| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:20 |
| - loss:MatryoshkaLoss |
| - loss:MultipleNegativesRankingLoss |
| base_model: nomic-ai/nomic-embed-text-v1.5 |
| widget: |
| - source_sentence: How can I contact my LIC agent or nearest branch according to the |
| provided instructions? |
| sentences: |
| - 'Contact your LIC agent or nearest branch or |
| |
| visit our website |
| |
| or |
| |
| www.licindia.in |
| |
| SMS |
| |
| to |
| |
| , (e.g. Mumbai.’) |
| |
| ‘YOUR CITY NAME’ |
| |
| 566773' |
| - 'LIC''s JEEVAN AROGYA (UIN: 512N266V02) |
| |
| (A Non-linked, Non-Parcipang, |
| |
| Individual, Health Insurance Plan) |
| |
| LIC''s Jeevan Arogya is a unique non-parcipang non-linked plan which provides |
| |
| health insurance cover against certain specified health risks and provides you |
| with |
| |
| mely support in case of medical emergencies and helps you and your family remain |
| |
| financiallyindependentindifficultmes. |
| |
| Health has been a major concern on everybody''s mind, including yours. In these |
| days |
| |
| ofskyrockengmedicalexpenses,whenafamilymemberisill,itisatraumacmefor |
| |
| the rest of the family. As a caring person, you do not want to let any unfortunate |
| |
| incident to affect your plans for you and your family. So why let any medical |
| |
| emergenciessha eryourpeaceofmind.' |
| - 'Contact your LIC agent or nearest branch or |
| |
| visit our website |
| |
| or |
| |
| www.licindia.in |
| |
| SMS |
| |
| to |
| |
| , (e.g. Mumbai.’) |
| |
| ‘YOUR CITY NAME’ |
| |
| 566773' |
| - source_sentence: How does the premium for a spouse (female) change as the age at |
| entry increases from 20 to 50 years? |
| sentences: |
| - 'Benefit to the Applicable Daily Benefit of the previous Policy Year. Such increase |
| in |
| |
| the Applicable Daily Benefit shall be effected on each policy anniversary during |
| the |
| |
| Cover Period and shall connue unl it a ains a maximum amount of 1.5 mes the |
| |
| Inial Daily Benefit. Thereaer, this amount in each Policy Year in future shall |
| |
| remainatthatmaximumlevela ained. |
| |
| |
| Further arithmec addion of an amount equal to “No Claim Benefit” (as |
| |
| described in Para 1.G) below) provided the policy a racts and is eligible for |
| it. |
| |
| Thereshallbeno maximum limitfor such increase which meansthat ifthis policyis |
| |
| eligible for “No Claim Benefit”, the same shall be granted throughout the Cover |
| |
| Periodwithoutanymaximumlimit. |
| |
| For members' |
| - 'chosen, whether you are Principal Insured or other insured life and the mode |
| of |
| |
| payment. |
| |
| Tables below give an indicave annual premium, payable yearly, for all health benefits |
| |
| corresponding to an Inial Daily Benefit of |
| |
| 1000 per day, for some of the ages in |
| |
| |
| respectof variouslivesthatcanbecoveredunderasinglepolicy:' |
| - '(Premiums indicated are exclusive of Taxes) |
| |
| Who can be insured? |
| |
| You (as Principal Insured (PI)), your spouse, your children, your parents and |
| parents of |
| |
| yourspousecanallbeinsured |
| |
| onepolicy.Quiteareliefisn''tit,tohaveallinsured |
| |
| under |
| |
| underonepolicy! |
| |
| Theminimumandmaximumageatentryisasunder: |
| |
| PRINCIPAL INSURED (Male) |
| |
| Age at entry |
| |
| 20 |
| |
| 30 |
| |
| 40 |
| |
| 50 |
| |
| Premium ( ) |
| |
| ` |
| |
| 1922.65 |
| |
| 2242.90 |
| |
| 2799.70 |
| |
| 3768.00 |
| |
| SPOUSE (Female) / PARENT (of PI/Spouse) (Female) |
| |
| Premium ( ) |
| |
| ` |
| |
| 1393.15 |
| |
| 1730.65 |
| |
| 2240.60 |
| |
| 2849.10 |
| |
| Age at entry |
| |
| 20 |
| |
| 30 |
| |
| 40 |
| |
| 50 |
| |
| CHILD |
| |
| Premium ( ) |
| |
| ` |
| |
| 792.00 |
| |
| 794.75 |
| |
| 812.35 |
| |
| 870.75 |
| |
| Age at entry |
| |
| 0 |
| |
| 5 |
| |
| 10 |
| |
| 15 |
| |
