BGE small 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
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
model = SentenceTransformer("haophancs/bge-base-financial-matryoshka")
sentences = [
"Under the Insurance Act, Chubb's Bermuda domiciled subsidiaries are prohibited from declaring or paying any dividends of more than 25 percent of total statutory capital and surplus, as shown in its previous financial year statutory balance sheet, unless at least seven days before payment of the dividends, it files with the BMA an affidavit signed by at least two directors of the relevant Bermuda domiciled subsidiary (one of whom must be a director resident in Bermuda) and by the relevant Bermuda domiciled subsidiary’s principal representative, that it will continue to meet its required solvency margins. Furthermore, Bermuda domiciled subsidiaries may only declare and pay a dividend from retained earnings and a dividend or distribution from contributed surplus if it has no reasonable grounds for believing that it is, or would after the payment be, unable to pay its liabilities as they become due, or if the realizable value of its assets would be less than the aggregate of its liabilities. In addition, Chubb's Bermuda domiciled subsidiaries must obtain the BMA's prior approval before reducing total statutory capital, as shown in its previous financial year's financial statements, by 15 percent or more.",
'What are the restrictions and requirements for Bermuda domiciled subsidiaries regarding the distribution of dividends under the Insurance Act?',
'What section deals with financial statements and supplementary data?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Evaluation
Metrics
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.7043 |
cosine_accuracy@3 |
0.8457 |
cosine_accuracy@5 |
0.88 |
cosine_accuracy@10 |
0.9243 |
cosine_precision@1 |
0.7043 |
cosine_precision@3 |
0.2819 |
cosine_precision@5 |
0.176 |
cosine_precision@10 |
0.0924 |
cosine_recall@1 |
0.7043 |
cosine_recall@3 |
0.8457 |
cosine_recall@5 |
0.88 |
cosine_recall@10 |
0.9243 |
cosine_ndcg@10 |
0.8154 |
cosine_mrr@10 |
0.7804 |
cosine_map@100 |
0.7829 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.7057 |
cosine_accuracy@3 |
0.8471 |
cosine_accuracy@5 |
0.8686 |
cosine_accuracy@10 |
0.9243 |
cosine_precision@1 |
0.7057 |
cosine_precision@3 |
0.2824 |
cosine_precision@5 |
0.1737 |
cosine_precision@10 |
0.0924 |
cosine_recall@1 |
0.7057 |
cosine_recall@3 |
0.8471 |
cosine_recall@5 |
0.8686 |
cosine_recall@10 |
0.9243 |
cosine_ndcg@10 |
0.8151 |
cosine_mrr@10 |
0.7802 |
cosine_map@100 |
0.7828 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.7071 |
cosine_accuracy@3 |
0.8386 |
cosine_accuracy@5 |
0.8757 |
cosine_accuracy@10 |
0.9229 |
cosine_precision@1 |
0.7071 |
cosine_precision@3 |
0.2795 |
cosine_precision@5 |
0.1751 |
cosine_precision@10 |
0.0923 |
cosine_recall@1 |
0.7071 |
cosine_recall@3 |
0.8386 |
cosine_recall@5 |
0.8757 |
cosine_recall@10 |
0.9229 |
cosine_ndcg@10 |
0.8152 |
cosine_mrr@10 |
0.7808 |
cosine_map@100 |
0.7833 |
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: 8 tokens
- mean: 45.4 tokens
- max: 252 tokens
|
- min: 9 tokens
- mean: 20.43 tokens
- max: 45 tokens
|
- Samples:
positive |
anchor |
In 2023, $2.2 billion or 5% was primarily related to patient co-pay assistance, cash discounts for prompt payment, distributor fees, and sales return provisions. |
What was the amount of sales return provisions in 2023 as part of gross-to-net deductions? |
Cash and cash equivalents were $21.9 billion at the end of 2023 as compared to $14.1 billion at the end of 2022, showing a $7.8 billion increase. |
How much did cash and cash equivalents increase by the end of 2023 compared to the end of 2022? |
The net increase in cash and cash equivalents for UnitedHealthcare in 2023 compared to 2022 was $72 million. |
What was the net increase in cash and cash equivalents for UnitedHealthcare in 2023 compared to 2022? |
- Loss:
MatryoshkaLoss
with these parameters:{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
384
],
"matryoshka_weights": [
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_384_cosine_map@100 |
dim_512_cosine_map@100 |
dim_768_cosine_map@100 |
0.8122 |
10 |
0.8256 |
- |
- |
- |
0.9746 |
12 |
- |
0.7719 |
0.7679 |
0.7652 |
1.6244 |
20 |
0.2984 |
- |
- |
- |
1.9492 |
24 |
- |
0.7784 |
0.7810 |
0.7791 |
2.4365 |
30 |
0.201 |
- |
- |
- |
2.9239 |
36 |
- |
0.7835 |
0.7832 |
0.7828 |
3.2487 |
40 |
0.1705 |
- |
- |
- |
3.8985 |
48 |
- |
0.7833 |
0.7828 |
0.7829 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.12.2
- 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}
}