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BGE base Financial Matryoshka

Name: bge-base-financial-matryoshka
Availability: InStock

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Hritikmore/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'The effective duration of our total AFS and HTM investments securities as of December 31, 2023 is approximately 3.9 years.',
    'What are the effective durations of the total Available-for-Sale (AFS) and Held-to-Maturity (HTM) investment securities as of December 31, 2023?',
    'What was the net unit growth percentage for Hilton in the year ended December 31, 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	0.7286
cosine_accuracy@3	0.8486
cosine_accuracy@5	0.8886
cosine_accuracy@10	0.9214
cosine_precision@1	0.7286
cosine_precision@3	0.2829
cosine_precision@5	0.1777
cosine_precision@10	0.0921
cosine_recall@1	0.7286
cosine_recall@3	0.8486
cosine_recall@5	0.8886
cosine_recall@10	0.9214
cosine_ndcg@10	0.8274
cosine_mrr@10	0.797
cosine_map@100	0.7999

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.72
cosine_accuracy@3	0.8443
cosine_accuracy@5	0.8786
cosine_accuracy@10	0.92
cosine_precision@1	0.72
cosine_precision@3	0.2814
cosine_precision@5	0.1757
cosine_precision@10	0.092
cosine_recall@1	0.72
cosine_recall@3	0.8443
cosine_recall@5	0.8786
cosine_recall@10	0.92
cosine_ndcg@10	0.8214
cosine_mrr@10	0.7897
cosine_map@100	0.7927

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7214
cosine_accuracy@3	0.8386
cosine_accuracy@5	0.8743
cosine_accuracy@10	0.9129
cosine_precision@1	0.7214
cosine_precision@3	0.2795
cosine_precision@5	0.1749
cosine_precision@10	0.0913
cosine_recall@1	0.7214
cosine_recall@3	0.8386
cosine_recall@5	0.8743
cosine_recall@10	0.9129
cosine_ndcg@10	0.8191
cosine_mrr@10	0.7889
cosine_map@100	0.7921

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6971
cosine_accuracy@3	0.8329
cosine_accuracy@5	0.8671
cosine_accuracy@10	0.9057
cosine_precision@1	0.6971
cosine_precision@3	0.2776
cosine_precision@5	0.1734
cosine_precision@10	0.0906
cosine_recall@1	0.6971
cosine_recall@3	0.8329
cosine_recall@5	0.8671
cosine_recall@10	0.9057
cosine_ndcg@10	0.8054
cosine_mrr@10	0.7729
cosine_map@100	0.7762

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6614
cosine_accuracy@3	0.7986
cosine_accuracy@5	0.8443
cosine_accuracy@10	0.8814
cosine_precision@1	0.6614
cosine_precision@3	0.2662
cosine_precision@5	0.1689
cosine_precision@10	0.0881
cosine_recall@1	0.6614
cosine_recall@3	0.7986
cosine_recall@5	0.8443
cosine_recall@10	0.8814
cosine_ndcg@10	0.7729
cosine_mrr@10	0.7378
cosine_map@100	0.7418

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: 45.87 tokens
max: 272 tokens
min: 2 tokens
mean: 20.43 tokens
max: 41 tokens

Samples:

positive	anchor
`Significant judgment is required in evaluating our tax positions and during the ordinary course of business, there are many transactions and calculations for which the ultimate tax settlement is uncertain. As a result, we recognize the effect of this uncertainty on our tax attributes or taxes payable based on our estimates of the eventual outcome.`	`Why might the company's tax settlements vary?`
`OPSUMIT is used for the treatment of pediatric pulmonary arterial hypertension.`	`What medical condition does OPSUMIT treat?`
`Tangible equity ratios and tangible book value per share of common stock are non-GAAP financial measures. For more information on these ratios and corresponding reconciliations to GAAP financial measures, see Supplemental Financial Data and Non-GAAP Reconciliations.`	`What is the tangible equity ratio considered according to standard financial measures?`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 2
lr_scheduler_type: cosine
warmup_ratio: 0.1
tf32: False
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
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: 2
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: False
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.2030	10	0.7168	-	-	-	-	-
0.4061	20	0.3345	-	-	-	-	-
0.6091	30	0.2234	-	-	-	-	-
0.8122	40	0.2126	-	-	-	-	-
0.9949	49	-	0.7796	0.7844	0.7905	0.7293	0.7973
1.0152	50	0.2301	-	-	-	-	-
1.2183	60	0.1595	-	-	-	-	-
1.4213	70	0.1082	-	-	-	-	-
1.6244	80	0.0911	-	-	-	-	-
1.8274	90	0.1068	-	-	-	-	-
1.9898	98	-	0.7762	0.7921	0.7927	0.7418	0.7999

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
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

bibtex

@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

bibtex

@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

bibtex

@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}
}

	positive	anchor
type	string	string
details	min: 2 tokens mean: 45.87 tokens max: 272 tokens	min: 2 tokens mean: 20.43 tokens max: 41 tokens