Sentence Similarity
sentence-transformers
Safetensors
nomic_bert
feature-extraction
Generated from Trainer
dataset_size:20
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use justOneMoreTestCase/insurance-rag-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use justOneMoreTestCase/insurance-rag-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("justOneMoreTestCase/insurance-rag-embeddings", trust_remote_code=True) sentences = [ "How can I contact my LIC agent or nearest branch according to the provided instructions?", "Contact your LIC agent or nearest branch or\nvisit our website\nor\nwww.licindia.in\nSMS\nto\n, (e.g. Mumbai.’)\n‘YOUR CITY NAME’\n566773", "LIC's JEEVAN AROGYA (UIN: 512N266V02)\n(A Non-linked, Non-Parcipang,\nIndividual, Health Insurance Plan)\nLIC's Jeevan Arogya is a unique non-parcipang non-linked plan which provides\nhealth insurance cover against certain specified health risks and provides you with\nmely support in case of medical emergencies and helps you and your family remain\nfinanciallyindependentindifficultmes.\nHealth has been a major concern on everybody's mind, including yours. In these days\nofskyrockengmedicalexpenses,whenafamilymemberisill,itisatraumacmefor\nthe rest of the family. As a caring person, you do not want to let any unfortunate\nincident to affect your plans for you and your family. So why let any medical\nemergenciessha eryourpeaceofmind.", "Contact your LIC agent or nearest branch or\nvisit our website\nor\nwww.licindia.in\nSMS\nto\n, (e.g. Mumbai.’)\n‘YOUR CITY NAME’\n566773" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:20
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: nomic-ai/nomic-embed-text-v1.5
widget:
- source_sentence: >-
How can I contact my LIC agent or nearest branch according to the provided
instructions?
sentences:
- |-
Contact your LIC agent or nearest branch or
visit our website
or
www.licindia.in
SMS
to
, (e.g. Mumbai.’)
‘YOUR CITY NAME’
566773
- >-
LIC's JEEVAN AROGYA (UIN: 512N266V02)
(A Non-linked, Non-Parcipang,
Individual, Health Insurance Plan)
LIC's Jeevan Arogya is a unique non-parcipang non-linked plan which
provides
health insurance cover against certain specified health risks and
provides you with
mely support in case of medical emergencies and helps you and your
family remain
financiallyindependentindifficultmes.
Health has been a major concern on everybody's mind, including yours. In
these days
ofskyrockengmedicalexpenses,whenafamilymemberisill,itisatraumacmefor
the rest of the family. As a caring person, you do not want to let any
unfortunate
incident to affect your plans for you and your family. So why let any
medical
emergenciessha eryourpeaceofmind.
- |-
Contact your LIC agent or nearest branch or
visit our website
or
www.licindia.in
SMS
to
, (e.g. Mumbai.’)
‘YOUR CITY NAME’
566773
- source_sentence: >-
How does the premium for a spouse (female) change as the age at entry
increases from 20 to 50 years?
sentences:
- >-
Benefit to the Applicable Daily Benefit of the previous Policy Year. Such
increase in
the Applicable Daily Benefit shall be effected on each policy anniversary
during the
Cover Period and shall connue unl it a ains a maximum amount of 1.5 mes
the
Inial Daily Benefit. Thereaer, this amount in each Policy Year in future
shall
remainatthatmaximumlevela ained.
Further arithmec addion of an amount equal to “No Claim Benefit” (as
described in Para 1.G) below) provided the policy a racts and is
eligible for it.
Thereshallbeno maximum limitfor such increase which meansthat ifthis
policyis
eligible for “No Claim Benefit”, the same shall be granted throughout the
Cover
Periodwithoutanymaximumlimit.
For members
- >-
chosen, whether you are Principal Insured or other insured life and the
mode of
payment.
Tables below give an indicave annual premium, payable yearly, for all
health benefits
corresponding to an Inial Daily Benefit of
1000 per day, for some of the ages in
respectof variouslivesthatcanbecoveredunderasinglepolicy:
- >-
(Premiums indicated are exclusive of Taxes)
Who can be insured?
