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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:10501
	- loss:CosineSimilarityLoss
	base_model: klue/roberta-base
	widget:
	- source_sentence: 이어 내년 4월부터 전자증명서는 건강보험자격확인서와 건강보험료 납부확인서 등 13종으로 늘어나고 사용처도 중앙부처는
	물론 은행과 보험사 등으로도 확대된다.
	sentences:
	- 4대 보험료 납부유예 및 감면조치는 4월에 납부해야 하는 3월 보험료부터 적용된다.
	- 그 외에는 모든 것에 만족했습니다.
	- 영하의 추운 날씨에는 장갑 잊지 말고 꼭 끼렴.
	- source_sentence: 야생동물 질병관리를 전담할 국가기관인 국립야생동물질병관리원이 올해 광주광역시 광산구 삼거동 일원에 개원 예정이다.
	sentences:
	- 위치는 좋으나 생활하기 좀 불편합니다.
	- 역에서 매우 가깝고, 쇼핑몰과 쇼핑몰 사이에는 숙소가 있습니다.
	- 추후 인도네시아와도 화상회의 및 온라인 세미나를 개최할 예정이다.
	- source_sentence: 작은 먹거리는 숙소 들어오게 전에 사는걸 추천해요.
	sentences:
	- 제일 최근에 스팸이 도착한 시간을 알려줘
	- 저는 당신이 숙소에 들어오기 전에 작은 음식을 사는 것을 추천합니다.
	- 올해는 황사 며칠동안 왔어?
	- source_sentence: 언제 만나는 것이 더 좋으실까요, 저녁 일곱시? 여덟시?
	sentences:
	- 이번주 일요일 약속 언제인지 궁금해.
	- 전자레인지와 가스레인지 중에 요리하고 싶은 걸로 알려줘
	- 뜨거운물말고 찬물로 세탁하고 더운물로 헹궈야될 것 같지 않아?
	- source_sentence: 지금까지 이탈리아 여행중에 가장 좋은 숙소였습니다
	sentences:
	- 지금까지 가본 호텔보다 더 좋은 숙소였습니다.
	- ‘코로나 아세안 대응기금’, ‘필수의료물품 비축제도’는 아세안+3가 함께 만들어낸 의미 있는 결과입니다.
	- 하루에 삼십분보단 한 시간 이상은 라디오 들어
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	co2_eq_emissions:
	emissions: 13.607209111220918
	energy_consumed: 0.0310949426904377
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: 12th Gen Intel(R) Core(TM) i5-12400
	ram_total_size: 31.784194946289062
	hours_used: 0.154
	hardware_used: 1 x NVIDIA GeForce RTX 3060
	model-index:
	- name: SentenceTransformer based on klue/roberta-base
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: pearson_cosine
	value: 0.34770715374416716
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.35560473197486514
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.3673847148331908
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.36460670798564826
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.36074518113660536
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.35482778401649034
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.21251176317804726
	name: Pearson Dot
	- type: spearman_dot
	value: 0.20063256899469895
	name: Spearman Dot
	- type: pearson_max
	value: 0.3673847148331908
	name: Pearson Max
	- type: spearman_max
	value: 0.36460670798564826
	name: Spearman Max
	- type: pearson_cosine
	value: 0.9591996448990093
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9206205258325634
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.9531423622288514
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.920406431818358
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.9532828644532834
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.9201721809761834
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.9482313505749467
	name: Pearson Dot
	- type: spearman_dot
	value: 0.9016036223997308
	name: Spearman Dot
	- type: pearson_max
	value: 0.9591996448990093
	name: Pearson Max
	- type: spearman_max
	value: 0.9206205258325634
	name: Spearman Max
	---

