--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:20 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: nomic-ai/nomic-embed-text-v1.5 widget: - source_sentence: How can I contact my LIC agent or nearest branch according to the provided instructions? sentences: - 'Contact your LIC agent or nearest branch or visit our website or www.licindia.in SMS to , (e.g. Mumbai.’) ‘YOUR CITY NAME’ 566773' - 'LIC''s JEEVAN AROGYA (UIN: 512N266V02) (A Non-linked, Non-Parcipang, Individual, Health Insurance Plan) LIC''s Jeevan Arogya is a unique non-parcipang non-linked plan which provides health insurance cover against certain specified health risks and provides you with mely support in case of medical emergencies and helps you and your family remain financiallyindependentindifficultmes. Health has been a major concern on everybody''s mind, including yours. In these days ofskyrockengmedicalexpenses,whenafamilymemberisill,itisatraumacmefor the rest of the family. As a caring person, you do not want to let any unfortunate incident to affect your plans for you and your family. So why let any medical emergenciessha eryourpeaceofmind.' - 'Contact your LIC agent or nearest branch or visit our website or www.licindia.in SMS to , (e.g. Mumbai.’) ‘YOUR CITY NAME’ 566773' - source_sentence: How does the premium for a spouse (female) change as the age at entry increases from 20 to 50 years? sentences: - 'Benefit to the Applicable Daily Benefit of the previous Policy Year. Such increase in the Applicable Daily Benefit shall be effected on each policy anniversary during the Cover Period and shall connue unl it a ains a maximum amount of 1.5 mes the Inial Daily Benefit. Thereaer, this amount in each Policy Year in future shall remainatthatmaximumlevela ained. Further arithmec addion of an amount equal to “No Claim Benefit” (as described in Para 1.G) below) provided the policy a racts and is eligible for it. Thereshallbeno maximum limitfor such increase which meansthat ifthis policyis eligible for “No Claim Benefit”, the same shall be granted throughout the Cover Periodwithoutanymaximumlimit. For members' - 'chosen, whether you are Principal Insured or other insured life and the mode of payment. Tables below give an indicave annual premium, payable yearly, for all health benefits corresponding to an Inial Daily Benefit of 1000 per day, for some of the ages in respectof variouslivesthatcanbecoveredunderasinglepolicy:' - '(Premiums indicated are exclusive of Taxes) Who can be insured? You (as Principal Insured (PI)), your spouse, your children, your parents and parents of yourspousecanallbeinsured onepolicy.Quiteareliefisn''tit,tohaveallinsured under underonepolicy! Theminimumandmaximumageatentryisasunder: PRINCIPAL INSURED (Male) Age at entry 20 30 40 50 Premium ( ) ` 1922.65 2242.90 2799.70 3768.00 SPOUSE (Female) / PARENT (of PI/Spouse) (Female) Premium ( ) ` 1393.15 1730.65 2240.60 2849.10 Age at entry 20 30 40 50 CHILD Premium ( ) ` 792.00 794.75 812.35 870.75 Age at entry 0 5 10 15 Self / spouse Parents / parents-in-law Children Minimum age at entry 18 years 18 years 91 days Maximum age at entry 65 years (last birthday) 75 (last birthday)' - source_sentence: Which additional benefits are stated to be payable depending on the chosen daily Hospital Cash Benefit? sentences: - '` 2000 per day ` 3000 per day ` 4000 per day This is the amount that will be payable to you in the event of hospitalisaon in the first year on a per day basis. The Major Surgical Benefit that you will be covered for will be 100 mes the Inial Daily Benefit you have chosen. Thus the inial Major Surgical Benefit Sum Assured will be 1 lakh, 2 lakh, 3 lakh, 4 lakh respecvely. Other benefits ` such as Day Care Procedure Benefit, Other Surgical Benefit and Premium waiver Benefit (PWB) menoned below shall also be payable depending upon the daily HospitalCashBenefitchosen. Step 2: Work out the premium payable along with our representave Your premium will depend on your age, gender, the Health cover opon you have' - 'chosen, whether you are Principal Insured or other insured life and the mode of payment. Tables below give an indicave annual premium, payable yearly, for all health benefits corresponding to an Inial Daily Benefit of 1000 per day, for some of the ages in respectof variouslivesthatcanbecoveredunderasinglepolicy:' - '65 years (last birthday) 75 (last birthday) 17 years (last birthday) Howlongareeachinsuredunderthispolicy? Each of the insured are covered for risks up to age (80). Children are insured up Health toage25years. • Hospitalcashbenefit(HCB) • MajorSurgicalBenefit(MSB) • DayCareProcedureBenefit • OtherSurgicalBenefit • AmbulanceBenefit • PremiumwaiverBenefit(PWB) A) HospitalCashBenefit: due to If you or any of the insured lives covered under the policy is hospitalised Accidental Body Injury or Sickness and the stay in hospital exceeds a connuous periodof24hours,thenforanyconnuousperiodof24hoursorpartthereof, 1. Benefits offered under the plan