You (as Principal Insured (PI)), your spouse, your children, your
parents and parents of
yourspousecanallbeinsured
onepolicy.Quiteareliefisn'tit,tohaveallinsured
under
underonepolicy!
Theminimumandmaximumageatentryisasunder:
PRINCIPAL INSURED (Male)
Age at entry
20
30
40
50
Premium ( )
`
1922.65
2242.90
2799.70
3768.00
SPOUSE (Female) / PARENT (of PI/Spouse) (Female)
Premium ( )
`
1393.15
1730.65
2240.60
2849.10
Age at entry
20
30
40
50
CHILD
Premium ( )
`
792.00
794.75
812.35
870.75
Age at entry
0
5
10
15
Self / spouse
Parents / parents-in-law
Children
Minimum age
at entry
18 years
18 years
91 days
Maximum age
at entry
65 years (last birthday)
75 (last birthday)
- source_sentence: >-
Which additional benefits are stated to be payable depending on the chosen
daily Hospital Cash Benefit?
sentences:
- >-
` 2000 per day
` 3000 per day
` 4000 per day
This is the amount that will be payable to you in the event of
hospitalisaon in the first
year on a per day basis. The Major Surgical Benefit that you will be
covered for will be
100 mes the Inial Daily Benefit you have chosen. Thus the inial Major
Surgical
Benefit Sum Assured will be
1 lakh, 2 lakh, 3 lakh, 4 lakh respecvely. Other benefits
`
such as Day Care Procedure Benefit, Other Surgical Benefit and Premium
waiver
Benefit (PWB) menoned below shall also be payable depending upon the
daily
HospitalCashBenefitchosen.
Step 2: Work out the premium payable along with our representave
Your premium will depend on your age, gender, the Health cover opon you
have
- >-
chosen, whether you are Principal Insured or other insured life and the
mode of
payment.
Tables below give an indicave annual premium, payable yearly, for all
health benefits
corresponding to an Inial Daily Benefit of
1000 per day, for some of the ages in
respectof variouslivesthatcanbecoveredunderasinglepolicy:
- >-
65 years (last birthday)
75 (last birthday)
17 years (last birthday)
Howlongareeachinsuredunderthispolicy?
Each of the insured are covered for
risks up to age (80). Children are insured up
Health
toage25years.
•
Hospitalcashbenefit(HCB)
•
MajorSurgicalBenefit(MSB)
•
DayCareProcedureBenefit
•
OtherSurgicalBenefit
•
AmbulanceBenefit
•
PremiumwaiverBenefit(PWB)
A) HospitalCashBenefit:
due to
If you or any of the insured lives covered under the policy is
hospitalised
Accidental Body Injury or Sickness and the stay in hospital exceeds a
connuous
periodof24hours,thenforanyconnuousperiodof24hoursorpartthereof,
1. Benefits offered under the plan are
- source_sentence: >-
If a policyholder selects a daily Hospital Cash Benefit of 3000 per day,
what will be the Initial Major Surgical Benefit sum assured?
sentences:
- >-
` 2000 per day
` 3000 per day
` 4000 per day
This is the amount that will be payable to you in the event of
hospitalisaon in the first
year on a per day basis. The Major Surgical Benefit that you will be
covered for will be
100 mes the Inial Daily Benefit you have chosen. Thus the inial Major
Surgical
Benefit Sum Assured will be
1 lakh, 2 lakh, 3 lakh, 4 lakh respecvely. Other benefits
`
such as Day Care Procedure Benefit, Other Surgical Benefit and Premium
waiver
Benefit (PWB) menoned below shall also be payable depending upon the
daily
HospitalCashBenefitchosen.
Step 2: Work out the premium payable along with our representave
Your premium will depend on your age, gender, the Health cover opon you
have
- >-
65 years (last birthday)
75 (last birthday)
17 years (last birthday)
Howlongareeachinsuredunderthispolicy?
Each of the insured are covered for
risks up to age (80). Children are insured up
Health
toage25years.
