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justOneMoreTestCase/insurance-rag-embeddings

sourceHugging Faceupdated 5mo agoView on Hugging Face
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Model Card

SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5

This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

  • —Model Type: Sentence Transformer
  • —Base model: nomic-ai/nomic-embed-text-v1.5 <!-- at revision e9b6763023c676ca8431644204f50c2b100d9aab -->
  • —Maximum Sequence Length: 8192 tokens
  • —Output Dimensionality: 768 dimensions
  • —Similarity Function: Cosine Similarity
  • —Supported Modality: Text <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

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': 'NomicBertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

bash
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'How is the Initial Daily Benefit (the Applicable Daily Benefit for the first policy year) determined and stated in the policy schedule?',
    'provided any such part\nexceeds a connuous period of 4 hours (aer having\nstay\ncompleted the 24 hours as above) in a non-ICU ward/room of a hospital, an\namount equal to the Applicable Daily Benefit (ADB) available under the policy\nduring that policy year shall be payable subject to benefit limits and condions\nmenonedinPara11A)andexclusionsmenonedinPara15below.\nDuring the first\nof cover commencement in respect of each insured, the\nyear\nApplicableDailyBenefitshallbetheInialDailyBenefitamountchosenbyyouand\nmenonedinthepolicySchedule.\nTheamountof DBforeachpolicyyear,aerthefirstpolicyyear,shallconsistof2parts:\nA\n\nAn arithmec addion of an amount equal to 5% (five percent) of the Inial Daily',
    'Periodwithoutanymaximumlimit.\nFor members\nsubsequently under the policy, the benefit in the first year\nincluded\nshall be equal to Inial Daily Benefit amount and thereaer the Applicable Daily\nBenefitshallincreaseasabove.\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)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6203, 0.6283],
#         [0.6203, 1.0000, 0.8679],
#         [0.6283, 0.8679, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Information Retrieval
MetricValue
cosine_accuracy@10.5455
cosine_accuracy@30.7727
cosine_accuracy@50.9091
cosine_accuracy@101.0
cosine_precision@10.5455
cosine_precision@30.2576
cosine_precision@50.1818
cosine_precision@100.1
cosine_recall@10.5455
cosine_recall@30.7727
cosine_recall@50.9091
cosine_recall@101.0
cosine_ndcg@100.7731
cosine_mrr@100.7011
cosine_map@1000.7011

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Training Details

Training Dataset

Unnamed Dataset
  • —Size: 20 training samples
  • —Columns: <code>sentence0</code> and <code>sentence1</code>
  • —Approximate statistics based on the first 20 samples: | | sentence0 | sentence1 | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 19 tokens</li><li>mean: 29.65 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 173.0 tokens</li><li>max: 226 tokens</li></ul> |
  • —Samples: | sentence0 | sentence1 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Which specific benefits (e.g., Hospital Cash Benefit, Major Surgical Benefit, Day Care Procedure Benefit, etc.) are available to the insured if they are hospitalized for a continuous period of 24 hours or more?</code> | <code>65 years (last birthday)<br>75 (last birthday)<br>17 years (last birthday)<br>Howlongareeachinsuredunderthispolicy?<br>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> | | <code>What are the four daily Hospital Cash Benefit options available when choosing the initial Daily Benefit for the LIC Jeevan Arogya policy?</code> | <code>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>•<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</code> | | <code>If a policyholder selects a daily Hospital Cash Benefit of 3000 per day, what will be the Initial Major Surgical Benefit sum assured?</code> | <code> 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<br>Benefit (PWB) menoned below shall also be payable depending upon the daily<br>HospitalCashBenefitchosen.<br>Step 2: Work out the premium payable along with our representave<br>Your premium will depend on your age, gender, the Health cover opon you have</code> |
  • —Loss: <code>MatryoshkaLoss</code> with these parameters:
json
  {
      "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
  • —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
  • —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
  • —parallelism_config: None
  • —deepspeed: None
  • —label_smoothing_factor: 0.0
  • —optim: adamwtorchfused
  • —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

EpochStepcosine_ndcg@10
1.020.7731

Training Time

  • —Training: 1.8 minutes

Framework Versions

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

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{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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