README.md

---
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:800000
- loss:SpladeLoss
- loss:SparseMarginMSELoss
- loss:FlopsLoss
base_model:
- prajjwal1/bert-small
widget:
- text: leagues, define
- text: >-
    WATCH HOW YOU WANT. STARZ lets you stream hit original series and movies on
    your favorite devices. Plus you can get the STARZ app on your smartphone or
    tablet and download full movies and shows to watch off-line, anytime,
    anywhere. START YOUR FREE TRIAL NOW.
- text: >-
    Furthermore, priority must be given to national jurisdiction. Pointing out
    that States applied universal jurisdiction differently, he expressed concern
    at the abuse of its application by some national courts, which rendered it a
    source of international conflict.
- text: >-
    My sil tells me that my mil cooked the eggplant at high heat for a very long
    time until it was almost burned. Is it possible that cooking it in such a
    way gets rid of the bitterness? My mil bought her eggplants at the chain
    grocery store- so this is not a freshness issue. Thanks for any ideas.
- text: how many tablespoons of garlic powder are in an ounce
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
model-index:
- name: SPLADE Sparse Encoder
  results:
  - task:
      type: sparse-information-retrieval
      name: Sparse Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: dot_accuracy@1
      value: 0.45475
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.7685
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.8785833333333334
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.9484166666666667
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.45475
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.2634444444444444
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.18283333333333338
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.09981666666666668
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.43995833333333334
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.754263888888889
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.867825
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.942448611111111
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.7030603243631471
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.6287952050264416
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.625266158775858
      name: Dot Map@100
    - type: query_active_dims
      value: 24.914167404174805
      name: Query Active Dims
    - type: query_sparsity_ratio
      value: 0.9991837308366367
      name: Query Sparsity Ratio
    - type: corpus_active_dims
      value: 171.85920667832903
      name: Corpus Active Dims
    - type: corpus_sparsity_ratio
      value: 0.9943693333766356
      name: Corpus Sparsity Ratio
datasets:
- microsoft/ms_marco
language:
- en
---

# SPLADE-Small

This is a SPLADE sparse retrieval model based on BERT-Small (29M) that was trained by distilling a Cross-Encoder on the MSMARCO dataset. The cross-encoder used was [ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2). 

This SPLADE model is `2x` smaller than Naver's official `splade-v3-distilbert` while having `91%` of it's performance on the MSMARCO benchmark. This model is small enough to be used without a GPU on a dataset of a few thousand documents. 

- `Collection:` https://huggingface.co/collections/rasyosef/splade-tiny-msmarco-687c548c0691d95babf65b70
- `Distillation Dataset:` https://huggingface.co/datasets/yosefw/msmarco-train-distil-v2
- `Code:` https://github.com/rasyosef/splade-tiny-msmarco

## Performance

The splade models were evaluated on 55 thousand queries and 8.84 million documents from the [MSMARCO](https://huggingface.co/datasets/microsoft/ms_marco) dataset.

||Size (# Params)|MRR@10 (MS MARCO dev)|
|:---|:----|:-------------------|
|`BM25`|-|18.0|-|-|
|`rasyosef/splade-tiny`|4.4M|30.9|
|`rasyosef/splade-mini`|11.2M|34.1|
|`rasyosef/splade-small`|28.8M|35.4|
|`naver/splade-v3-distilbert`|67.0M|38.7|


## 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 SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("rasyosef/splade-small")
# Run inference
queries = [
    "how many tablespoons of garlic powder are in an ounce",
]
documents = [
    '1 Fluid Ounce (fl oz) = 2 tablespoons   16 Tablespoons = 1 cup   16 Fluid Ounce (fl oz) = 2 cup. two ! 9.7 grams of garlic powder will be present in a tablespoon. 1 dry ounce is between 2 and 2.38 tablespoons, 16 tablespoons is incorrect. --------------------- 16 tablespoons per dry ounce. It is approximately 1/2 ounce. Usually 1/4 to 1/2 tsp.',
    'Spices, garlic powder weigh(s) 164 gram per (metric cup) or 5.47 ounce per (US cup)',
    'How many teaspoons of garlic powder equal a clove of garlic? Weigh the garlic clove and then weigh the garlic powder to make sure it is the same weight. That is how much powder equals a clove of garlic.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[26.3104, 20.4381, 15.5539]])
```

## Model Details

### Model Description
- **Model Type:** SPLADE Sparse Encoder
- **Base model:** [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 30522 dimensions
- **Similarity Function:** Dot Product
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)

### Full Model Architecture