| Self / spouse |
| |
| Parents / parents-in-law |
| |
| Children |
| |
| Minimum age |
| |
| at entry |
| |
| 18 years |
| |
| 18 years |
| |
| 91 days |
| |
| Maximum age |
| |
| at entry |
| |
| 65 years (last birthday) |
| |
| 75 (last birthday)' |
| - source_sentence: Which additional benefits are stated to be payable depending on |
| the chosen daily Hospital Cash Benefit? |
| sentences: |
| - '` 2000 per day |
| |
| ` 3000 per day |
| |
| ` 4000 per day |
| |
| This is the amount that will be payable to you in the event of hospitalisaon in |
| the first |
| |
| year on a per day basis. The Major Surgical Benefit that you will be covered for |
| will be |
| |
| 100 mes the Inial Daily Benefit you have chosen. Thus the inial Major Surgical |
| |
| Benefit Sum Assured will be |
| |
| 1 lakh, 2 lakh, 3 lakh, 4 lakh respecvely. Other benefits |
| |
| ` |
| |
| such as Day Care Procedure Benefit, Other Surgical Benefit and Premium waiver |
| |
| Benefit (PWB) menoned below shall also be payable depending upon the daily |
| |
| HospitalCashBenefitchosen. |
| |
| Step 2: Work out the premium payable along with our representave |
| |
| Your premium will depend on your age, gender, the Health cover opon you have' |
| - 'chosen, whether you are Principal Insured or other insured life and the mode |
| of |
| |
| payment. |
| |
| Tables below give an indicave annual premium, payable yearly, for all health benefits |
| |
| corresponding to an Inial Daily Benefit of |
| |
| 1000 per day, for some of the ages in |
| |
| |
| respectof variouslivesthatcanbecoveredunderasinglepolicy:' |
| - '65 years (last birthday) |
| |
| 75 (last birthday) |
| |
| 17 years (last birthday) |
| |
| Howlongareeachinsuredunderthispolicy? |
| |
| Each of the insured are covered for |
| |
| risks up to age (80). Children are insured up |
| |
| Health |
| |
| toage25years. |
| |
| • |
| |
| Hospitalcashbenefit(HCB) |
| |
| • |
| |
| MajorSurgicalBenefit(MSB) |
| |
| • |
| |
| DayCareProcedureBenefit |
| |
| • |
| |
| OtherSurgicalBenefit |
| |
| • |
| |
| AmbulanceBenefit |
| |
| • |
| |
| PremiumwaiverBenefit(PWB) |
| |
| A) HospitalCashBenefit: |
| |
| due to |
| |
| If you or any of the insured lives covered under the policy is hospitalised |
| |
| Accidental Body Injury or Sickness and the stay in hospital exceeds a connuous |
| |
| periodof24hours,thenforanyconnuousperiodof24hoursorpartthereof, |
| |
| 1. Benefits offered under the plan are' |
| - source_sentence: If a policyholder selects a daily Hospital Cash Benefit of 3000 |
| per day, what will be the Initial Major Surgical Benefit sum assured? |
| sentences: |
| - '` 2000 per day |
| |
| ` 3000 per day |
| |
| ` 4000 per day |
| |
| This is the amount that will be payable to you in the event of hospitalisaon in |
| the first |
| |
| year on a per day basis. The Major Surgical Benefit that you will be covered for |
| will be |
| |
| 100 mes the Inial Daily Benefit you have chosen. Thus the inial Major Surgical |
| |
| Benefit Sum Assured will be |
| |
| 1 lakh, 2 lakh, 3 lakh, 4 lakh respecvely. Other benefits |
| |
| ` |
| |
| such as Day Care Procedure Benefit, Other Surgical Benefit and Premium waiver |
| |
| Benefit (PWB) menoned below shall also be payable depending upon the daily |
| |
| HospitalCashBenefitchosen. |
| |
| Step 2: Work out the premium payable along with our representave |
| |
| Your premium will depend on your age, gender, the Health cover opon you have' |
| - '65 years (last birthday) |
| |
| 75 (last birthday) |
| |
| 17 years (last birthday) |
| |
| Howlongareeachinsuredunderthispolicy? |
| |
| Each of the insured are covered for |
| |
| risks up to age (80). Children are insured up |
| |
| Health |
| |
| toage25years. |
| |
| • |
| |
| Hospitalcashbenefit(HCB) |
| |
| • |
| |
| MajorSurgicalBenefit(MSB) |
