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: >-
      The platform offers a number of free services to its members: access to
      their credit scores and reports, credit and identity monitoring, credit
      report dispute, tools to help understand net worth and make financial
      progress, and personalized recommendations of credit card, loan, and
      insurance products. Credit Karma Money offers members online savings and
      checking accounts through an FDIC member bank partner. Credit Karma Money
      also provides tools to help members improve their credit scores.
    sentences:
      - What is the mechanism of action for Veklury?
      - What services does Credit Karma offer to its members?
      - >-
        What was the annual amortization expense forecast for
        acquisition-related intangible assets in 2025, according to a specified
        financial projection?
  - source_sentence: >-
      Vaccine related exit costs of $0.8 billion were reported in the 2023
      annual report.
    sentences:
      - What factors primarily drove the decrease in Veklury's sales in 2023?
      - >-
        What were the vaccine related exit costs reported by Johnson & Johnson
        in their 2023 annual report?
      - What was the percentage increase in interest income from 2022 to 2023?
  - source_sentence: >-
      Broadband revenues increased in 2023 by 8.1% driven by an increase in
      fiber customers and higher average revenue per user, partially offset by
      declines in copper-based broadband services.
    sentences:
      - >-
        What was the percent change in broadband revenues for AT&T in 2023
        compared to 2022?
      - >-
        What factors primarily drove the increase in net cash provided by
        operating activities for fiscal 2023?
      - >-
        How much interest does Chevron hold in the production sharing contract
        for deepwater Block 14?
  - source_sentence: >-
      SEC regulations require the company to disclose certain information about
      proceedings arising under federal, state or local environmental
      regulations if they reasonably believe that such proceedings may result in
      monetary sanctions exceeding $1 million.
    sentences:
      - >-
        What does the term 'Acquired brands' refer to and how does it affect the
        reported volumes?
      - >-
        How many new medicine candidates are currently in clinical development
        or under regulatory review?
      - >-
        Under what conditions are the Company required to disclose certain
        proceedings according to SEC regulations?
  - source_sentence: >-
      2023 highlights include net revenues of $5,003.3 million which decreased
      15% from $5,856.7 million in 2022.
    sentences:
      - How did Hasbro's net revenues in 2023 compare to the previous year?
      - How much cash did continuing operating activities provide in 2023?
      - >-
        Which pages of IBM’s 2023 Annual Report provide information on Financial
        Statements and Supplementary Data?
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: 0.68
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.81
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8514285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8942857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.68
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17028571428571426
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08942857142857143
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.68
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.81
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8514285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8942857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7882073443841624
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7541315192743764
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7584597649275473
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.68
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8028571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8457142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8971428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.68
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2676190476190476
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16914285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0897142857142857
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.68
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8028571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8457142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8971428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7870684908640463
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7519659863945578
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7559459500178702
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6714285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7985714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8457142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8842857142857142
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6714285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2661904761904762
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16914285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08842857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6714285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7985714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8457142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8842857142857142
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7799432706618373
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7462352607709751
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7505911400077954
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.66
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7914285714285715
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8285714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8814285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.66
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2638095238095238
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1657142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08814285714285712
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.66
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7914285714285715
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8285714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8814285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7707461487192945
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7354421768707481
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7395774801009367
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6271428571428571
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7542857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8014285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.86
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6271428571428571
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.25142857142857145
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16028571428571428
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08599999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6271428571428571
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7542857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8014285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.86
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7403886246637359
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7025532879818592
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7068862427781479
            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("amichelini/bge-base-financial-matryoshka")
# Run inference
sentences = [
    '2023 highlights include net revenues of $5,003.3 million which decreased 15% from $5,856.7 million in 2022.',
    "How did Hasbro's net revenues in 2023 compare to the previous year?",
    'How much cash did continuing operating activities provide in 2023?',
]
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	0.68
cosine_accuracy@3	0.81
cosine_accuracy@5	0.8514
cosine_accuracy@10	0.8943
cosine_precision@1	0.68
cosine_precision@3	0.27
cosine_precision@5	0.1703
cosine_precision@10	0.0894
cosine_recall@1	0.68
cosine_recall@3	0.81
cosine_recall@5	0.8514
cosine_recall@10	0.8943
cosine_ndcg@10	0.7882
cosine_mrr@10	0.7541
cosine_map@100	0.7585