```
SparseEncoder(
  (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertForMaskedLM'})
  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
```

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<details><summary>Click to expand</summary>

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## More
<details><summary>Click to expand</summary>

## Evaluation

### Metrics

#### Sparse Information Retrieval

* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)

| Metric                | Value      |
|:----------------------|:-----------|
| dot_accuracy@1        | 0.4547     |
| dot_accuracy@3        | 0.7685     |
| dot_accuracy@5        | 0.8786     |
| dot_accuracy@10       | 0.9484     |
| dot_precision@1       | 0.4547     |
| dot_precision@3       | 0.2634     |
| dot_precision@5       | 0.1828     |
| dot_precision@10      | 0.0998     |
| dot_recall@1          | 0.44       |
| dot_recall@3          | 0.7543     |
| dot_recall@5          | 0.8678     |
| dot_recall@10         | 0.9424     |
| **dot_ndcg@10**       | **0.7031** |
| dot_mrr@10            | 0.6288     |
| dot_map@100           | 0.6253     |
| query_active_dims     | 24.9142    |
| query_sparsity_ratio  | 0.9992     |
| corpus_active_dims    | 171.8592   |
| corpus_sparsity_ratio | 0.9944     |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 800,000 training samples
* Columns: <code>query</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                            | positive                                                                            | negative_1                                                                          | negative_2                                                                          | negative_3                                                                          | negative_4                                                                          | label                              |
  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------|
  | type    | string                                                                           | string                                                                              | string                                                                              | string                                                                              | string                                                                              | string                                                                              | list                               |
  | details | <ul><li>min: 4 tokens</li><li>mean: 8.96 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 79.51 tokens</li><li>max: 230 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 78.09 tokens</li><li>max: 203 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 77.84 tokens</li><li>max: 215 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 76.65 tokens</li><li>max: 249 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 74.67 tokens</li><li>max: 227 tokens</li></ul> | <ul><li>size: 4 elements</li></ul> |
* Samples:
  | query                                         | positive                                                                                                                                                                                                                                                                                                                                                                                                                                             | negative_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | negative_2                                                                                                                                                                                                                                                                                                                                                                                                         | negative_3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | negative_4                                                                                                                                                                                                                                                                                        | label                                                                        |
  |:----------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|
  | <code>who was president during detente</code> | <code>Détente ended after the Soviet intervention in Afghanistan, which led to the United States boycott of the 1980 Olympics in Moscow. Ronald Reagan's election as president in 1980, based in large part on an anti-détente campaign, marked the close of détente and a return to Cold War tensions.</code>                                                                                                                                       | <code>Soviet Premier Alexei Kosygin (front) next to U.S. President Lyndon B. Johnson (behind) during the Glassboro Summit Conference The most obvious manifestation of détente was the series of summits held between the leaders of the two superpowers and the treaties that resulted from these meetings.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                       | <code>The activities of President Ronald Reagan returned tensions to a fever pitch. Soviet relations with the People's Republic of China Détente could probably not have taken place, and certainly wouldn't have assumed the form that it did, without the rift that developed between the world's two primary communist regimes, the Soviet Union and the People's Republic of China (PRC).</code>               | <code>Détente is the easing of strained relations, especially in a political situation. The term originates in the time of the Triple Entente and Entente cordiale in reference to an easing of tensions between England and France who, subsequent to being commingled polities under Norman rule, were warring rivals for the better part of a millennium but pursuant to a policy of détente became enduring allies. In the context of the Cold War, the lessening of tensions between the East and West, along ...</code> | <code>Détente (French pronunciation: ​[detɑ̃t], meaning relaxation)[1] is the easing of strained relations, especially in political situation.</code>                                                                                                                                             | <code>[1.0, 2.1879749298095703, 8.371654510498047, 10.16702938079834]</code> |
