| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - dense |
| | - generated_from_trainer |
| | - dataset_size:102127 |
| | - loss:SpladeLoss |
| | - loss:SparseMultipleNegativesRankingLoss |
| | - loss:FlopsLoss |
| | base_model: seregadgl/splade_gemma_google_base_checkpoint_100_clear |
| | widget: |
| | - source_sentence: 'query: 6460338 acdelco' |
| | sentences: |
| | - 'document: очиститель тормозов rsqprofessional арт 072589767pl volkswagen id buzz |
| | янтарный' |
| | - 'document: гтц 6460338 для chevrolet traverse' |
| | - 'document: гтц 6960358 для chevrolet traverse' |
| | - source_sentence: 'query: audioquest cinnamon usb 0 7500 см ' |
| | sentences: |
| | - 'document: кабель usb аудиоквест cinnamon 0 7500 см 8712516' |
| | - 'document: задняя камера рамке номерного знака интерпауэр ip616 54785862' |
| | - 'document: аудиокабель soundwave 200 см' |
| | - source_sentence: 'query: акустическое пианино weber w 121 pw ' |
| | sentences: |
| | - 'document: акустическое пианино steinway model s' |
| | - 'document: инструмент для игры на пианино вебер w 121 pw' |
| | - 'document: велосипед сильвербек strela sport 700c 54 см blue 60097000435025' |
| | - source_sentence: 'query: шкаф шрм24' |
| | sentences: |
| | - 'document: wardrobe shrm 24 4348563' |
| | - 'document: духовой шкаф бертаццони f6011provtn' |
| | - 'document: шкаф мдф30' |
| | - source_sentence: 'query: 1452634 santool jawa 300 cl' |
| | sentences: |
| | - 'document: смартфон эппл iphone xs max 512gb' |
| | - 'document: 1453934 santool съемник для сальников jawa 300 cl' |
| | - 'document: 1452634 santool съемник для сальников jawa 300 cl' |
| | datasets: |
| | - seregadgl/car_and_product_triplet_103k |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | metrics: |
| | - cosine_accuracy@1 |
| | - 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 seregadgl/splade_gemma_google_base_checkpoint_100_clear |
| | results: |
| | - task: |
| | type: information-retrieval |
| | name: Information Retrieval |
| | dataset: |
| | name: val set fine |
| | type: val_set_fine |
| | metrics: |
| | - type: cosine_accuracy@1 |
| | value: 0.742 |
| | name: Cosine Accuracy@1 |
| | - type: cosine_precision@1 |
| | value: 0.742 |
| | name: Cosine Precision@1 |
| | - type: cosine_precision@3 |
| | value: 0.27633333333333326 |
| | name: Cosine Precision@3 |
| | - type: cosine_precision@5 |
| | value: 0.1728 |
| | name: Cosine Precision@5 |
| | - type: cosine_precision@10 |
| | value: 0.08910000000000001 |
| | name: Cosine Precision@10 |
| | - type: cosine_recall@1 |
| | value: 0.742 |
| | name: Cosine Recall@1 |
| | - type: cosine_recall@3 |
| | value: 0.829 |
| | name: Cosine Recall@3 |
| | - type: cosine_recall@5 |
| | value: 0.864 |
| | name: Cosine Recall@5 |
| | - type: cosine_recall@10 |
| | value: 0.891 |
| | name: Cosine Recall@10 |
| | - type: cosine_ndcg@10 |
| | value: 0.8160719769563038 |
| | name: Cosine Ndcg@10 |
| | - type: cosine_mrr@10 |
| | value: 0.7919432539682544 |
| | name: Cosine Mrr@10 |
| | - type: cosine_map@100 |
| | value: 0.7955622385483846 |
| | name: Cosine Map@100 |
| | --- |
| | |
| | # SentenceTransformer based on seregadgl/splade_gemma_google_base_checkpoint_100_clear |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [seregadgl/splade_gemma_google_base_checkpoint_100_clear](https://huggingface.co/seregadgl/splade_gemma_google_base_checkpoint_100_clear) on the [car_and_product_triplet_103k](https://huggingface.co/datasets/seregadgl/car_and_product_triplet_103k) 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:** [seregadgl/splade_gemma_google_base_checkpoint_100_clear](https://huggingface.co/seregadgl/splade_gemma_google_base_checkpoint_100_clear) <!-- at revision 20c38a098901bc44c1031a7537d0e3bf0aa93063 --> |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Output Dimensionality:** 768 dimensions |
| | - **Similarity Function:** Cosine Similarity |
| | - **Training Dataset:** |
| | - [car_and_product_triplet_103k](https://huggingface.co/datasets/seregadgl/car_and_product_triplet_103k) |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **License:** Unknown --> |
| |
|
| | ### 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({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'}) |
| | (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): SparseLayer( |