| |
| • |
| |
| DayCareProcedureBenefit |
| |
| • |
| |
| OtherSurgicalBenefit |
| |
| • |
| |
| AmbulanceBenefit |
| |
| • |
| |
| PremiumwaiverBenefit(PWB) |
| |
| A) HospitalCashBenefit: |
| |
| due to |
| |
| If you or any of the insured lives covered under the policy is hospitalised |
| |
| Accidental Body Injury or Sickness and the stay in hospital exceeds a connuous |
| |
| periodof24hours,thenforanyconnuousperiodof24hoursorpartthereof, |
| |
| 1. Benefits offered under the plan are' |
| - '(Premiums indicated are exclusive of Taxes) |
| |
| Who can be insured? |
| |
| You (as Principal Insured (PI)), your spouse, your children, your parents and |
| parents of |
| |
| yourspousecanallbeinsured |
| |
| onepolicy.Quiteareliefisn''tit,tohaveallinsured |
| |
| under |
| |
| underonepolicy! |
| |
| Theminimumandmaximumageatentryisasunder: |
| |
| PRINCIPAL INSURED (Male) |
| |
| Age at entry |
| |
| 20 |
| |
| 30 |
| |
| 40 |
| |
| 50 |
| |
| Premium ( ) |
| |
| ` |
| |
| 1922.65 |
| |
| 2242.90 |
| |
| 2799.70 |
| |
| 3768.00 |
| |
| SPOUSE (Female) / PARENT (of PI/Spouse) (Female) |
| |
| Premium ( ) |
| |
| ` |
| |
| 1393.15 |
| |
| 1730.65 |
| |
| 2240.60 |
| |
| 2849.10 |
| |
| Age at entry |
| |
| 20 |
| |
| 30 |
| |
| 40 |
| |
| 50 |
| |
| CHILD |
| |
| Premium ( ) |
| |
| ` |
| |
| 792.00 |
| |
| 794.75 |
| |
| 812.35 |
| |
| 870.75 |
| |
| Age at entry |
| |
| 0 |
| |
| 5 |
| |
| 10 |
| |
| 15 |
| |
| Self / spouse |
| |
| Parents / parents-in-law |
| |
| Children |
| |
| Minimum age |
| |
| at entry |
| |
| 18 years |
| |
| 18 years |
| |
| 91 days |
| |
| Maximum age |
| |
| at entry |
| |
| 65 years (last birthday) |
| |
| 75 (last birthday)' |
| - source_sentence: How is the Initial Daily Benefit (the Applicable Daily Benefit |
| for the first policy year) determined and stated in the policy schedule? |
| sentences: |
| - 'Periodwithoutanymaximumlimit. |
| |
| For members |
| |
| subsequently under the policy, the benefit in the first year |
| |
| included |
| |
| shall be equal to Inial Daily Benefit amount and thereaer the Applicable Daily |
| |
| Benefitshallincreaseasabove. |
| |
| IfanyofthememberinsuredisrequiredtostayinanIntensiveCareUnitofahospital, |
| |
| t |
| |
| subject |
| |
| benefit limits and |
| |
| wo mes the |
| |
| Daily |
| |
| will be payable |
| |
| to |
| |
| Applicable |
| |
| Benefit |
| |
| condionsmenonedinPara11A)andexclusionsmenonedinPara15below. |
| |
| During one period of 24 connuous hours (i.e. one day) of Hospitalisaon (aer |
| |
| having completed the 24 hours as above), if the said Hospitalisaon included stay |
| |
| inanIntensiveCareUnitaswellasinanyotherin-paent(non-IntensiveCareUnit)' |
| - 'emergenciessha eryourpeaceofmind. |
| |
| LIC''sJeevanArogyagivesyou: |
| |
| • |
| |
| Valuablefinancialproteconincaseofhospitalisaon,surgeryetc |
| |
| • |
| |
| IncreasingHealthcovereveryyear |
| |
| • |
| |
| Lumpsumbenefitirrespecveofactualmedicalcosts |
| |
| • |
| |
| Noclaimbenefit |
| |
| • |
| |
| Flexiblebenefitlimittochoosefrom |
| |
| • |
| |
| Flexiblepremiumpaymentopons |
| |
| • |
| |
| Veryeasytochooseyourplan |
| |
| Step 1 |
| |
| 2 |
| |
| Step |
| |
| Choose the level of Health cover you need |
| |
| Work out the premium payable along with our Representave |
| |
| Step 1: Choose the level of Health cover you need: |
| |
| You can choose the amount of Inial Daily Benefit (i.e. the daily Hospital Cash |
| Benefit |
| |
| applicableinthefirstyearofthepolicy)asperyourneedfromoutofthefollowingchoices: |
| |
| ` 1000 per day |
| |
| ` 2000 per day |
| |
| ` 3000 per day |
| |
| ` 4000 per day' |
| - 'provided any such part |
| |
| exceeds a connuous period of 4 hours (aer having |
| |
| stay |
| |
| completed the 24 hours as above) in a non-ICU ward/room of a hospital, an |
| |
