metadata

language:
  - en
license: apache-2.0
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:1K<n<10K
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
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
widget:
  - source_sentence: What types of industries does TTI service?
    sentences:
      - What types of businesses does HPE serve?
      - How much did the company's revenues decrease in 2023 compared to 2022?
      - >-
        By what percentage did the quarterly cash dividend increase on January
        26, 2023?
  - source_sentence: What does ITEM 8 in Form 10-K refer to?
    sentences:
      - >-
        ITEM 8 in Form 10-K refers to the Financial Statements and Supplementary
        Data.
      - UnitedHealth Group reported net earnings of $23,144 million in 2023.
      - >-
        What factors contributed to the decrease in automotive leasing revenue
        in 2023?
  - source_sentence: What are consolidated financial statements?
    sentences:
      - >-
        The report on the Consolidated Financial Statements is dated February
        16, 2024.
      - How much did the foreclosed properties decrease in value during 2023?
      - What was Chipotle Mexican Grill's net income in 2023?
  - source_sentence: What were the total product sales in 2023?
    sentences:
      - Total product sales in 2023 amounted to $27,305 million.
      - How does AutoZone manage its foreign operations in terms of currency?
      - >-
        What restrictions does the Bank Holding Company Act impose on JPMorgan
        Chase?
  - source_sentence: What is the global presence of Lubrizol?
    sentences:
      - >-
        How does The Coca-Cola Company distribute its beverage products
        globally?
      - What are the two operating segments of NVIDIA as mentioned in the text?
      - >-
        How much did Delta Air Lines spend on debt and finance lease obligations
        in 2023?
pipeline_tag: sentence-similarity
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: 0.6957142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8342857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8628571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9085714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6957142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2780952380952381
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17257142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09085714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6957142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8342857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8628571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9085714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8045138729797765
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7709591836734694
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7746687336147619
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8271428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8642857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9157142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2757142857142857
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17285714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09157142857142857
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8271428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8642857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9157142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.807258910509631
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7726218820861678
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7757170101327764
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6928571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.82
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8585714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9028571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6928571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2733333333333334
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1717142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09028571428571427
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6928571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.82
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8585714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9028571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7979490809476271
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7643027210884353
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7684617620062486
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6857142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.81
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8542857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.89
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6857142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17085714285714282
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.089
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6857142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.81
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8542857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.89
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7877753635329912
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7549472789115641
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7596045003108374
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6528571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7571428571428571
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8185714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8685714285714285
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6528571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2523809523809524
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1637142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08685714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6528571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7571428571428571
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8185714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8685714285714285
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7557078446701566
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7201400226757368
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7249497855774768
            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("Andresckamilo/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'What is the global presence of Lubrizol?',
    'How does The Coca-Cola Company distribute its beverage products globally?',
    'What are the two operating segments of NVIDIA as mentioned in the text?',
]
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	0.6957
cosine_accuracy@3	0.8343
cosine_accuracy@5	0.8629
cosine_accuracy@10	0.9086
cosine_precision@1	0.6957
cosine_precision@3	0.2781
cosine_precision@5	0.1726
cosine_precision@10	0.0909
cosine_recall@1	0.6957
cosine_recall@3	0.8343
cosine_recall@5	0.8629
cosine_recall@10	0.9086
cosine_ndcg@10	0.8045
cosine_mrr@10	0.771
cosine_map@100	0.7747

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7
cosine_accuracy@3	0.8271
cosine_accuracy@5	0.8643
cosine_accuracy@10	0.9157
cosine_precision@1	0.7
cosine_precision@3	0.2757
cosine_precision@5	0.1729
cosine_precision@10	0.0916
cosine_recall@1	0.7
cosine_recall@3	0.8271
cosine_recall@5	0.8643
cosine_recall@10	0.9157
cosine_ndcg@10	0.8073
cosine_mrr@10	0.7726
cosine_map@100	0.7757

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6929
cosine_accuracy@3	0.82
cosine_accuracy@5	0.8586
cosine_accuracy@10	0.9029
cosine_precision@1	0.6929
cosine_precision@3	0.2733
cosine_precision@5	0.1717
cosine_precision@10	0.0903
cosine_recall@1	0.6929
cosine_recall@3	0.82
cosine_recall@5	0.8586
cosine_recall@10	0.9029
cosine_ndcg@10	0.7979
cosine_mrr@10	0.7643
cosine_map@100	0.7685

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6857
cosine_accuracy@3	0.81
cosine_accuracy@5	0.8543
cosine_accuracy@10	0.89
cosine_precision@1	0.6857
cosine_precision@3	0.27
cosine_precision@5	0.1709
cosine_precision@10	0.089
cosine_recall@1	0.6857
cosine_recall@3	0.81
cosine_recall@5	0.8543
cosine_recall@10	0.89
cosine_ndcg@10	0.7878
cosine_mrr@10	0.7549
cosine_map@100	0.7596

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6529
cosine_accuracy@3	0.7571
cosine_accuracy@5	0.8186
cosine_accuracy@10	0.8686
cosine_precision@1	0.6529
cosine_precision@3	0.2524
cosine_precision@5	0.1637
cosine_precision@10	0.0869
cosine_recall@1	0.6529
cosine_recall@3	0.7571
cosine_recall@5	0.8186
cosine_recall@10	0.8686
cosine_ndcg@10	0.7557
cosine_mrr@10	0.7201
cosine_map@100	0.7249

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: 6 tokens
mean: 45.39 tokens
max: 371 tokens

min: 7 tokens
mean: 20.23 tokens
max: 45 tokens

	positive	anchor
type	string	string
details	min: 6 tokens mean: 45.39 tokens max: 371 tokens	min: 7 tokens mean: 20.23 tokens max: 45 tokens

Samples:

positive	anchor
`Chubb mitigates exposure to climate change risk by ceding catastrophe risk in our insurance portfolio through both reinsurance and capital markets, and our investment portfolio through the diversification of risk, industry, location, type and duration of security.`	`How does Chubb respond to the risks associated with climate change?`
`Item 8 of Part IV in the Annual Report on Form 10-K details the consolidated financial statements and accompanying notes.`	`What documents are detailed in Item 8 of Part IV of the Annual Report on Form 10-K?`
`While the outcome of this matter cannot be determined at this time, it is not currently expected to have a material adverse impact on our business.`	`Is the outcome of the investigation into Tesla's waste segregation practices currently determinable?`

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	1.521	-	-	-	-	-
0.9746	12	-	0.7434	0.7579	0.7641	0.6994	0.7678
1.6244	20	0.6597	-	-	-	-	-
1.9492	24	-	0.7583	0.7628	0.7726	0.7219	0.7735
2.4365	30	0.4472	-	-	-	-	-
2.9239	36	-	0.7578	0.7661	0.7747	0.7251	0.7753
3.2487	40	0.3865	-	-	-	-	-
3.8985	48	-	0.7596	0.7685	0.7757	0.7249	0.7747

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.0.0
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.30.1
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}
}