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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:956
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/multi-qa-mpnet-base-cos-v1
widget:
- source_sentence: Does my insurance policy exclude medical costs for the first 30
days' illness, but cover accident-related claims?
sentences:
- "any notice for renewal. \nb. Renewal shall not be denied on the ground that\
\ the insured person had made a claim or claims in the preceding \npolicy years."
- '• Minimum entry age for proposer/ spouse/ dependent parents - 18 years
• Maximum Entry Age for proposer/ spouse/ dependent parents - 80 years
• Minimum Entry age for dependent Children - 3 months
• Maximum Entry Age for dependent Children - 25 years'
- "a. Expenses related to the treatment of any illness within 30 days from the\
\ first policy commencement date shall \nbe excluded except claims arising due\
\ to an accident, provided the same are covered."
- source_sentence: I have a pre-authorization for a procedure, what should I bring
along when I get admitted to the hospital to avoid paying the medical bills?
sentences:
- "Obesity/ Weight Control \nChange of Gender treatments\nCosmetic or plastic\
\ Surgery \nHazardous or Adventure sports \nBreach of law \nExcluded Providers\n\
Substance Abuse and Alcohol \nWellness and Rejuvenation \nDietary Supplements\
\ & \nSubstances"
- '56-60 11,950 12,760 7,874 18,887 13,573 9,243 17,848 13,162 21,348 16,437 11,308
24,345 18,177 13,206 35,360 29,906 24,726
61-65 14,352 15,319 9,444 22,688 16,298 11,089 21,442 15,804 25,652 19,744 13,571
29,256 21,833 15,852 42,495 35,932 29,699'
- "specified must be produced to the Network Hospital identified in the pre-authorization\
\ letter at the time of Y our \nadmission to the same.\niii. If the procedure\
\ above is followed, Y ou will not be required to directly pay for the Medical\
\ Expenses above"
- source_sentence: Can you tell me the range of insured sum for a 4 member family
in INR?
sentences:
- "i. Obesity-related cardiomyopathy\n ii. Coronary heart disease\n iii. Severe\
\ Sleep Apnea\n iv. Uncontrolled T ype2 Diabetes\n7. Change-of-gender treatments:\
\ (Excl07)"
- 'Age/
deduc-
tible
200000 200000 300000 200000 300000 500000 300000 500000 300000 500000 1000000
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
7,610 5,553 14,756 12,498 10,354'
- "CIN: U66010PN2000PLC015329, UIN:BAJHLIP23069V032223 13\nFAMILY SIZE: 4 MEMBER\n\
Sum \nInsured \n(in INR)\n300000 500000 1000000 1500000 2000000 2500000 5000000\n\
Age/\ndeduc-\ntible"
- source_sentence: Does IRDAI have rules on portability that let someone who's been
continuously insured under any health policy from an Indian general or health
insurer carry over waiting period benefits?
sentences:
- '◼ WHAT ARE THE EXCLUSIONS AND WAITING PERIOD UNDER THE POLICY?
I. Waiting Period
A. Pre-Existing Diseases - Code- Excl01
a. Expenses related to the treatment of a pre-existing Disease (PED) and its
direct complications shall be excluded'
- "has been continuously covered without any lapses under any health insurance policy\
\ with an Indian General/\nHealth insurer, the proposed insured person will get\
\ the accrued continuity benefits in waiting periods as per \nIRDAI guidelines\
\ on portability."
- "Cumulative Bonus:\n For every claim free policy year, there will be increase\
\ of 10% of \nthe Sum Insured, maximum up to 100%. If a claim is made in any \n\
particular Policy Year, the Cumulative Bonus accrued shall not be \nreduced.\n\
SBIG Health Super T op-Up,"
- source_sentence: what kind of coverage is provided by insurance for medical expenses
that go beyond the normal amount?