  | <code>what is an ftp file</code>              | <code>File Transfer Protocol (FTP) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. FTP is a client-server protocol that relies on two communications channels between client and server: a command channel for controlling the conversation and a data channel for transmitting file content. Clients initiate conversations with servers by requesting to download a file.</code> | <code>The FTP (File Transfer Protocol) utility program is commonly used for copying files to and from other computers. These computers may be at the same site or at different sites thousands of miles apart. FTP is a general protocol that works on UNIX systems as well as a variety of other (non-UNIX) systems.</code>                                                                                                                                                                                                                                                                                                                                                                                                                              | <code>To transfer files via File Transfer Protocol (FTP), you need to establish an FTP connection. To make an FTP connection you can use a standard Web browser (Internet Explorer, Mozilla Firefox, etc.) or an FTP Client. To transfer a file with FTP you need to have an FTP accounts for the web space you are going to transfer the file to. FTP hosting account where you plan to upload your files.</code> | <code>The Difference Between FTP Servers and File Servers. When two terms are similar and even describe a similar concept, people have a tendency to start using them interchangeably. This is definitely true in the case of FTP servers and file servers, which sound like they accomplish the same goal but in reality are two very different animals altogether.</code>                                                                                                                                                   | <code>The command-line secure file transfer program (sftp) and graphical SFTP clients, such as WinSCP and Fetch, use SSH2 encryption to authenticate and establish secure channels between networked hosts.</code>                                                                                | <code>[1.0, 5.810799598693848, 7.961757183074951, 18.629709243774414]</code> |
  | <code>what causes a t wave abnormality</code> | <code>T –wave abnormalities may not necessarily indicate the presence of a severe heart condition. There are non-specific wave changes that result from common, non-specific causes of T-wave abnormality which includes the following: 1  No obvious causes, which are usually associated with women.  Fever.</code>                                                                                                                                | <code>5 Causes Of T-Wave Abnormality. T wave is basically the diagrammatically representation of ventricular polarization called electrocardiography. The structure of a T wave is like slight inverted upward stroke that follow a peak generated by R and S waves. One heart beat is represented in form of Q, R, S and T wave. The abnormality in T waves may be indicated by longer, flatter or higher peaks in the diagram. The measurement of heart beat in such a way makes it possible to diagnose heart related problems easily. If you are suffering from any heart related disease, the electrocardiogram will show an abnormality in the T wave. In this write up we will discuss various causes of abnormality in T wave measurement.</code> | <code>Specific states or conditions that cause T-wave abnormality. Complete inversions can signify the presence of cardiovascular diseases and other serious complications, which include the following: Ischemia is a condition in which oxygenated blood becomes constrained in a certain body part.</code>                                                                                                      | <code>T Wave Abnormalities. christine m. smith. I just received a copy of an ECG I had done in Sept 1998. The preliminary report was borderline ECG and the final interpretation was Abnormal ECG. There was normal sinus rhythm and Nonspecific Anterior T abnormalities. When compared to an ECG taken in 1986 there was minimal T wave change. I have been told by many that abnormalities like this are usually no problem.</code>                                                                                        | <code>Prolonged Q-T interval. Long QT syndrome is a heart rhythm disorder that can cause serious irregular heart rhythms (arrhythmias). In a normal heart, your heart circulates blood throughout your body during each heartbeat. Your heart's chambers contract and relax to pump blood.</code> | <code>[1.0, 1.0, 5.3564229011535645, 11.585516929626465]</code>              |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
  ```json
  {
      "loss": "SparseMarginMSELoss",
      "document_regularizer_weight": 0.12,
      "query_regularizer_weight": 0.2
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 48
- `per_device_eval_batch_size`: 48
- `learning_rate`: 4e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.025
- `fp16`: True
- `load_best_model_at_end`: True
- `push_to_hub`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 48
- `per_device_eval_batch_size`: 48
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 4e-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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.025
- `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`: True
- `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`: False
- `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`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch | Step  | Training Loss | dot_ndcg@10 |
|:-----:|:-----:|:-------------:|:-----------:|
| 1.0   | 16667 | 8.363         | 0.6961      |
| 2.0   | 33334 | 6.5021        | 0.7031      |
| 3.0   | 50001 | 5.2209        | 0.7031      |


### Framework Versions
- Python: 3.12.11
- Sentence Transformers: 5.1.0
- Transformers: 4.55.3
- PyTorch: 2.8.0+cu126
- Accelerate: 1.10.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4

## 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},
}
```

#### SparseMarginMSELoss
```bibtex
@misc{hofstätter2021improving,
    title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
    author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
    year={2021},
    eprint={2010.02666},
    archivePrefix={arXiv},
    primaryClass={cs.IR}
}
```

#### 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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</details>