are' - source_sentence: If a policyholder selects a daily Hospital Cash Benefit of 3000 per day, what will be the Initial Major Surgical Benefit sum assured? sentences: - '` 2000 per day ` 3000 per day ` 4000 per day This is the amount that will be payable to you in the event of hospitalisaon in the first year on a per day basis. The Major Surgical Benefit that you will be covered for will be 100 mes the Inial Daily Benefit you have chosen. Thus the inial Major Surgical Benefit Sum Assured will be 1 lakh, 2 lakh, 3 lakh, 4 lakh respecvely. Other benefits ` such as Day Care Procedure Benefit, Other Surgical Benefit and Premium waiver Benefit (PWB) menoned below shall also be payable depending upon the daily HospitalCashBenefitchosen. Step 2: Work out the premium payable along with our representave Your premium will depend on your age, gender, the Health cover opon you have' - '65 years (last birthday) 75 (last birthday) 17 years (last birthday) Howlongareeachinsuredunderthispolicy? Each of the insured are covered for risks up to age (80). Children are insured up Health toage25years. • Hospitalcashbenefit(HCB) • MajorSurgicalBenefit(MSB) • DayCareProcedureBenefit • OtherSurgicalBenefit • AmbulanceBenefit • PremiumwaiverBenefit(PWB) A) HospitalCashBenefit: due to If you or any of the insured lives covered under the policy is hospitalised Accidental Body Injury or Sickness and the stay in hospital exceeds a connuous periodof24hours,thenforanyconnuousperiodof24hoursorpartthereof, 1. Benefits offered under the plan are' - '(Premiums indicated are exclusive of Taxes) Who can be insured? You (as Principal Insured (PI)), your spouse, your children, your parents and parents of yourspousecanallbeinsured onepolicy.Quiteareliefisn''tit,tohaveallinsured under underonepolicy! Theminimumandmaximumageatentryisasunder: PRINCIPAL INSURED (Male) Age at entry 20 30 40 50 Premium ( ) ` 1922.65 2242.90 2799.70 3768.00 SPOUSE (Female) / PARENT (of PI/Spouse) (Female) Premium ( ) ` 1393.15 1730.65 2240.60 2849.10 Age at entry 20 30 40 50 CHILD Premium ( ) ` 792.00 794.75 812.35 870.75 Age at entry 0 5 10 15 Self / spouse Parents / parents-in-law Children Minimum age at entry 18 years 18 years 91 days Maximum age at entry 65 years (last birthday) 75 (last birthday)' - source_sentence: How is the Initial Daily Benefit (the Applicable Daily Benefit for the first policy year) determined and stated in the policy schedule? sentences: - 'Periodwithoutanymaximumlimit. For members subsequently under the policy, the benefit in the first year included shall be equal to Inial Daily Benefit amount and thereaer the Applicable Daily Benefitshallincreaseasabove. IfanyofthememberinsuredisrequiredtostayinanIntensiveCareUnitofahospital, t subject benefit limits and wo mes the Daily will be payable to Applicable Benefit condionsmenonedinPara11A)andexclusionsmenonedinPara15below. During one period of 24 connuous hours (i.e. one day) of Hospitalisaon (aer having completed the 24 hours as above), if the said Hospitalisaon included stay inanIntensiveCareUnitaswellasinanyotherin-paent(non-IntensiveCareUnit)' - 'emergenciessha eryourpeaceofmind. LIC''sJeevanArogyagivesyou: • Valuablefinancialproteconincaseofhospitalisaon,surgeryetc • IncreasingHealthcovereveryyear • Lumpsumbenefitirrespecveofactualmedicalcosts • Noclaimbenefit • Flexiblebenefitlimittochoosefrom • Flexiblepremiumpaymentopons • Veryeasytochooseyourplan Step 1 2 Step Choose the level of Health cover you need Work out the premium payable along with our Representave Step 1: Choose the level of Health cover you need: You can choose the amount of Inial Daily Benefit (i.e. the daily Hospital Cash Benefit applicableinthefirstyearofthepolicy)asperyourneedfromoutofthefollowingchoices: ` 1000 per day ` 2000 per day ` 3000 per day ` 4000 per day' - 'provided any such part exceeds a connuous period of 4 hours (aer having stay completed the 24 hours as above) in a non-ICU ward/room of a hospital, an amount equal to the Applicable Daily Benefit (ADB) available under the policy during that policy year shall be payable subject to benefit limits and condions menonedinPara11A)andexclusionsmenonedinPara15below. During the first of cover commencement in respect of each insured, the year ApplicableDailyBenefitshallbetheInialDailyBenefitamountchosenbyyouand menonedinthepolicySchedule. Theamountof DBforeachpolicyyear,aerthefirstpolicyyear,shallconsistof2parts: A An arithmec addion of an amount equal to 5% (five percent) of the Inial Daily' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.5454545454545454 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7727272727272727 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9090909090909091 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5454545454545454 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2575757575757575 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18181818181818185 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000003 