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.68
cosine_accuracy@3	0.8029
cosine_accuracy@5	0.8457
cosine_accuracy@10	0.8971
cosine_precision@1	0.68
cosine_precision@3	0.2676
cosine_precision@5	0.1691
cosine_precision@10	0.0897
cosine_recall@1	0.68
cosine_recall@3	0.8029
cosine_recall@5	0.8457
cosine_recall@10	0.8971
cosine_ndcg@10	0.7871
cosine_mrr@10	0.752
cosine_map@100	0.7559

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6714
cosine_accuracy@3	0.7986
cosine_accuracy@5	0.8457
cosine_accuracy@10	0.8843
cosine_precision@1	0.6714
cosine_precision@3	0.2662
cosine_precision@5	0.1691
cosine_precision@10	0.0884
cosine_recall@1	0.6714
cosine_recall@3	0.7986
cosine_recall@5	0.8457
cosine_recall@10	0.8843
cosine_ndcg@10	0.7799
cosine_mrr@10	0.7462
cosine_map@100	0.7506

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.66
cosine_accuracy@3	0.7914
cosine_accuracy@5	0.8286
cosine_accuracy@10	0.8814
cosine_precision@1	0.66
cosine_precision@3	0.2638
cosine_precision@5	0.1657
cosine_precision@10	0.0881
cosine_recall@1	0.66
cosine_recall@3	0.7914
cosine_recall@5	0.8286
cosine_recall@10	0.8814
cosine_ndcg@10	0.7707
cosine_mrr@10	0.7354
cosine_map@100	0.7396

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6271
cosine_accuracy@3	0.7543
cosine_accuracy@5	0.8014
cosine_accuracy@10	0.86
cosine_precision@1	0.6271
cosine_precision@3	0.2514
cosine_precision@5	0.1603
cosine_precision@10	0.086
cosine_recall@1	0.6271
cosine_recall@3	0.7543
cosine_recall@5	0.8014
cosine_recall@10	0.86
cosine_ndcg@10	0.7404
cosine_mrr@10	0.7026
cosine_map@100	0.7069

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: 4 tokens
mean: 46.33 tokens
max: 326 tokens

min: 7 tokens
mean: 20.38 tokens
max: 43 tokens

	positive	anchor
type	string	string
details	min: 4 tokens mean: 46.33 tokens max: 326 tokens	min: 7 tokens mean: 20.38 tokens max: 43 tokens

Samples:

positive	anchor
`The data includes transaction and integration costs listed as follows for each year: $0, $0, $59, $0, $0, $0, $269, $91, $39, $269, $91, $98.`	`What were the values of transaction and integration costs for each of the years provided in the data?`
`In 2023, Delta Air Lines announced an increase in remuneration from their partnership with American Express to $6.8 billion, with expected growth of 10% in 2024.`	`What was the remuneration from Delta Air Lines' partnership with American Express in 2023, and what is the growth expectation for 2024?`
`On December 1, 2023, we advanced $10.0 billion under the ASR program and received approximately 215 million shares of common stock with a value of $6.8 billion, which were immediately retired.`	`What significant financial activity occurred on December 1, 2023, under the ASR program?`

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
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: 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
eval_on_start: False
eval_use_gather_object: 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	0	-	0.6648	0.6922	0.6982	0.6028	0.7029
0.8122	10	1.5362	-	-	-	-	-
0.9746	12	-	0.7259	0.7402	0.7481	0.6913	0.7510
1.6244	20	0.6012	-	-	-	-	-
1.9492	24	-	0.7341	0.7503	0.7554	0.7051	0.7576
2.4365	30	0.4225	-	-	-	-	-
2.9239	36	-	0.7383	0.7522	0.7569	0.7063	0.7570
3.2487	40	0.358	-	-	-	-	-
3.8985	48	-	0.7396	0.7506	0.7559	0.7069	0.7585

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.1.1
Transformers: 4.44.2
PyTorch: 2.4.1+cu121
Accelerate: 0.34.2
Datasets: 3.0.0
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}
}