|
--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:956 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/multi-qa-mpnet-base-cos-v1 |
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widget: |
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- source_sentence: Does my insurance policy exclude medical costs for the first 30 |
|
days' illness, but cover accident-related claims? |
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sentences: |
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- "any notice for renewal. \nb. Renewal shall not be denied on the ground that\ |
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\ the insured person had made a claim or claims in the preceding \npolicy years." |
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- '• Minimum entry age for proposer/ spouse/ dependent parents - 18 years |
|
|
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• Maximum Entry Age for proposer/ spouse/ dependent parents - 80 years |
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|
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• Minimum Entry age for dependent Children - 3 months |
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|
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• Maximum Entry Age for dependent Children - 25 years' |
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- "a. Expenses related to the treatment of any illness within 30 days from the\ |
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\ first policy commencement date shall \nbe excluded except claims arising due\ |
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\ to an accident, provided the same are covered." |
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- source_sentence: I have a pre-authorization for a procedure, what should I bring |
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along when I get admitted to the hospital to avoid paying the medical bills? |
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sentences: |
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- "Obesity/ Weight Control \nChange of Gender treatments\nCosmetic or plastic\ |
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\ Surgery \nHazardous or Adventure sports \nBreach of law \nExcluded Providers\n\ |
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Substance Abuse and Alcohol \nWellness and Rejuvenation \nDietary Supplements\ |
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\ & \nSubstances" |
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- '56-60 11,950 12,760 7,874 18,887 13,573 9,243 17,848 13,162 21,348 16,437 11,308 |
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24,345 18,177 13,206 35,360 29,906 24,726 |
|
|
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61-65 14,352 15,319 9,444 22,688 16,298 11,089 21,442 15,804 25,652 19,744 13,571 |
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29,256 21,833 15,852 42,495 35,932 29,699' |
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- "specified must be produced to the Network Hospital identified in the pre-authorization\ |
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\ letter at the time of Y our \nadmission to the same.\niii. If the procedure\ |
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\ above is followed, Y ou will not be required to directly pay for the Medical\ |
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\ Expenses above" |
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- source_sentence: Can you tell me the range of insured sum for a 4 member family |
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in INR? |
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sentences: |
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- "i. Obesity-related cardiomyopathy\n ii. Coronary heart disease\n iii. Severe\ |
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\ Sleep Apnea\n iv. Uncontrolled T ype2 Diabetes\n7. Change-of-gender treatments:\ |
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\ (Excl07)" |
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- 'Age/ |
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|
|
deduc- |
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|
|
tible |
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|
|
200000 200000 300000 200000 300000 500000 300000 500000 300000 500000 1000000 |
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300000 500000 1000000 300000 500000 1000000 |
|
|
|
21-25 5,010 5,361 3,326 7,906 5,695 3,899 7,466 5,523 8,918 6,882 4,759 10,163 |
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7,610 5,553 14,756 12,498 10,354' |
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- "CIN: U66010PN2000PLC015329, UIN:BAJHLIP23069V032223 13\nFAMILY SIZE: 4 MEMBER\n\ |
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Sum \nInsured \n(in INR)\n300000 500000 1000000 1500000 2000000 2500000 5000000\n\ |
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Age/\ndeduc-\ntible" |
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- source_sentence: Does IRDAI have rules on portability that let someone who's been |
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continuously insured under any health policy from an Indian general or health |
|
insurer carry over waiting period benefits? |
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sentences: |
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- '◼ WHAT ARE THE EXCLUSIONS AND WAITING PERIOD UNDER THE POLICY? |
|
|
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I. Waiting Period |
|
|
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A. Pre-Existing Diseases - Code- Excl01 |
|
|
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a. Expenses related to the treatment of a pre-existing Disease (PED) and its |
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direct complications shall be excluded' |
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- "has been continuously covered without any lapses under any health insurance policy\ |
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\ with an Indian General/\nHealth insurer, the proposed insured person will get\ |
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\ the accrued continuity benefits in waiting periods as per \nIRDAI guidelines\ |
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\ on portability." |