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5454545454545454 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7727272727272727 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9090909090909091 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.773062927015556 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7011363636363636 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7011363636363636 name: Cosine Map@100 --- # SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Supported Modality:** Text ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'NomicBertModel'}) (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'How is the Initial Daily Benefit (the Applicable Daily Benefit for the first policy year) determined and stated in the policy schedule?', 'provided any such part\nexceeds a connuous period of 4 hours (aer having\nstay\ncompleted the 24 hours as above) in a non-ICU ward/room of a hospital, an\namount equal to the Applicable Daily Benefit (ADB) available under the policy\nduring that policy year shall be payable subject to benefit limits and condions\nmenonedinPara11A)andexclusionsmenonedinPara15below.\nDuring the first\nof cover commencement in respect of each insured, the\nyear\nApplicableDailyBenefitshallbetheInialDailyBenefitamountchosenbyyouand\nmenonedinthepolicySchedule.\nTheamountof DBforeachpolicyyear,aerthefirstpolicyyear,shallconsistof2parts:\nA\n\nAn arithmec addion of an amount equal to 5% (five percent) of the Inial Daily', 'Periodwithoutanymaximumlimit.\nFor members\nsubsequently under the policy, the benefit in the first year\nincluded\nshall be equal to Inial Daily Benefit amount and thereaer the Applicable Daily\nBenefitshallincreaseasabove.\nIfanyofthememberinsuredisrequiredtostayinanIntensiveCareUnitofahospital,\nt\nsubject\nbenefit limits and\nwo mes the\nDaily\nwill be payable\nto\nApplicable\nBenefit\ncondionsmenonedinPara11A)andexclusionsmenonedinPara15below.\nDuring one period of 24 connuous hours (i.e. one day) of Hospitalisaon (aer\nhaving completed the 24 hours as above), if the said Hospitalisaon included stay\ninanIntensiveCareUnitaswellasinanyotherin-paent(non-IntensiveCareUnit)', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.6203, 0.6283], # [0.6203, 1.0000, 0.8679], # [0.6283, 0.8679, 1.0000]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.5455 | | cosine_accuracy@3 | 0.7727 | | cosine_accuracy@5 | 0.9091 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.5455 | | cosine_precision@3 | 0.2576 | | cosine_precision@5 | 0.1818 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.5455 | | cosine_recall@3 | 0.7727 | | cosine_recall@5 | 0.9091 | | cosine_recall@10 | 1.0 | | **cosine_ndcg@10** | **0.7731** | | cosine_mrr@10 | 0.7011 | | cosine_map@100 | 0.7011 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 20 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 20 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * 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 are | | What 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
` 2000 per day
` 3000 per day
` 4000 per day | | If 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
` 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 | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `num_train_epochs`: 5 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `do_predict`: False - `prediction_loss_only`: True - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: None - `warmup_ratio`: None - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `enable_jit_checkpoint`: False - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `use_cpu`: False - `seed`: 42 - `data_seed`: None - `bf16`: False - `fp16`: False - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: -1 - `ddp_backend`: None - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `parallelism_config`: None - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `group_by_length`: False - `length_column_name`: length - `project`: huggingface - `trackio_space_id`: trackio - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `auto_find_batch_size`: False - `full_determinism`: False - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_num_input_tokens_seen`: no - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: True - `use_cache`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | cosine_ndcg@10 | |:-----:|:----:|:--------------:| | 1.0 | 2 | 0.7731 | ### Training Time - **Training**: 1.8 minutes ### Framework Versions - Python: 3.12.13 - Sentence Transformers: 5.4.1 - Transformers: 5.0.0 - PyTorch: 2.10.0+cpu - Accelerate: 1.13.0 - Datasets: 4.8.5 - Tokenizers: 0.22.2 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{oord2019representationlearningcontrastivepredictive, title={Representation Learning with Contrastive Predictive Coding}, author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, year={2019}, eprint={1807.03748}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/1807.03748}, } ```