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BGE Health Insurance Matryoshka

Name: bge-finetuned-insurance-matryoshka
Availability: InStock

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 dimensions
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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("ethanteh/bge-finetuned-insurance-matryoshka")
# Run inference
sentences = [
    'Pre-retirement Deductible 0; Post-retirement Deductible 0\n\nAge Male Female MSIII 200 MSIII 300 MSIII 500 MSIII 200 MSIII 300 28 1,166 1,590 2,324 1,246 1,755 29 1,184 1,598 2,336 1,264 1,763 30 1,212 1,609 2,345 1,284 1,772 31 1,241 1,648 2,403 1,305 1,783 32 1,272 1,693 2,469 1,336 1,791 33 1,305 1,744 2,544 1,366 1,829 34 1,340 1,799 2,625 1,396 1,873 35 1,384 1,865 2,723 1,426 1,918 36 1,430 1,932 2,821 1,457 1,962 37 1,490 2,016 2,944 1,490 2,004 38 1,555 2,103 3,072 1,528 2,046 39 1,658 2,239 3,272 1,567 2,087 40 1,794 2,419 3,536 1,610 2,134 41 1,930 2,600 3,801 1,674 2,214 MSIII 500 2,564 2,576 2,588 2,602 2,606 2,661 2,725 2,791 2,856 2,918 2,980 3,041 3,110 3,228\n\nNote:\n\nFor presentation purposes, the insurance charges are rounded up to the next ringgit.\n\nAnnual Insurance Charges for HLA MediShield III Rider\n\nOccupational Class 1 & 2',
    'What are the annual insurance charges for a 35-year-old male under HLA MediShield III Rider, Occupational Class 1 & 2, assuming pre and post retirement deductibles are 0?',
    'What types of post-hospitalization support are included in the medical coverage?',
]
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

Datasets: dim_768, dim_512, dim_256, dim_128 and dim_64
Evaluated with InformationRetrievalEvaluator

Metric	dim_768	dim_512	dim_256	dim_128	dim_64
cosine_accuracy@1	0.0602	0.0843	0.0843	0.0964	0.0723
cosine_accuracy@3	0.5301	0.494	0.494	0.4578	0.4096
cosine_accuracy@5	0.6627	0.6386	0.6506	0.5783	0.5301
cosine_accuracy@10	0.7711	0.7831	0.7711	0.7952	0.7349
cosine_precision@1	0.0602	0.0843	0.0843	0.0964	0.0723
cosine_precision@3	0.1767	0.1647	0.1647	0.1526	0.1365
cosine_precision@5	0.1325	0.1277	0.1301	0.1157	0.106
cosine_precision@10	0.0771	0.0783	0.0771	0.0795	0.0735
cosine_recall@1	0.0602	0.0843	0.0843	0.0964	0.0723
cosine_recall@3	0.5301	0.494	0.494	0.4578	0.4096
cosine_recall@5	0.6627	0.6386	0.6506	0.5783	0.5301
cosine_recall@10	0.7711	0.7831	0.7711	0.7952	0.7349
cosine_ndcg@10	0.4436	0.4512	0.4432	0.4422	0.4003
cosine_mrr@10	0.3357	0.3434	0.337	0.331	0.2946
cosine_map@100	0.3482	0.3544	0.3477	0.3393	0.3079

Training Details

Training Dataset

json

Dataset: json
Size: 739 training samples
Columns: positive and anchor
Approximate statistics based on the first 739 samples:
positive anchor
type string string
details
min: 13 tokens
mean: 262.79 tokens
max: 512 tokens
min: 10 tokens
mean: 20.63 tokens
max: 48 tokens

Samples:

