metadata

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. 

        b.  Renewal shall not be denied on the ground that the insured person
        had made a claim or claims in the preceding 

        policy 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 

        be 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 
        Change of Gender treatments
        Cosmetic or plastic Surgery 
        Hazardous or Adventure sports 
        Breach of law 
        Excluded Providers
        Substance Abuse and Alcohol 
        Wellness and Rejuvenation 
        Dietary Supplements & 
        Substances
      - >-
        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 

        admission to the same.

        iii.  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
           ii. Coronary heart disease
           iii. Severe Sleep Apnea
           iv. Uncontrolled T ype2 Diabetes
        7.  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
        FAMILY SIZE: 4 MEMBER
        Sum 
        Insured 
        (in INR)
        300000 500000 1000000 1500000 2000000 2500000 5000000
        Age/
        deduc-
        tible
  - 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/

        Health insurer, the proposed insured person will get the accrued
        continuity benefits in waiting periods as per 

        IRDAI guidelines on portability.
      - |-
        Cumulative Bonus:
         For every claim free policy year, there will be increase of 10% of 
        the Sum Insured, maximum up to 100%. If a claim is made in any 
        particular Policy Year, the Cumulative Bonus accrued shall not be 
        reduced.
        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 
        - 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 

        due 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 model finetuned from sentence-transformers/multi-qa-mpnet-base-cos-v1 on the 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- mediclaim

Model Sources

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

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("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]

Evaluation

Metrics

Information Retrieval

Datasets: dim_768, dim_512, dim_256, dim_128 and dim_64
Evaluated with 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

Training Details

Training Dataset

mediclaim

Dataset: mediclaim at 943cab1
Size: 956 training samples
Columns: anchor and positive
Approximate statistics based on the first 956 samples:
anchor positive
type string string
details
min: 10 tokens
mean: 23.14 tokens
max: 85 tokens

min: 6 tokens
mean: 57.2 tokens
max: 135 tokens

	anchor	positive
type	string	string
details	min: 10 tokens mean: 23.14 tokens max: 85 tokens	min: 6 tokens mean: 57.2 tokens max: 135 tokens

Samples:

anchor	positive
`Can I get a preventive health check-up covered under my insurance, and if yes, is there a limit to it?`	`by the Medical Practitioner. vii. The Deductible shall not be applicable on this bene�t. Stay Fit Health Check Up The Insured may avail a health check-up, only for Preventive Test, up to a limit speci�ed in the Policy Schedule, provided`
`Which claims are excluded if they don't follow the Transplantation of Human Organs Amendment Bill 2011?`	`4 CIN: U66010PN2000PLC015329, UIN: BAJHLIP23069V032223 Specific exclusions: 1. Claims which have NOT been admitted under Medical expenses section 2. Claims not in compliance with THE TRANSPLANTATION OF HUMAN ORGANS (AMENDMENT) BILL, 2011`
`Will the insurance pay for lawful abortion and related hospital stays?`	`ii. We will also cover expenses towards lawful medical termination of pregnancy during the Policy period. iii. In patient Hospitalization Expenses of pre-natal and post-natal hospitalization`

Loss: MatryoshkaLoss with these parameters:

{
    "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 at 943cab1
Size: 956 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 956 samples:
anchor positive
type string string
details
min: 10 tokens
mean: 22.4 tokens
max: 62 tokens

min: 6 tokens
mean: 56.76 tokens
max: 133 tokens

	anchor	positive
type	string	string
details	min: 10 tokens mean: 22.4 tokens max: 62 tokens	min: 6 tokens mean: 56.76 tokens max: 133 tokens

Samples:

anchor	positive
`Is there any refund for medical exams if I get a policy and it's accepted?`	`• If pre-policy checkup is conducted, 50% of the medical tests charges would be reimbursed, subject to acceptance of proposal and policy issuance. Age of the person to be insured Sum Insured Medical Examination`
`Are there any exclusions for coverage of substance abuse treatment or its consequences?`	`are payable but not the complete claim. 12. T reatment for Alcoholism, drug or substance abuse or any addictive condition and consequences thereof. (Excl12)`
`Can you tell me about the medical bills I might have within 90 days after being discharged?`	`CIN: U66010PN2000PLC015329, UIN:BAJHLIP23069V032223 3 c. Post-hospitalisation expenses The medical expenses incurred in the 90 days immediately after you were discharged, provided that:`

Loss: MatryoshkaLoss with these parameters:

{
    "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

Click to expand

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

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

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{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}
}