	# SentenceTransformer based on klue/roberta-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [klue/roberta-base](https://huggingface.co/klue/roberta-base). 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: [klue/roberta-base](https://huggingface.co/klue/roberta-base) <!-- at revision 02f94ba5e3fcb7e2a58a390b8639b0fac974a8da -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/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}) with Transformer model: RobertaModel
	(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})
	)
	```

	## 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 = [
	'지금까지 이탈리아 여행중에 가장 좋은 숙소였습니다',
	'지금까지 가본 호텔보다 더 좋은 숙소였습니다.',
	'‘코로나 아세안 대응기금’, ‘필수의료물품 비축제도’는 아세안+3가 함께 만들어낸 의미 있는 결과입니다.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

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

	</details>
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	## Evaluation

	### Metrics

	#### Semantic Similarity

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

	\| Metric \| Value \|
	\|:-------------------\|:-----------\|
	\| pearson_cosine \| 0.3477 \|
	\| spearman_cosine \| 0.3556 \|
	\| pearson_manhattan \| 0.3674 \|
	\| spearman_manhattan \| 0.3646 \|
	\| pearson_euclidean \| 0.3607 \|
	\| spearman_euclidean \| 0.3548 \|
	\| pearson_dot \| 0.2125 \|
	\| spearman_dot \| 0.2006 \|
	\| pearson_max \| 0.3674 \|
	\| spearman_max \| 0.3646 \|

	#### Semantic Similarity

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

	\| Metric \| Value \|
	\|:-------------------\|:-----------\|
	\| pearson_cosine \| 0.9592 \|
	\| spearman_cosine \| 0.9206 \|
	\| pearson_manhattan \| 0.9531 \|
	\| spearman_manhattan \| 0.9204 \|
	\| pearson_euclidean \| 0.9533 \|
	\| spearman_euclidean \| 0.9202 \|
	\| pearson_dot \| 0.9482 \|
	\| spearman_dot \| 0.9016 \|
	\| pearson_max \| 0.9592 \|
	\| spearman_max \| 0.9206 \|

	<!--
	## 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.
	-->

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	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 10,501 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 7 tokens</li><li>mean: 20.14 tokens</li><li>max: 59 tokens</li></ul> \| <ul><li>min: 7 tokens</li><li>mean: 19.71 tokens</li><li>max: 68 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:----------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------\|:------------------\|
	\| <code>가스레인지 사용하지 않도록 유의해주세요</code> \| <code>가스레인지 사용은 삼가주세요</code> \| <code>0.74</code> \|
	\| <code>이번주하고 다음주 중에 언제 동기 모임이 있어?</code> \| <code>언제 자연어처리 학회 논문 접수가 마감되나요?</code> \| <code>0.02</code> \|
	\| <code>또한 각 부처는 생활방역 관련 업무를 종합·체계적으로 수행하기 위해 기관별로 생활방역 전담팀(TF)을 구성한다.</code> \| <code>또한 생활방지와 관련된 업무를 종합적이고 체계적으로 수행하기 위하여 각 부서별로 생활방역 전담 태스크포스(TF)를 구성하여야 합니다.</code> \| <code>0.72</code> \|
	* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
	```json
	{
	"loss_fct": "torch.nn.modules.loss.MSELoss"
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 4
	- `multi_dataset_batch_sampler`: round_robin

	#### 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`: 16
	- `per_device_eval_batch_size`: 16
	- `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`: 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`: 4
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `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`: False
	- `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`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `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
	- `dispatch_batches`: None
	- `split_batches`: 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
	- `eval_use_gather_object`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| spearman_max \|
	\|:------:\|:----:\|:-------------:\|:------------:\|
	\| 0 \| 0 \| - \| 0.3646 \|
	\| 0.7610 \| 500 \| 0.0278 \| - \|
	\| 1.0 \| 657 \| - \| 0.9187 \|
	\| 1.5221 \| 1000 \| 0.0085 \| 0.9117 \|
	\| 2.0 \| 1314 \| - \| 0.9201 \|
	\| 2.2831 \| 1500 \| 0.0044 \| - \|
	\| 3.0 \| 1971 \| - \| 0.9186 \|
	\| 3.0441 \| 2000 \| 0.0034 \| 0.9199 \|
	\| 3.8052 \| 2500 \| 0.0027 \| - \|
	\| 4.0 \| 2628 \| - \| 0.9206 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.031 kWh
	- Carbon Emitted: 0.014 kg of CO2
	- Hours Used: 0.154 hours

	### Training Hardware
	- On Cloud: No
	- GPU Model: 1 x NVIDIA GeForce RTX 3060
	- CPU Model: 12th Gen Intel(R) Core(TM) i5-12400
	- RAM Size: 31.78 GB

	### Framework Versions
	- Python: 3.12.4
	- Sentence Transformers: 3.2.1
	- Transformers: 4.45.2
	- PyTorch: 2.4.0+cu121
	- Accelerate: 0.29.3
	- Datasets: 2.19.0
	- Tokenizers: 0.20.1

	## 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",
	}
	```

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