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- "Cumulative Bonus:\n For every claim free policy year, there will be increase\ |
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\ of 10% of \nthe Sum Insured, maximum up to 100%. If a claim is made in any \n\ |
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particular Policy Year, the Cumulative Bonus accrued shall not be \nreduced.\n\ |
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SBIG Health Super T op-Up," |
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- source_sentence: what kind of coverage is provided by insurance for medical expenses |
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that go beyond the normal amount? |
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sentences: |
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- "Enhances any existing health policy from any insurance provider \n- corporate\ |
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\ or personal" |
|
- 'Age/ |
|
|
|
deduc- |
|
|
|
tible |
|
|
|
200000 200000 300000 200000 300000 500000 300000 500000 300000 500000 1000000 |
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300000 500000 1000000 300000 500000 1000000 |
|
|
|
21-25 6,544 7,011 4,345 10,389 7,490 5,127 9,839 7,283 11,767 9,087 6,289 13,419 |
|
10,054 7,343 19,518 16,543 13,717' |
|
- "health insurance cover and provides wider health protection for you and your\ |
|
\ family. In case of higher expenses \ndue to illness or accidents, Extra Care\ |
|
\ Plus policy takes care of the additional expenses. It is important to consider" |
|
datasets: |
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- surajvbangera/mediclaim |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
|
metrics: |
|
- cosine_accuracy@1 |
|
- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
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- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
model-index: |
|
- name: SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-cos-v1 |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3020833333333333 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8020833333333334 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.875 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9583333333333334 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3020833333333333 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2673611111111111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17499999999999996 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09583333333333333 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3020833333333333 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8020833333333334 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.875 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9583333333333334 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.6497808285407043 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.5484209656084658 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5512795209742883 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.28125 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.78125 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.875 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9479166666666666 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.28125 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2604166666666667 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17499999999999996 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09479166666666665 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.28125 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.78125 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.875 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9479166666666666 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.6294431516700937 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.5250578703703704 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5287000615125614 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3020833333333333 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7916666666666666 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8854166666666666 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9375 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3020833333333333 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2638888888888889 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1770833333333333 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09375 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3020833333333333 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7916666666666666 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8854166666666666 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9375 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.6396822227743622 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.5409846230158731 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5445532958553793 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.2708333333333333 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.78125 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.84375 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9479166666666666 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.2708333333333333 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2604166666666667 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16874999999999996 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09479166666666666 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.2708333333333333 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.78125 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.84375 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9479166666666666 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.6229142362169651 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.5167080026455027 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5187267142104471 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.25 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7291666666666666 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8333333333333334 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9166666666666666 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.25 