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---
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:1441500
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
base_model: Shuu12121/CodeModernBERT-Finch
widget:
- text: "public static TaggableReadPreference secondary(final TagSet tagSet,\n \
\ final long maxStaleness, final\
\ TimeUnit timeUnit) {\n return new SecondaryReadPreference(singletonList(tagSet),\
\ maxStaleness, timeUnit);\n }"
- text: '// DoTimeout performs the given request and waits for response during
// the given timeout duration.
//
// Request must contain at least non-zero RequestURI with full url (including
// scheme and host) or non-zero Host header + RequestURI.
//
// Client determines the server to be requested in the following order:
//
// - from RequestURI if it contains full url with scheme and host;
// - from Host header otherwise.
//
// The function doesn''t follow redirects. Use Get* for following redirects.
//
// Response is ignored if resp is nil.
//
// ErrTimeout is returned if the response wasn''t returned during
// the given timeout.
//
// ErrNoFreeConns is returned if all Client.MaxConnsPerHost connections
// to the requested host are busy.
//
// It is recommended obtaining req and resp via AcquireRequest
// and AcquireResponse in performance-critical code.
//
// Warning: DoTimeout does not terminate the request itself. The request will
// continue in the background and the response will be discarded.
// If requests take too long and the connection pool gets filled up please
// try setting a ReadTimeout.'
- text: "func (c *Compressor) selectEncoder(h http.Header, w io.Writer) (io.Writer,\
\ string, func()) {\n\theader := h.Get(\"Accept-Encoding\")\n\n\t// Parse the\
\ names of all accepted algorithms from the header.\n\taccepted := strings.Split(strings.ToLower(header),\
\ \",\")\n\n\t// Find supported encoder by accepted list by precedence\n\tfor\
\ _, name := range c.encodingPrecedence {\n\t\tif matchAcceptEncoding(accepted,\
\ name) {\n\t\t\tif pool, ok := c.pooledEncoders[name]; ok {\n\t\t\t\tencoder\
\ := pool.Get().(ioResetterWriter)\n\t\t\t\tcleanup := func() {\n\t\t\t\t\tpool.Put(encoder)\n\
\t\t\t\t}\n\t\t\t\tencoder.Reset(w)\n\t\t\t\treturn encoder, name, cleanup\n\n\
\t\t\t}\n\t\t\tif fn, ok := c.encoders[name]; ok {\n\t\t\t\treturn fn(w, c.level),\
\ name, func() {}\n\t\t\t}\n\t\t}\n\n\t}\n\n\t// No encoder found to match the\
\ accepted encoding\n\treturn nil, \"\", func() {}\n}"
- text: 'Parse the template file and return it as string
@param array $arrAttributes An optional attributes array
@return string The template markup'
- text: "function seed_mix() {\n a ^= b << 11; d = add(d, a); b = add(b, c);\n\
\ b ^= c >>> 2; e = add(e, b); c = add(c, d);\n c ^= d << 8; f =\
\ add(f, c); d = add(d, e);\n d ^= e >>> 16; g = add(g, d); e = add(e, f);\n\
\ e ^= f << 10; h = add(h, e); f = add(f, g);\n f ^= g >>> 4; a =\
\ add(a, f); g = add(g, h);\n g ^= h << 8; b = add(b, g); h = add(h, a);\n\
\ h ^= a >>> 9; c = add(c, h); a = add(a, b);\n }"
pipeline_tag: feature-extraction
library_name: sentence-transformers
---
# SPLADE Sparse Encoder
This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [Shuu12121/CodeModernBERT-Finch](https://huggingface.co/Shuu12121/CodeModernBERT-Finch) using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30005-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** SPLADE Sparse Encoder
- **Base model:** [Shuu12121/CodeModernBERT-Finch](https://huggingface.co/Shuu12121/CodeModernBERT-Finch) <!-- at revision 8159a3905097a6cea798bb86d548caed9c1ad37d -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 30005 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': 8192, 'do_lower_case': False, 'architecture': 'ModernBertForMaskedLM'})
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30005})
)
```
## 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("sparse_encoder_model_id")
# Run inference
sentences = [
'Will detect inputs that begin with @MyNamespace/... and replace the namespace with the corresponding path.\n\n@see \\Assetic\\Factory\\AssetFactory::parseInput()',