| | (linear): Linear(in_features=768, out_features=262144, bias=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("seregadgl/splade_gemma_google_base_checkpoint_100_ver2") |
| | # Run inference |
| | sentences = [ |
| | 'query: 1452634 santool jawa 300 cl', |
| | 'document: 1452634 santool съемник для сальников jawa 300 cl', |
| | 'document: 1453934 santool съемник для сальников jawa 300 cl', |
| | ] |
| | 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, 0.1443, 0.1452], |
| | # [0.1443, 1.0000, 0.7490], |
| | # [0.1452, 0.7490, 1.0000]]) |
| | ``` |
| |
|
| | <!-- |
| | ### 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 |
| |
|
| | * Dataset: `val_set_fine` |
| | * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
| |
|
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | cosine_accuracy@1 | 0.742 | |
| | | cosine_precision@1 | 0.742 | |
| | | cosine_precision@3 | 0.2763 | |
| | | cosine_precision@5 | 0.1728 | |
| | | cosine_precision@10 | 0.0891 | |
| | | cosine_recall@1 | 0.742 | |
| | | cosine_recall@3 | 0.829 | |
| | | cosine_recall@5 | 0.864 | |
| | | cosine_recall@10 | 0.891 | |
| | | **cosine_ndcg@10** | **0.8161** | |
| | | cosine_mrr@10 | 0.7919 | |
| | | cosine_map@100 | 0.7956 | |
| | |
| | <!-- |
| | ## 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 |
| | |
| | #### car_and_product_triplet_103k |
| | |
| | * Dataset: [car_and_product_triplet_103k](https://huggingface.co/datasets/seregadgl/car_and_product_triplet_103k) at [3519181](https://huggingface.co/datasets/seregadgl/car_and_product_triplet_103k/tree/35191818e272dc373544bd86903a5146c6f993e2) |
| | * Size: 102,127 training samples |
| | * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | anchor | positive | negative | |
| | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
| | | type | string | string | string | |
| | | details | <ul><li>min: 5 tokens</li><li>mean: 16.27 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 23.62 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 23.2 tokens</li><li>max: 47 tokens</li></ul> | |
| | * Samples: |
| | | anchor | positive | negative | |
| | |:--------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| |
| | | <code>query: погружной блендер tefal optichef hb64f810</code> | <code>document: погружной блендер тефаль optichef hb64f810</code> | <code>document: погружной миксер tefal mixchef hb64f850</code> | |
| | | <code>query: 375675836 niteo</code> | <code>document: тосол 375675836 для ford f350 полуночный синий</code> | <code>document: тосол 375625836 для ford f350 полуночный синий фиалковый</code> | |
| | | <code>query: накидка с подогревом dodge viper pink</code> | <code>document: накидка с подогревом acdelco арт 787327sx dodge viper розовый</code> | <code>document: 787327sx накидка с подогревом indian challenger лаймовый</code> | |
| | * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#spladeloss) with these parameters: |
| | ```json |
| | { |
| | "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)", |
| | "document_regularizer_weight": 1e-05, |
| | "query_regularizer_weight": 1e-05 |
| | } |
| | ``` |
| | |
| | ### Evaluation Dataset |
| | |
| | #### car_and_product_triplet_103k |
| | |
| | * Dataset: [car_and_product_triplet_103k](https://huggingface.co/datasets/seregadgl/car_and_product_triplet_103k) at [3519181](https://huggingface.co/datasets/seregadgl/car_and_product_triplet_103k/tree/35191818e272dc373544bd86903a5146c6f993e2) |
| | * Size: 1,000 evaluation samples |
| | * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | anchor | positive | negative | |
| | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | | type | string | string | string | |
| | | details | <ul><li>min: 5 tokens</li><li>mean: 16.73 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 23.54 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 22.66 tokens</li><li>max: 65 tokens</li></ul> | |
| | * Samples: |
| | | anchor | positive | negative | |
| | |:------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | | <code>query: зеркала для 'слепых' зон volkswagen arteon</code> | <code>document: зеркала для 'слепых' зон 86635985zz для volkswagen arteon перламутровочёрный</code> | <code>document: 86635985zz зеркала для 'слепых' зон иж юпитер2 голубой</code> | |
| | | <code>query: elf bar lux 1500 лимонад голубой малины 1500 </code> | <code>document: одноразовая электронная сигарета эльф бар 1 5000 мл lemonade blue raspberry 340440526</code> | <code>document: elf bar vibe 1000 мохито зелёного яблока 1000</code> | |