•
Hospitalcashbenefit(HCB)
•
MajorSurgicalBenefit(MSB)
•
DayCareProcedureBenefit
•
OtherSurgicalBenefit
•
AmbulanceBenefit
•
PremiumwaiverBenefit(PWB)
A) HospitalCashBenefit:
due to
If you or any of the insured lives covered under the policy is
hospitalised
Accidental Body Injury or Sickness and the stay in hospital exceeds a
connuous
periodof24hours,thenforanyconnuousperiodof24hoursorpartthereof,
1. Benefits offered under the plan are
- >-
(Premiums indicated are exclusive of Taxes)
Who can be insured?
You (as Principal Insured (PI)), your spouse, your children, your
parents and parents of
yourspousecanallbeinsured
onepolicy.Quiteareliefisn'tit,tohaveallinsured
under
underonepolicy!
Theminimumandmaximumageatentryisasunder:
PRINCIPAL INSURED (Male)
Age at entry
20
30
40
50
Premium ( )
`
1922.65
2242.90
2799.70
3768.00
SPOUSE (Female) / PARENT (of PI/Spouse) (Female)
Premium ( )
`
1393.15
1730.65
2240.60
2849.10
Age at entry
20
30
40
50
CHILD
Premium ( )
`
792.00
794.75
812.35
870.75
Age at entry
0
5
10
15
Self / spouse
Parents / parents-in-law
Children
Minimum age
at entry
18 years
18 years
91 days
Maximum age
at entry
65 years (last birthday)
75 (last birthday)
- source_sentence: >-
How is the Initial Daily Benefit (the Applicable Daily Benefit for the
first policy year) determined and stated in the policy schedule?
sentences:
- >-
Periodwithoutanymaximumlimit.
For members
subsequently under the policy, the benefit in the first year
included
shall be equal to Inial Daily Benefit amount and thereaer the Applicable
Daily
Benefitshallincreaseasabove.
IfanyofthememberinsuredisrequiredtostayinanIntensiveCareUnitofahospital,
t
subject
benefit limits and
wo mes the
Daily
will be payable
to
Applicable
Benefit
condionsmenonedinPara11A)andexclusionsmenonedinPara15below.
During one period of 24 connuous hours (i.e. one day) of Hospitalisaon
(aer
having completed the 24 hours as above), if the said Hospitalisaon
included stay
inanIntensiveCareUnitaswellasinanyotherin-paent(non-IntensiveCareUnit)
- >-
emergenciessha eryourpeaceofmind.
LIC'sJeevanArogyagivesyou:
•
Valuablefinancialproteconincaseofhospitalisaon,surgeryetc
•
IncreasingHealthcovereveryyear
•
Lumpsumbenefitirrespecveofactualmedicalcosts
•
Noclaimbenefit
•
Flexiblebenefitlimittochoosefrom
•
Flexiblepremiumpaymentopons
•
Veryeasytochooseyourplan
Step 1
2
Step
Choose the level of Health cover you need
Work out the premium payable along with our Representave
Step 1: Choose the level of Health cover you need:
You can choose the amount of Inial Daily Benefit (i.e. the daily Hospital
Cash Benefit
applicableinthefirstyearofthepolicy)asperyourneedfromoutofthefollowingchoices:
` 1000 per day
` 2000 per day
` 3000 per day
` 4000 per day
- >-
provided any such part
exceeds a connuous period of 4 hours (aer having
stay
completed the 24 hours as above) in a non-ICU ward/room of a hospital,
an
amount equal to the Applicable Daily Benefit (ADB) available under the
policy
during that policy year shall be payable subject to benefit limits and
condions
menonedinPara11A)andexclusionsmenonedinPara15below.
During the first
of cover commencement in respect of each insured, the
year
ApplicableDailyBenefitshallbetheInialDailyBenefitamountchosenbyyouand
menonedinthepolicySchedule.