positive	anchor
Summary Table of Critical Illness Coverage (Con’t)[object Object][object Object]Benefit Payout[object Object][object Object]No Critical Illnesses Early Stage 50% of Rider Sum Assured 36 40 46 Intermediate Stage 50% of Rider Sum Assured Advanced Stage 100% of Rider Sum Assured deficit with persisting clinical[object Object][object Object]Third Degree Burns – of specified[object Object][object Object]Summary Table of Critical Illness Coverage (Con’t)[object Object][object Object]Critical Illnesses Early Stage 50% of Rider Sum Assured Human Immunodeficiency - Insertion of a Vena-cava filter Benefit Payout Intermediate Stage 50% of Rider Sum Assured Advanced Stage Hypertension – of specified[object Object][object Object]No[object Object][object Object]100% of Rider Sum Assured[object Object][object Object]Human Immunodeficiency Virus[object Object][object Object]50[object Object][object Object]in[object Object][object Object]56[object Object][object Object]60[object Object][object Object]Cardiomyopathy – of specified[object Object][object Object]Summary Table of Critical Illness Coverage (Con’t)[object Object][object Object]Critical Illnesses Early Stage 50% of Rider Sum Assured 61 62 63 64 65 66 67 68 69 70 Benefit Payout Intermediate Stage 50% of Rider Sum Assured Advanced Stage 100% of Rider Sum Assured deficit with persisting clinical[object Object][object Object]No[object Object][object Object]Special Benefit[object Object][object Object]Special Benefit Diabetes Recovery Benefit...	`What benefits are available for conditions like third-degree burns, HIV, or cardiomyopathy, according to this table?`
Q: Where can I purchase this plan?[object Object][object Object]official website at greateasternlife.com/my/direct[object Object][object Object]Q: How do I pay my premiums?[object Object][object Object]A: You have the flexibility to pay your premium annually or half-[object Object][object Object]Q: Am I covered for emergency medical assistance outside Malaysia?[object Object][object Object]A: You are covered for emergency medical assistance received while travelling outside Malaysia but subject to a maximum of 120 consecutive days on any one trip.[object Object][object Object]Note: Terms and conditions apply.[object Object][object Object]Q: Am I covered for treatment outside Malaysia?[object Object][object Object]attending physician, benefits in respect of the treatment[object Object][object Object]for[object Object][object Object]Q: How much will I get upon surrender of my policy?[object Object][object Object]This plan has no surrender value. However, upon cancellation of the policy by policyowner, you will be entitled for a proportionate refund (where applicable) of the last premium paid as below:[object Object][object Object]A:[object Object][object Object]Policy purchased for a period Refund of Annual Refund of Half- less than or Premium Yearly Premium equivalent to 3 months 30% 30% 30%[object Object][object Object]Note: The above refunds are only applicable provide...	`What are the premium payment options for the GREAT Health Direct plan?`
66[object Object][object Object]Uremic[object Object][object Object]Syndrome[object Object][object Object]Syndrome[object Object][object Object]Nil[object Object][object Object]Nil[object Object][object Object]Syndrome[object Object][object Object]No. 67 68 69 70 71 72 73 74 75 76 Category Necrotising Fasciitis Respiratory Diphteria requiring mechanical ventilation Loss of limb(s) Cerebral Aneurysm Requiring Brain Surgery Multiple Root Avulsions of Brachial Plexus Rheumatic Fever with Heart Valve Disorders Resection of the whole small intestine (duodenum, jejunum and ileum) Severe Pulmonary Fibrosis Rabies Generalized • Nil • Nil • Nil • Nil • Nil • Nil • Nil • Nil • Nil • Nil A-Plus Beyond Early Critical Shield Early Intermediate Critical Illness Critical Illness • Nil • Nil • Nil • Nil • Nil • Nil • Nil • Nil • Nil • Nil A-Plus Beyond Critical Shield Advanced Critical Illness • Necrotising Fasciitis • Respiratory Diphteria requiring mechanical ventilation • Loss of limb(s) • Cerebral Aneurysm Requiring Brain Surgery • Multiple Root Avulsions of Brachial Plexus • Rheumatic Fever with Heart Valve Disorders • Resection of the whole small intestine (duodenum, jejunum and ileum) •...	`What illnesses are listed under the 'Early Critical Illness' category?`

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: 10
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: True
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
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: 10
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: True
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.8602	5	-	0.4356	0.4285	0.4026	0.3877	0.3048
1.8602	10	46.1696	0.4348	0.4279	0.4035	0.3920	0.3294
2.8602	15	-	0.4340	0.4252	0.4032	0.4127	0.3518
3.8602	20	20.5361	0.4403	0.4493	0.4117	0.4156	0.3761
4.8602	25	-	0.4558	0.4413	0.4233	0.4166	0.3859
5.8602	30	15.2732	0.4407	0.4359	0.4259	0.4266	0.3974
6.8602	35	-	0.4416	0.4408	0.4389	0.4417	0.3983
7.8602	40	11.1479	0.4427	0.4512	0.4432	0.4427	0.397
8.8602	45	-	0.4460	0.4512	0.4432	0.4422	0.4003
9.8602	50	12.725	0.4436	0.4512	0.4432	0.4422	0.4003

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.3
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 2.19.1
Tokenizers: 0.21.0

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: 13 tokens mean: 262.79 tokens max: 512 tokens	min: 10 tokens mean: 20.63 tokens max: 48 tokens