'protected function parseInput($input, array $options = array())\n {\n $matches = null;\n // search for @MyNamespace/path/to/asset\n if (preg_match("|^\\@([a-z_][_a-z0-9]*)/|i", $input, $matches)) {\n $ns = $matches[1];\n if (!array_key_exists($ns, $this->namespaces)) {\n throw new \\RuntimeException("$ns : unknown namespace !");\n }\n $input = $this->namespaces[$ns] . substr($input, strlen($ns) + 1);\n }\n return parent::parseInput($input, $options);\n }',
'function seed_mix() {\n a ^= b << 11; d = add(d, a); b = add(b, c);\n b ^= c >>> 2; e = add(e, b); c = add(c, d);\n c ^= d << 8; f = add(f, c); d = add(d, e);\n d ^= e >>> 16; g = add(g, d); e = add(e, f);\n e ^= f << 10; h = add(h, e); f = add(f, g);\n f ^= g >>> 4; a = add(a, f); g = add(g, h);\n g ^= h << 8; b = add(b, g); h = add(h, a);\n h ^= a >>> 9; c = add(c, h); a = add(a, b);\n }',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 30005]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[26.3028, 23.1010, 3.4799],
# [23.1010, 42.4588, 6.9869],
# [ 3.4799, 6.9869, 59.2962]])
```
<!--
### 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.*
-->
<!--
## 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
#### Unnamed Dataset
* Size: 1,441,500 training samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | text1 | text2 | label |
|:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 49.63 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 180.64 tokens</li><li>max: 6082 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
| text1 | text2 | label |
|:----------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>// makeWin32File makes a new win32File from an existing file handle</code> | <code>func makeWin32File(h syscall.Handle) (*win32File, error) {<br> f := &win32File{handle: h}<br> ioInitOnce.Do(initIo)<br> _, err := createIoCompletionPort(h, ioCompletionPort, 0, 0xffffffff)<br> if err != nil {<br> return nil, err<br> }<br> err = setFileCompletionNotificationModes(h, cFILE_SKIP_COMPLETION_PORT_ON_SUCCESS|cFILE_SKIP_SET_EVENT_ON_HANDLE)<br> if err != nil {<br> return nil, err<br> }<br> f.readDeadline.channel = make(timeoutChan)<br> f.writeDeadline.channel = make(timeoutChan)<br> return f, nil<br>}</code> | <code>1.0</code> |
| <code>// Convert_v1_FlexPersistentVolumeSource_To_core_FlexPersistentVolumeSource is an autogenerated conversion function.</code> | <code>func Convert_v1_FlexPersistentVolumeSource_To_core_FlexPersistentVolumeSource(in *v1.FlexPersistentVolumeSource, out *core.FlexPersistentVolumeSource, s conversion.Scope) error {<br> return autoConvert_v1_FlexPersistentVolumeSource_To_core_FlexPersistentVolumeSource(in, out, s)<br>}</code> | <code>1.0</code> |
| <code>// AddRunCmd is defined on the RunCmdsConfig interface.</code> | <code>func (cfg *cloudConfig) AddRunCmd(args ...string) {<br> cfg.attrs["runcmd"] = append(cfg.RunCmds(), strings.Join(args, " "))<br>}</code> | <code>1.0</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"document_regularizer_weight": 3e-05,
"query_regularizer_weight": 5e-05
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 6,000 evaluation samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | text1 | text2 | label |
|:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 45.53 tokens</li><li>max: 495 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 183.92 tokens</li><li>max: 7677 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
| text1 | text2 | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>// establish data storage, format and dimensions of a renderbuffer object's image</code> | <code>func RenderbufferStorage(target uint32, internalformat uint32, width int32, height int32) {<br> syscall.Syscall6(gpRenderbufferStorage, 4, uintptr(target), uintptr(internalformat), uintptr(width), uintptr(height), 0, 0)<br>}</code> | <code>1.0</code> |