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.24305555555555558 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16666666666666666 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09166666666666666 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.25 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7291666666666666 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8333333333333334 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9166666666666666 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5921613565527261 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.486338458994709 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.49077409326175775 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-cos-v1 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-mpnet-base-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-cos-v1) on the [mediclaim](https://huggingface.co/datasets/surajvbangera/mediclaim) 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:** [sentence-transformers/multi-qa-mpnet-base-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-cos-v1) <!-- at revision 822dbc9732879fe45b5d79fdb372f2ccec4c76b5 --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 dimensions |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
|
- [mediclaim](https://huggingface.co/datasets/surajvbangera/mediclaim) |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel |
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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: |
|
|
|
```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("surajvbangera/mediclaim_embedding") |
|
# Run inference |
|
sentences = [ |
|
'what kind of coverage is provided by insurance for medical expenses that go beyond the normal amount?', |
|
'health insurance cover and provides wider health protection for you and your family. In case of higher expenses \ndue to illness or accidents, Extra Care Plus policy takes care of the additional expenses. It is important to consider', |
|
'Age/\ndeduc-\ntible\n200000 200000 300000 200000 300000 500000 300000 500000 300000 500000 1000000 300000 500000 1000000 300000 500000 1000000\n21-25 6,544 7,011 4,345 10,389 7,490 5,127 9,839 7,283 11,767 9,087 6,289 13,419 10,054 7,343 19,518 16,543 13,717', |
|
] |
|
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] |
|
``` |
|
|
|
<!-- |
|
### 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 |
|
|
|
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |
|
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------| |
|
| cosine_accuracy@1 | 0.3021 | 0.2812 | 0.3021 | 0.2708 | 0.25 | |
|
| cosine_accuracy@3 | 0.8021 | 0.7812 | 0.7917 | 0.7812 | 0.7292 | |
|
| cosine_accuracy@5 | 0.875 | 0.875 | 0.8854 | 0.8438 | 0.8333 | |
|
| cosine_accuracy@10 | 0.9583 | 0.9479 | 0.9375 | 0.9479 | 0.9167 | |
|
| cosine_precision@1 | 0.3021 | 0.2812 | 0.3021 | 0.2708 | 0.25 | |
|
| cosine_precision@3 | 0.2674 | 0.2604 | 0.2639 | 0.2604 | 0.2431 | |
|
| cosine_precision@5 | 0.175 | 0.175 | 0.1771 | 0.1687 | 0.1667 | |
|
| cosine_precision@10 | 0.0958 | 0.0948 | 0.0938 | 0.0948 | 0.0917 | |
|
| cosine_recall@1 | 0.3021 | 0.2812 | 0.3021 | 0.2708 | 0.25 | |
|
| cosine_recall@3 | 0.8021 | 0.7812 | 0.7917 | 0.7812 | 0.7292 | |
|
| cosine_recall@5 | 0.875 | 0.875 | 0.8854 | 0.8438 | 0.8333 | |
|
| cosine_recall@10 | 0.9583 | 0.9479 | 0.9375 | 0.9479 | 0.9167 | |
|
| **cosine_ndcg@10** | **0.6498** | **0.6294** | **0.6397** | **0.6229** | **0.5922** | |
|
| cosine_mrr@10 | 0.5484 | 0.5251 | 0.541 | 0.5167 | 0.4863 | |
|
| cosine_map@100 | 0.5513 | 0.5287 | 0.5446 | 0.5187 | 0.4908 | |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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|
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### Training Dataset |
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|
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#### mediclaim |
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|
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* Dataset: [mediclaim](https://huggingface.co/datasets/surajvbangera/mediclaim) at [943cab1](https://huggingface.co/datasets/surajvbangera/mediclaim/tree/943cab115f9a1d649d8a886fb35668e54ad0e1f7) |
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* Size: 956 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 956 samples: |
|
| | anchor | positive | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 10 tokens</li><li>mean: 23.14 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 57.2 tokens</li><li>max: 135 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
|
|:---------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Can I get a preventive health check-up covered under my insurance, and if yes, is there a limit to it?</code> | <code>by the Medical Practitioner.<br> vii. The Deductible shall not be applicable on this bene�t.<br> Stay Fit Health Check Up<br> The Insured may avail a health check-up, only for Preventive <br>Test, up to a limit speci�ed in the Policy Schedule, provided</code> | |
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| <code>Which claims are excluded if they don't follow the Transplantation of Human Organs Amendment Bill 2011?</code> | <code>4 CIN: U66010PN2000PLC015329, UIN: BAJHLIP23069V032223<br> Specific exclusions:<br> 1. Claims which have NOT been admitted under Medical expenses section<br> 2. Claims not in compliance with THE TRANSPLANTATION OF HUMAN ORGANS (AMENDMENT) BILL, 2011</code> | |
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| <code>Will the insurance pay for lawful abortion and related hospital stays?</code> | <code>ii. We will also cover expenses towards lawful medical termination of pregnancy during the Policy period.<br> iii. In patient Hospitalization Expenses of pre-natal and post-natal hospitalization</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Evaluation Dataset |
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#### mediclaim |
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* Dataset: [mediclaim](https://huggingface.co/datasets/surajvbangera/mediclaim) at [943cab1](https://huggingface.co/datasets/surajvbangera/mediclaim/tree/943cab115f9a1d649d8a886fb35668e54ad0e1f7) |
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* Size: 956 evaluation samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 956 samples: |