Theamountof
DBforeachpolicyyear,aerthefirstpolicyyear,shallconsistof2parts:
A
An arithmec addion of an amount equal to 5% (five percent) of the Inial
Daily
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.5454545454545454
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7727272727272727
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9090909090909091
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5454545454545454
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2575757575757575
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18181818181818185
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10000000000000003
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5454545454545454
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7727272727272727
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9090909090909091
name: Cosine Recall@5
- type: cosine_recall@10
value: 1
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.773062927015556
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7011363636363636
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7011363636363636
name: Cosine Map@100
SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: nomic-ai/nomic-embed-text-v1.5
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Cosine Similarity
- Supported Modality: Text
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'NomicBertModel'})
(1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
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("sentence_transformers_model_id")
# Run inference
sentences = [
'How is the Initial Daily Benefit (the Applicable Daily Benefit for the first policy year) determined and stated in the policy schedule?',
'provided any such part\nexceeds a connuous period of 4 hours (aer having\nstay\ncompleted the 24 hours as above) in a non-ICU ward/room of a hospital, an\namount equal to the Applicable Daily Benefit (ADB) available under the policy\nduring that policy year shall be payable subject to benefit limits and condions\nmenonedinPara11A)andexclusionsmenonedinPara15below.\nDuring the first\nof cover commencement in respect of each insured, the\nyear\nApplicableDailyBenefitshallbetheInialDailyBenefitamountchosenbyyouand\nmenonedinthepolicySchedule.\nTheamountof DBforeachpolicyyear,aerthefirstpolicyyear,shallconsistof2parts:\nA\n\nAn arithmec addion of an amount equal to 5% (five percent) of the Inial Daily',
'Periodwithoutanymaximumlimit.\nFor members\nsubsequently under the policy, the benefit in the first year\nincluded\nshall be equal to Inial Daily Benefit amount and thereaer the Applicable Daily\nBenefitshallincreaseasabove.\nIfanyofthememberinsuredisrequiredtostayinanIntensiveCareUnitofahospital,\nt\nsubject\nbenefit limits and\nwo mes the\nDaily\nwill be payable\nto\nApplicable\nBenefit\ncondionsmenonedinPara11A)andexclusionsmenonedinPara15below.\nDuring one period of 24 connuous hours (i.e. one day) of Hospitalisaon (aer\nhaving completed the 24 hours as above), if the said Hospitalisaon included stay\ninanIntensiveCareUnitaswellasinanyotherin-paent(non-IntensiveCareUnit)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6203, 0.6283],
# [0.6203, 1.0000, 0.8679],
# [0.6283, 0.8679, 1.0000]])
Evaluation
Metrics
Information Retrieval
- Evaluated with
InformationRetrievalEvaluator
| Metric | Value |
|---|---|
| cosine_accuracy@1 | 0.5455 |
| cosine_accuracy@3 | 0.7727 |
| cosine_accuracy@5 | 0.9091 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.5455 |
| cosine_precision@3 | 0.2576 |
| cosine_precision@5 | 0.1818 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.5455 |
| cosine_recall@3 | 0.7727 |
| cosine_recall@5 | 0.9091 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.7731 |
| cosine_mrr@10 | 0.7011 |
| cosine_map@100 | 0.7011 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 20 training samples
- Columns:
sentence_0andsentence_1 - Approximate statistics based on the first 20 samples:
sentence_0 sentence_1 type string string details - min: 19 tokens
- mean: 29.65 tokens
- max: 56 tokens
- min: 44 tokens
- mean: 173.0 tokens
- max: 226 tokens
- Samples:
sentence_0 sentence_1 Which specific benefits (e.g., Hospital Cash Benefit, Major Surgical Benefit, Day Care Procedure Benefit, etc.) are available to the insured if they are hospitalized for a continuous period of 24 hours or more?65 years (last birthday)
75 (last birthday)
17 years (last birthday)
Howlongareeachinsuredunderthispolicy?
Each of the insured are covered for
risks up to age (80). Children are insured up
Health
toage25years.