| <code>// GetObject is a wrapper around gtk_builder_get_object(). The returned result<br>// is an IObject, so it will need to be type-asserted to the appropriate type before<br>// being used. For example, to get an object and type assert it as a window:<br>//<br>// obj, err := builder.GetObject("window")<br>// if err != nil {<br>// // object not found<br>// return<br>// }<br>// if w, ok := obj.(*gtk.Window); ok {<br>// // do stuff with w here<br>// } else {<br>// // not a *gtk.Window<br>// }<br>//</code> | <code>func (b *Builder) GetObject(name string) (glib.IObject, error) {<br> cstr := C.CString(name)<br> defer C.free(unsafe.Pointer(cstr))<br> c := C.gtk_builder_get_object(b.native(), (*C.gchar)(cstr))<br> if c == nil {<br> return nil, errors.New("object '" + name + "' not found")<br> }<br> obj, err := cast(c)<br> if err != nil {<br> return nil, err<br> }<br> return obj, nil<br>}</code> | <code>1.0</code> |
| <code>// augmentGoroutine processes source files to improve call to be more<br>// descriptive.<br>//<br>// It modifies the routine.</code> | <code>func (c *cache) augmentGoroutine(goroutine *Goroutine) {<br> if c.files == nil {<br> c.files = map[string][]byte{}<br> }<br> if c.parsed == nil {<br> c.parsed = map[string]*parsedFile{}<br> }<br> // For each call site, look at the next call and populate it. Then we can<br> // walk back and reformat things.<br> for i := range goroutine.Stack.Calls {<br> c.load(goroutine.Stack.Calls[i].LocalSrcPath)<br> }<br><br> // Once all loaded, we can look at the next call when available.<br> for i := 0; i < len(goroutine.Stack.Calls)-1; i++ {<br> // Get the AST from the previous call and process the call line with it.<br> if f := c.getFuncAST(&goroutine.Stack.Calls[i]); f != nil {<br> processCall(&goroutine.Stack.Calls[i], f)<br> }<br> }<br>}</code> | <code>1.0</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"document_regularizer_weight": 3e-05,
"query_regularizer_weight": 5e-05
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 2
- `gradient_accumulation_steps`: 25
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 2
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 25
- `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`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `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
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `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 |
|:------:|:-----:|:-------------:|
| 0.0173 | 500 | 252.5855 |
| 0.0347 | 1000 | 0.4281 |
| 0.0520 | 1500 | 0.071 |
| 0.0694 | 2000 | 0.0579 |
| 0.0867 | 2500 | 0.04 |
| 0.1041 | 3000 | 0.0422 |
| 0.1214 | 3500 | 0.041 |
| 0.1387 | 4000 | 0.0347 |
| 0.1561 | 4500 | 0.0341 |
| 0.1734 | 5000 | 0.0288 |
| 0.1908 | 5500 | 0.0243 |
| 0.2081 | 6000 | 0.0249 |
| 0.2255 | 6500 | 0.0242 |
| 0.2428 | 7000 | 0.0204 |
| 0.2601 | 7500 | 0.0206 |
| 0.2775 | 8000 | 0.0198 |
| 0.2948 | 8500 | 0.0205 |
| 0.3122 | 9000 | 0.0176 |
| 0.3295 | 9500 | 0.0207 |
| 0.3469 | 10000 | 0.0196 |
| 0.3642 | 10500 | 0.0132 |
| 0.3815 | 11000 | 0.016 |
| 0.3989 | 11500 | 0.0151 |
| 0.4162 | 12000 | 0.0168 |
| 0.4336 | 12500 | 0.0161 |
| 0.4509 | 13000 | 0.0156 |
| 0.4683 | 13500 | 0.0134 |
| 0.4856 | 14000 | 0.0156 |
| 0.5029 | 14500 | 0.0138 |
| 0.5203 | 15000 | 0.0134 |
| 0.5376 | 15500 | 0.0146 |
| 0.5550 | 16000 | 0.0153 |
| 0.5723 | 16500 | 0.0135 |
| 0.5897 | 17000 | 0.0136 |
| 0.6070 | 17500 | 0.0122 |
| 0.6243 | 18000 | 0.0115 |
| 0.6417 | 18500 | 0.0132 |
| 0.6590 | 19000 | 0.0101 |
| 0.6764 | 19500 | 0.0092 |
| 0.6937 | 20000 | 0.0117 |
| 0.7111 | 20500 | 0.0098 |
| 0.7284 | 21000 | 0.0122 |
| 0.7458 | 21500 | 0.0102 |
| 0.7631 | 22000 | 0.0088 |
| 0.7804 | 22500 | 0.0093 |
| 0.7978 | 23000 | 0.0101 |
| 0.8151 | 23500 | 0.0083 |
| 0.8325 | 24000 | 0.0095 |
| 0.8498 | 24500 | 0.0081 |
| 0.8672 | 25000 | 0.0095 |
| 0.8845 | 25500 | 0.009 |
| 0.9018 | 26000 | 0.0081 |
| 0.9192 | 26500 | 0.0065 |
| 0.9365 | 27000 | 0.009 |
| 0.9539 | 27500 | 0.0075 |
| 0.9712 | 28000 | 0.0078 |
| 0.9886 | 28500 | 0.0094 |
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 5.0.0
- Transformers: 4.53.1
- PyTorch: 2.6.0+cu124
- Accelerate: 1.8.1
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## 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",
}
```
#### 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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## Glossary
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