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8271428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8628571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8985714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8023663256793517
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7712675736961451
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7758522351159084
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.69
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8271428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.86
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9028571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.69
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2757142857142857
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17199999999999996
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09028571428571427
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.69
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8271428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.86
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9028571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7998655910794988
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7665912698412698
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7706925401671437
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6957142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8228571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.86
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8914285714285715
            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: 0.17199999999999996
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08914285714285713
            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: 0.86
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8914285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7974564108711016
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7669535147392289
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7718155211819018
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6871428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8128571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8457142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8857142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6871428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27095238095238094
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16914285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08857142857142856
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6871428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8128571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8457142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8857142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.787697533881839
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.756192743764172
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7610331995977764
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6328571428571429
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7771428571428571
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8171428571428572
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8571428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6328571428571429
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.259047619047619
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16342857142857142
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08571428571428569
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6328571428571429
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7771428571428571
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8171428571428572
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8571428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7482728321357093
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7131224489795914
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7189753431460272
            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("NickyNicky/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'For the fiscal year ended August 26, 2023, we reported net sales of $17.5 billion compared with $16.3 billion for the year ended August 27, 2022, a 7.4% increase from fiscal 2022. This growth was driven primarily by a domestic same store sales increase of 3.4% and net sales of $327.8 million from new domestic and international stores.',
    "What drove the 7.4% increase in AutoZone's net sales for fiscal 2023 compared to fiscal 2022?",
    "What percentage of HP's external U.S. hires in fiscal year 2023 were racially or ethnically diverse?",
]
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	0.6986
cosine_accuracy@3	0.8271
cosine_accuracy@5	0.8629
cosine_accuracy@10	0.8986
cosine_precision@1	0.6986
cosine_precision@3	0.2757
cosine_precision@5	0.1726
cosine_precision@10	0.0899
cosine_recall@1	0.6986
cosine_recall@3	0.8271
cosine_recall@5	0.8629
cosine_recall@10	0.8986
cosine_ndcg@10	0.8024
cosine_mrr@10	0.7713
cosine_map@100	0.7759

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.69
cosine_accuracy@3	0.8271
cosine_accuracy@5	0.86
cosine_accuracy@10	0.9029
cosine_precision@1	0.69
cosine_precision@3	0.2757
cosine_precision@5	0.172
cosine_precision@10	0.0903
cosine_recall@1	0.69
cosine_recall@3	0.8271
cosine_recall@5	0.86
cosine_recall@10	0.9029
cosine_ndcg@10	0.7999
cosine_mrr@10	0.7666
cosine_map@100	0.7707

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6957
cosine_accuracy@3	0.8229
cosine_accuracy@5	0.86
cosine_accuracy@10	0.8914
cosine_precision@1	0.6957
cosine_precision@3	0.2743
cosine_precision@5	0.172
cosine_precision@10	0.0891
cosine_recall@1	0.6957
cosine_recall@3	0.8229
cosine_recall@5	0.86
cosine_recall@10	0.8914
cosine_ndcg@10	0.7975
cosine_mrr@10	0.767
cosine_map@100	0.7718

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6871
cosine_accuracy@3	0.8129
cosine_accuracy@5	0.8457
cosine_accuracy@10	0.8857
cosine_precision@1	0.6871
cosine_precision@3	0.271
cosine_precision@5	0.1691
cosine_precision@10	0.0886
cosine_recall@1	0.6871
cosine_recall@3	0.8129
cosine_recall@5	0.8457
cosine_recall@10	0.8857
cosine_ndcg@10	0.7877
cosine_mrr@10	0.7562
cosine_map@100	0.761

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6329
cosine_accuracy@3	0.7771
cosine_accuracy@5	0.8171
cosine_accuracy@10	0.8571
cosine_precision@1	0.6329
cosine_precision@3	0.259
cosine_precision@5	0.1634
cosine_precision@10	0.0857
cosine_recall@1	0.6329
cosine_recall@3	0.7771
cosine_recall@5	0.8171
cosine_recall@10	0.8571
cosine_ndcg@10	0.7483
cosine_mrr@10	0.7131
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: 2 tokens
mean: 46.19 tokens
max: 371 tokens

min: 2 tokens
mean: 20.39 tokens
max: 46 tokens

	positive	anchor
type	string	string
details	min: 2 tokens mean: 46.19 tokens max: 371 tokens	min: 2 tokens mean: 20.39 tokens max: 46 tokens

Samples:

positive	anchor
`Cash used in financing activities in fiscal 2022 was primarily attributable to settlement of stock-based awards.`	`Why was there a net outflow of cash in financing activities in fiscal 2022?`
`Certain vendors have been impacted by volatility in the supply chain financing market.`	`How have certain vendors been impacted in the supply chain financing market?`
`In the consolidated financial statements for Visa, the net cash provided by operating activities amounted to 20,755 units in the most recent period, 18,849 units in the previous period, and 15,227 units in the period before that.`	`How much net cash did Visa's operating activities generate in the most recent period according to the financial statements?`

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
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: False
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	1.5643	-	-	-	-	-
0.9746	12	-	0.7349	0.7494	0.7524	0.6987	0.7569
1.6244	20	0.6756	-	-	-	-	-
1.9492	24	-	0.7555	0.7659	0.7683	0.7190	0.7700
2.4365	30	0.4561	-	-	-	-	-
2.9239	36	-	0.7592	0.7698	0.7698	0.7184	0.7741
3.2487	40	0.3645	-	-	-	-	-
3.8985	48	-	0.7610	0.7718	0.7707	0.7190	0.7759

Framework Versions

Python: 3.10.12
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}
}