sentences:
- "Enhances any existing health policy from any insurance provider \n- corporate\
\ or personal"
- 'Age/
deduc-
tible
200000 200000 300000 200000 300000 500000 300000 500000 300000 500000 1000000
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:
- surajvbangera/mediclaim
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 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
*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
#### mediclaim
* Dataset: [mediclaim](https://huggingface.co/datasets/surajvbangera/mediclaim) at [943cab1](https://huggingface.co/datasets/surajvbangera/mediclaim/tree/943cab115f9a1d649d8a886fb35668e54ad0e1f7)
* Size: 956 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 956 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| 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> |
* Samples:
| anchor | positive |
|:---------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <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> |
| <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> |
| <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> |
* 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
}
```
### Evaluation Dataset
#### mediclaim
* Dataset: [mediclaim](https://huggingface.co/datasets/surajvbangera/mediclaim) at [943cab1](https://huggingface.co/datasets/surajvbangera/mediclaim/tree/943cab115f9a1d649d8a886fb35668e54ad0e1f7)
* Size: 956 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 956 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| 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> |
* Samples:
| anchor | positive |
|:---------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <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> |
| <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> |
| <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> |
* 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
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 40
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 40
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### 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 |
|:--------:|:------:|:-------------:|:---------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| -1 | -1 | - | - | 0.4723 | 0.4748 | 0.5015 | 0.4589 | 0.3867 |
| 1.0 | 2 | - | 1.5925 | 0.4821 | 0.4846 | 0.5122 | 0.4604 | 0.3971 |
| 2.0 | 4 | - | 1.5925 | 0.4821 | 0.4846 | 0.5122 | 0.4604 | 0.3971 |
| 3.0 | 6 | - | 1.0402 | 0.5431 | 0.5468 | 0.5530 | 0.5009 | 0.4435 |
| 4.0 | 8 | - | 0.7900 | 0.5876 | 0.5926 | 0.6075 | 0.5484 | 0.4726 |
| 5.0 | 10 | 33.0646 | 0.6077 | 0.5890 | 0.6039 | 0.6270 | 0.5779 | 0.5072 |
| 6.0 | 12 | - | 0.5213 | 0.6357 | 0.6379 | 0.6522 | 0.5966 | 0.5417 |
| 7.0 | 14 | - | 0.4735 | 0.6425 | 0.6395 | 0.6286 | 0.5995 | 0.5795 |
| 8.0 | 16 | - | 0.4416 | 0.6253 | 0.6387 | 0.6227 | 0.5903 | 0.5738 |
| 9.0 | 18 | - | 0.4236 | 0.6303 | 0.6489 | 0.6387 | 0.6179 | 0.5670 |
| **10.0** | **20** | **8.8456** | **0.4115** | **0.6465** | **0.6519** | **0.6369** | **0.6112** | **0.572** |
| 11.0 | 22 | - | 0.4059 | 0.6447 | 0.6270 | 0.6318 | 0.6169 | 0.5950 |
| 12.0 | 24 | - | 0.4036 | 0.6382 | 0.6318 | 0.6346 | 0.6063 | 0.6026 |
| 13.0 | 26 | - | 0.4022 | 0.6485 | 0.6410 | 0.6441 | 0.6163 | 0.5900 |
| 14.0 | 28 | - | 0.4022 | 0.6520 | 0.6426 | 0.6597 | 0.6225 | 0.6001 |
| 15.0 | 30 | 4.4602 | 0.4033 | 0.6507 | 0.6363 | 0.6576 | 0.6217 | 0.6134 |
| 16.0 | 32 | - | 0.4047 | 0.6530 | 0.6389 | 0.6609 | 0.6350 | 0.6068 |
| 17.0 | 34 | - | 0.4058 | 0.6501 | 0.6344 | 0.6501 | 0.6281 | 0.5997 |
| 18.0 | 36 | - | 0.4067 | 0.6509 | 0.6333 | 0.6553 | 0.6360 | 0.6050 |
| 19.0 | 38 | - | 0.4070 | 0.6561 | 0.6331 | 0.6602 | 0.6397 | 0.6051 |
| 20.0 | 40 | 3.9605 | 0.4071 | 0.6498 | 0.6294 | 0.6397 | 0.6229 | 0.5922 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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