| amount equal to the Applicable Daily Benefit (ADB) available under the policy |
| |
| during that policy year shall be payable subject to benefit limits and condions |
| |
| menonedinPara11A)andexclusionsmenonedinPara15below. |
| |
| During the first |
| |
| of cover commencement in respect of each insured, the |
| |
| year |
| |
| ApplicableDailyBenefitshallbetheInialDailyBenefitamountchosenbyyouand |
| |
| menonedinthepolicySchedule. |
| |
| Theamountof DBforeachpolicyyear,aerthefirstpolicyyear,shallconsistof2parts: |
| |
| A |
| |
| |
| An arithmec addion of an amount equal to 5% (five percent) of the Inial Daily' |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy@1 |
| - cosine_accuracy@3 |
| - cosine_accuracy@5 |
| - cosine_accuracy@10 |
| - cosine_precision@1 |
| - cosine_precision@3 |
| - cosine_precision@5 |
| - cosine_precision@10 |
| - cosine_recall@1 |
| - cosine_recall@3 |
| - cosine_recall@5 |
| - cosine_recall@10 |
| - cosine_ndcg@10 |
| - cosine_mrr@10 |
| - cosine_map@100 |
| model-index: |
| - name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5 |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: Unknown |
| type: unknown |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.5454545454545454 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.7727272727272727 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.9090909090909091 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 1.0 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.5454545454545454 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.2575757575757575 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.18181818181818185 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.10000000000000003 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.5454545454545454 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.7727272727272727 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.9090909090909091 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 1.0 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.773062927015556 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.7011363636363636 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.7011363636363636 |
| name: Cosine Map@100 |
| --- |
| |
| # SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/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](https://huggingface.co/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 |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### 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]]) |
| ``` |
| <!-- |
| ### 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>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.5455 | |
| | cosine_accuracy@3 | 0.7727 | |
| | cosine_accuracy@5 | 0.9091 | |
| | cosine_accuracy@10 | 1.0 | |
| | cosine_precision@1 | 0.5455 | |
| | cosine_precision@3 | 0.2576 | |
| | cosine_precision@5 | 0.1818 | |
| | cosine_precision@10 | 0.1 | |
| | cosine_recall@1 | 0.5455 | |
| | cosine_recall@3 | 0.7727 | |
| | cosine_recall@5 | 0.9091 | |
| | cosine_recall@10 | 1.0 | |
| | **cosine_ndcg@10** | **0.7731** | |
| | cosine_mrr@10 | 0.7011 | |
| | cosine_map@100 | 0.7011 | |
| |
| <!-- |
| ## 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: 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: |
| | sentence_0 | sentence_1 | |
| |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <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>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) 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`: {'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 |
| | Epoch | Step | cosine_ndcg@10 | |
| |:-----:|:----:|:--------------:| |
| | 1.0 | 2 | 0.7731 | |
| |
| |
| ### 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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