| | | <code>query: удалитель наклеек chevrolet corvette onyx</code> | <code>document: удалитель наклеек 20810588pl для chevrolet corvette оникс</code> | <code>document: удалитель наклеек 20810588pl для maserati levante янтарный</code> | |
| | * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#spladeloss) with these parameters: |
| | ```json |
| | { |
| | "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)", |
| | "document_regularizer_weight": 1e-05, |
| | "query_regularizer_weight": 1e-05 |
| | } |
| | ``` |
| | |
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| | |
| | - `eval_strategy`: steps |
| | - `gradient_accumulation_steps`: 16 |
| | - `learning_rate`: 0.0001 |
| | - `num_train_epochs`: 1 |
| | - `warmup_steps`: 10 |
| | - `fp16`: True |
| | - `load_best_model_at_end`: True |
| | - `router_mapping`: {'query': 'anchor', 'document': 'positive'} |
| |
|
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| |
|
| | - `overwrite_output_dir`: False |
| | - `do_predict`: False |
| | - `eval_strategy`: steps |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 8 |
| | - `per_device_eval_batch_size`: 8 |
| | - `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`: 0.0001 |
| | - `weight_decay`: 0.0 |
| | - `adam_beta1`: 0.9 |
| | - `adam_beta2`: 0.999 |
| | - `adam_epsilon`: 1e-08 |
| | - `max_grad_norm`: 1.0 |
| | - `num_train_epochs`: 1 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: linear |
| | - `lr_scheduler_kwargs`: {} |
| | - `warmup_ratio`: 0.0 |
| | - `warmup_steps`: 10 |
| | - `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 |
| | - `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} |
| | - `parallelism_config`: 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 |
| | - `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 |
| | - `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 |
| | - `hub_revision`: None |
| | - `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 |
| | - `include_tokens_per_second`: False |
| | - `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 |
| | - `prompts`: None |
| | - `batch_sampler`: batch_sampler |
| | - `multi_dataset_batch_sampler`: proportional |
| | - `router_mapping`: {'query': 'anchor', 'document': 'positive'} |
| | - `learning_rate_mapping`: {} |
| |
|
| | </details> |
| |
|
| | ### Training Logs |
| | | Epoch | Step | Validation Loss | val_set_fine_cosine_ndcg@10 | |
| | |:----------:|:------:|:---------------:|:---------------------------:| |
| | | 0.0125 | 10 | 0.8461 | 0.7841 | |
| | | 0.0251 | 20 | 0.8195 | 0.8009 | |
| | | 0.0376 | 30 | 0.7884 | 0.7967 | |
| | | 0.0501 | 40 | 0.7641 | 0.8097 | |
| | | **0.0627** | **50** | **0.7503** | **0.8146** | |
| | | 0.0752 | 60 | 0.7140 | 0.8151 | |
| | | 0.0877 | 70 | 0.7165 | 0.8180 | |
| | | 0.1003 | 80 | 0.6955 | 0.8131 | |
| | | 0.1128 | 90 | 0.6866 | 0.8157 | |
| | | 0.1253 | 100 | 0.6735 | 0.8170 | |
| | | 0.1379 | 110 | 0.6766 | 0.8159 | |
| | | 0.1504 | 120 | 0.6609 | 0.8161 | |
| |
|
| | * The bold row denotes the saved checkpoint. |
| |
|
| | ### Framework Versions |
| | - Python: 3.12.12 |
| | - Sentence Transformers: 5.2.2 |
| | - Transformers: 4.57.1 |
| | - PyTorch: 2.8.0+cu126 |
| | - Accelerate: 1.11.0 |
| | - Datasets: 4.4.2 |
| | - Tokenizers: 0.22.1 |
| |
|
| | ## 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", |
| | } |
| | ``` |
| |
|
| | #### SpladeLoss |
| | ```bibtex |
| | @misc{formal2022distillationhardnegativesampling, |
| | title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective}, |
| | author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant}, |
| | year={2022}, |
| | eprint={2205.04733}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.IR}, |
| | url={https://arxiv.org/abs/2205.04733}, |
| | } |
| | ``` |
| |
|
| | #### SparseMultipleNegativesRankingLoss |
| | ```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} |
| | } |
| | ``` |
| |
|
| | #### FlopsLoss |
| | ```bibtex |
| | @article{paria2020minimizing, |
| | title={Minimizing flops to learn efficient sparse representations}, |
| | author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s}, |
| | journal={arXiv preprint arXiv:2004.05665}, |
| | year={2020} |
| | } |
| | ``` |
| |
|
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