|
| | anchor | positive | |
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 10 tokens</li><li>mean: 22.4 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 56.76 tokens</li><li>max: 133 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:---------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Is there any refund for medical exams if I get a policy and it's accepted?</code> | <code>• If pre-policy checkup is conducted, 50% of the medical tests charges would be reimbursed, subject to acceptance <br>of proposal and policy issuance.<br>Age of the person <br>to be insured<br>Sum Insured Medical Examination</code> | |
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| <code>Are there any exclusions for coverage of substance abuse treatment or its consequences?</code> | <code>are payable but not the complete claim. <br>12. T reatment for Alcoholism, drug or substance abuse or any addictive condition and consequences thereof. <br>(Excl12)</code> | |
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| <code>Can you tell me about the medical bills I might have within 90 days after being discharged?</code> | <code>CIN: U66010PN2000PLC015329, UIN:BAJHLIP23069V032223 3<br> c. Post-hospitalisation expenses<br> The medical expenses incurred in the 90 days immediately after you were discharged, provided that:</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 40 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 40 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
|
</details> |
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|
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### Training Logs |
|
| Epoch | Step | Training Loss | Validation 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 | |
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|:--------:|:------:|:-------------:|:---------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
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| -1 | -1 | - | - | 0.4723 | 0.4748 | 0.5015 | 0.4589 | 0.3867 | |
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| 1.0 | 2 | - | 1.5925 | 0.4821 | 0.4846 | 0.5122 | 0.4604 | 0.3971 | |
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| 2.0 | 4 | - | 1.5925 | 0.4821 | 0.4846 | 0.5122 | 0.4604 | 0.3971 | |
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| 3.0 | 6 | - | 1.0402 | 0.5431 | 0.5468 | 0.5530 | 0.5009 | 0.4435 | |
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| 4.0 | 8 | - | 0.7900 | 0.5876 | 0.5926 | 0.6075 | 0.5484 | 0.4726 | |
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| 5.0 | 10 | 33.0646 | 0.6077 | 0.5890 | 0.6039 | 0.6270 | 0.5779 | 0.5072 | |
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| 6.0 | 12 | - | 0.5213 | 0.6357 | 0.6379 | 0.6522 | 0.5966 | 0.5417 | |
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| 7.0 | 14 | - | 0.4735 | 0.6425 | 0.6395 | 0.6286 | 0.5995 | 0.5795 | |
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| 8.0 | 16 | - | 0.4416 | 0.6253 | 0.6387 | 0.6227 | 0.5903 | 0.5738 | |
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| 9.0 | 18 | - | 0.4236 | 0.6303 | 0.6489 | 0.6387 | 0.6179 | 0.5670 | |
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| **10.0** | **20** | **8.8456** | **0.4115** | **0.6465** | **0.6519** | **0.6369** | **0.6112** | **0.572** | |
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| 11.0 | 22 | - | 0.4059 | 0.6447 | 0.6270 | 0.6318 | 0.6169 | 0.5950 | |
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| 12.0 | 24 | - | 0.4036 | 0.6382 | 0.6318 | 0.6346 | 0.6063 | 0.6026 | |
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| 13.0 | 26 | - | 0.4022 | 0.6485 | 0.6410 | 0.6441 | 0.6163 | 0.5900 | |
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| 14.0 | 28 | - | 0.4022 | 0.6520 | 0.6426 | 0.6597 | 0.6225 | 0.6001 | |
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| 15.0 | 30 | 4.4602 | 0.4033 | 0.6507 | 0.6363 | 0.6576 | 0.6217 | 0.6134 | |
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| 16.0 | 32 | - | 0.4047 | 0.6530 | 0.6389 | 0.6609 | 0.6350 | 0.6068 | |
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| 17.0 | 34 | - | 0.4058 | 0.6501 | 0.6344 | 0.6501 | 0.6281 | 0.5997 | |
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| 18.0 | 36 | - | 0.4067 | 0.6509 | 0.6333 | 0.6553 | 0.6360 | 0.6050 | |
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| 19.0 | 38 | - | 0.4070 | 0.6561 | 0.6331 | 0.6602 | 0.6397 | 0.6051 | |
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| 20.0 | 40 | 3.9605 | 0.4071 | 0.6498 | 0.6294 | 0.6397 | 0.6229 | 0.5922 | |
|
|
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* The bold row denotes the saved checkpoint. |
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|
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### Framework Versions |
|
- Python: 3.11.11 |
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- Sentence Transformers: 3.4.1 |
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- Transformers: 4.48.3 |
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- PyTorch: 2.5.1+cu124 |
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- Accelerate: 1.3.0 |
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- Datasets: 3.3.2 |
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- Tokenizers: 0.21.0 |
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|
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## Citation |
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|
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### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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|
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#### MatryoshkaLoss |
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```bibtex |
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@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
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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}, |
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eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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|
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#### MultipleNegativesRankingLoss |
|
```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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