insurance-rag-embeddings2

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-MiniLM-L6-v2 <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Okay, I need to create two high-quality, diverse questions based on the given insurance policy context. Let me start by understanding the context thoroughly.',
    'Hospitalizaon\nDaily Hospital Cash Benefit would also be paid for first 24 hours (day one) of\nhospitalizaon, regardless of whether the Insured was admi ed in a general or\nspecialwardorinanintensivecareunit.\nB) Major\nBenefit:\nSurgical\nIn the event of an Insured under this plan, due to medical necessity, undergoing\none of the surgeries defined in Major Surgical Benefit Annexure, within the cover\nperiod in a hospital due to Accidental Bodily Injury or Sickness, the respecve\nbenefit percentage of the Major Surgical Benefit Sum Assured, as specified against\neach of the eligible surgeries menoned in Major Surgical Benefit Annexure, shall\nbe paid subject to benefit limits and condions menoned in Para 11B) and\nexclusionsmenonedinPara15below.',
    'Benefitshallincreaseasabove.\nIfanyofthememberinsuredisrequiredtostayinanIntensiveCareUnitofahospital,\nt\nsubject\nbenefit limits and\nwo mes the\nDaily\nwill be payable\nto\nApplicable\nBenefit\ncondionsmenonedinPara11A)andexclusionsmenonedinPara15below.\nDuring one period of 24 connuous hours (i.e. one day) of Hospitalisaon (aer\nhaving completed the 24 hours as above), if the said Hospitalisaon included stay\ninanIntensiveCareUnitaswellasinanyotherin-paent(non-IntensiveCareUnit)\nward of the Hospital, the Corporaon shall pay benefits as if the admission was to\nthe Intensive Care Unit provided that the period of Hospitalisaon in the Intensive\nCareUnitwasatleast4connuoushours.\npayable\nor\nNo benefit will be\nfor the first 24 hours of hospitalisaon. However, f\nevery\nthat extends for a connuous period of 7 days or more, the\nHospitalizaon\nDaily Hospital Cash Benefit would also be paid for first 24 hours (day one) of\nhospitalizaon, regardless of whether the Insured was admi ed in a general or',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3726, 0.2615],
#         [0.3726, 1.0000, 0.7728],
#         [0.2615, 0.7728, 1.0000]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Information Retrieval

• Evaluated with <code>InformationRetrievalEvaluator</code>

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 20 training samples
• Columns: <code>sentence_0</code> and <code>sentence_1</code>
• Approximate statistics based on the first 20 samples:

  	sentence_0	sentence_1
  type	string	string
  details	<ul><li>min: 26 tokens</li><li>mean: 56.7 tokens</li><li>max: 98 tokens</li></ul>	<ul><li>min: 44 tokens</li><li>mean: 214.1 tokens</li><li>max: 256 tokens</li></ul>

• Samples:

  sentence_0	sentence_1
  <code>What happens if a policyholder chooses a lower Initial Daily Benefit (e.g., ₹1,000) but later requires a major surgery costing significantly more than the 100x multiplier of their selected daily benefit? How does the policy’s lump sum benefit structure affect their coverage in this scenario?</code>	<code>•<br>IncreasingHealthcovereveryyear<br>•<br>Lumpsumbenefitirrespecveofactualmedicalcosts<br>•<br>Noclaimbenefit<br>•<br>Flexiblebenefitlimittochoosefrom<br>•<br>Flexiblepremiumpaymentopons<br>•<br>Veryeasytochooseyourplan<br>Step 1<br>2<br>Step<br>Choose the level of Health cover you need<br>Work out the premium payable along with our Representave<br>Step 1: Choose the level of Health cover you need:<br>You can choose the amount of Inial Daily Benefit (i.e. the daily Hospital Cash Benefit<br>applicableinthefirstyearofthepolicy)asperyourneedfromoutofthefollowingchoices:<br> 1000 per day<br> 2000 per day<br> 3000 per day<br> 4000 per day<br>This is the amount that will be payable to you in the event of hospitalisaon in the first<br>year on a per day basis. The Major Surgical Benefit that you will be covered for will be<br>100 mes the Inial Daily Benefit you have chosen. Thus the inial Major Surgical<br>Benefit Sum Assured will be<br>1 lakh, 2 lakh, 3 lakh, 4 lakh respecvely. Other benefits<br>`<br>such as Day Care Procedure Benefit, Other Surgical Benefit and Premium waiver</code>
  <code>Okay, let's tackle this. The user wants me to generate two high-quality, diverse questions based on the context provided about LIC's Jeevan Arogya. The first question needs to be a direct factual one, and the second a complex scenario-based one. They should not overlap and be challenging.</code>	<code>LIC's JEEVAN AROGYA (UIN: 512N266V02)<br>(A Non-linked, Non-Parcipang,<br>Individual, Health Insurance Plan)<br>LIC's Jeevan Arogya is a unique non-parcipang non-linked plan which provides<br>health insurance cover against certain specified health risks and provides you with<br>mely support in case of medical emergencies and helps you and your family remain<br>financiallyindependentindifficultmes.<br>Health has been a major concern on everybody's mind, including yours. In these days<br>ofskyrockengmedicalexpenses,whenafamilymemberisill,itisatraumacmefor<br>the rest of the family. As a caring person, you do not want to let any unfortunate<br>incident to affect your plans for you and your family. So why let any medical<br>emergenciessha eryourpeaceofmind.<br>LIC'sJeevanArogyagivesyou:<br>•<br>Valuablefinancialproteconincaseofhospitalisaon,surgeryetc<br>•<br>IncreasingHealthcovereveryyear<br>•<br>Lumpsumbenefitirrespecveofactualmedicalcosts<br>•<br>Noclaimbenefit<br>•<br>Flexiblebenefitlimittochoosefrom<br>•<br>Flexiblepremiumpaymentopons<br>•</code>
  <code>Okay, let me tackle this. The user wants two high-quality, diverse questions based on the given insurance policy context. First, I need to understand the context thoroughly.</code>	<code>Each of the insured are covered for<br>risks up to age (80). Children are insured up<br>Health<br>toage25years.<br>•<br>Hospitalcashbenefit(HCB)<br>•<br>MajorSurgicalBenefit(MSB)<br>•<br>DayCareProcedureBenefit<br>•<br>OtherSurgicalBenefit<br>•<br>AmbulanceBenefit<br>•<br>PremiumwaiverBenefit(PWB)<br>A) HospitalCashBenefit:<br>due to<br>If you or any of the insured lives covered under the policy is hospitalised<br>Accidental Body Injury or Sickness and the stay in hospital exceeds a connuous<br>periodof24hours,thenforanyconnuousperiodof24hoursorpartthereof,<br>1. Benefits offered under the plan are</code>

• Loss: <code>MatryoshkaLoss</code> with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 10
• per_device_eval_batch_size: 10
• num_train_epochs: 5
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• do_predict: False
• prediction_loss_only: True
• per_device_train_batch_size: 10
• per_device_eval_batch_size: 10
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 5
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_ratio: None
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• enable_jit_checkpoint: False
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• use_cpu: False
• seed: 42
• data_seed: None
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: -1
• ddp_backend: None
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• 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
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• hub_revision: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_for_metrics: []
• eval_do_concat_batches: True
• auto_find_batch_size: False
• full_determinism: False
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_num_input_tokens_seen: no
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: True
• use_cache: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Training Time

• Training: 4.9 seconds

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.4.1
• Transformers: 5.0.0
• PyTorch: 2.10.0+cu128
• Accelerate: 1.13.0
• Datasets: 4.8.5
• Tokenizers: 0.22.2

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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

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