•
Hospitalcashbenefit(HCB)
•
MajorSurgicalBenefit(MSB)
•
DayCareProcedureBenefit
•
OtherSurgicalBenefit
•
AmbulanceBenefit
•
PremiumwaiverBenefit(PWB)
A) HospitalCashBenefit:
due to
If you or any of the insured lives covered under the policy is hospitalised
Accidental Body Injury or Sickness and the stay in hospital exceeds a connuous
periodof24hours,thenforanyconnuousperiodof24hoursorpartthereof,
1. Benefits offered under the plan areWhat are the four daily Hospital Cash Benefit options available when choosing the initial Daily Benefit for the LIC Jeevan Arogya policy?emergenciessha eryourpeaceofmind.
LIC'sJeevanArogyagivesyou:
•
Valuablefinancialproteconincaseofhospitalisaon,surgeryetc
•
IncreasingHealthcovereveryyear
•
Lumpsumbenefitirrespecveofactualmedicalcosts
•
Noclaimbenefit
•
Flexiblebenefitlimittochoosefrom
•
Flexiblepremiumpaymentopons
•
Veryeasytochooseyourplan
Step 1
2
Step
Choose the level of Health cover you need
Work out the premium payable along with our Representave
Step 1: Choose the level of Health cover you need:
You can choose the amount of Inial Daily Benefit (i.e. the daily Hospital Cash Benefit
applicableinthefirstyearofthepolicy)asperyourneedfromoutofthefollowingchoices:1000 per day<br>2000 per day3000 per day<br>4000 per dayIf a policyholder selects a daily Hospital Cash Benefit of 3000 per day, what will be the Initial Major Surgical Benefit sum assured?2000 per day<br>3000 per day4000 per day<br>This is the amount that will be payable to you in the event of hospitalisaon in the first<br>year on a per day basis. The Major Surgical Benefit that you will be covered for will be<br>100 mes the Inial Daily Benefit you have chosen. Thus the inial Major Surgical<br>Benefit Sum Assured will be<br>1 lakh, 2 lakh, 3 lakh, 4 lakh respecvely. Other benefits<br>
such as Day Care Procedure Benefit, Other Surgical Benefit and Premium waiver
Benefit (PWB) menoned below shall also be payable depending upon the daily
HospitalCashBenefitchosen.
Step 2: Work out the premium payable along with our representave
Your premium will depend on your age, gender, the Health cover opon you have - Loss:
MatryoshkaLosswith 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
per_device_train_batch_size: 10per_device_eval_batch_size: 10num_train_epochs: 5multi_dataset_batch_sampler: round_robin
All Hyperparameters
Click to expand
do_predict: Falseprediction_loss_only: Trueper_device_train_batch_size: 10per_device_eval_batch_size: 10gradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 5e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1num_train_epochs: 5max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: Nonewarmup_ratio: Nonewarmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Trueenable_jit_checkpoint: Falsesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseuse_cpu: Falseseed: 42data_seed: Nonebf16: Falsefp16: Falsebf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: -1ddp_backend: Nonedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonedisable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}parallelism_config: Nonedeepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torch_fusedoptim_args: Nonegroup_by_length: Falselength_column_name: lengthproject: huggingfacetrackio_space_id: trackioddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Truepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Nonehub_always_push: Falsehub_revision: Nonegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_for_metrics: []eval_do_concat_batches: Trueauto_find_batch_size: Falsefull_determinism: Falseddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Noneinclude_num_input_tokens_seen: noneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseliger_kernel_config: Noneeval_use_gather_object: Falseaverage_tokens_across_devices: Trueuse_cache: Falseprompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: round_robinrouter_mapping: {}learning_rate_mapping: {}
Training Logs
| Epoch | Step | cosine_ndcg@10 |
|---|---|---|
| 1.0 | 2 | 0.7731 |
Training Time
- Training: 1.8 minutes
Framework Versions
- Python: 3.12.13
- Sentence Transformers: 5.4.1
- Transformers: 5.0.0
- PyTorch: 2.10.0+cpu
- Accelerate: 1.13.0
- Datasets: 4.8.5
- Tokenizers: 0.22.2
Citation
BibTeX
Sentence Transformers
@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{oord2019representationlearningcontrastivepredictive,
title={Representation Learning with Contrastive Predictive Coding},
author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
year={2019},
eprint={1807.03748},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/1807.03748},
}