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	---
	base_model: klue/roberta-base
	datasets: []
	language: []
	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
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:574408
	- loss:MultipleNegativesRankingLoss
	- loss:CosineSimilarityLoss
	widget:
	- source_sentence: 한 여성이 뜨거운 물 냄비에 음식을 넣고 있다.
	sentences:
	- 한 여성이 고기를 튀기고 있다.
	- '세계 브리핑 아시아 : 미얀마 : 치명적인 반 무슬림 폭력 사태가 폭발했다.'
	- 아기가 잠들고 있다.
	- source_sentence: 러시아 비행기 추락 사고로 사망자 수 증가
	sentences:
	- 이탈리아 코치 추락으로 사망자 수가 39명으로 증가
	- 헬리콥터 펍 추락 후 사망 두려워하는 세 명
	- 흑백 개는 입에 갈색 물체를 물고 헤엄친다.
	- source_sentence: 거울에 비친 한 여자가 옆에 있는 다른 여자와 함께 카메라를 외면하고 앉아 있었다.
	sentences:
	- 보도 위를 걷는 여자와 함께 길을 건너는 흑인 여성의 뒷모습.
	- 여자가 거울을 응시하고 있다
	- 한 여성이 햄버거를 응시하고 있다
	- source_sentence: 스키를 탄 사람이 공중으로 뛰어오른다.
	sentences:
	- 밖에 한 남자가 있다.
	- 그는 눈 위를 달리고 있다.
	- 그는 스키를 타고 공중으로 뛰어올랐다.
	- source_sentence: 내 옆이나 내 뒤에, 경외심을 느끼며 언더톤으로 말했다.
	sentences:
	- 그는 나와는 거리가 멀었다.
	- FSIS는 접수된 의견과 기관의 요구 사항 재평가를 고려하여 연간 부담을 8,053,319시간으로 줄였습니다.
	- 그는 나와 가까웠다.
	model-index:
	- name: SentenceTransformer based on klue/roberta-base
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.8657393669442817
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.866343037897214
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8568809906017532
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8621129068016818
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8569880055215549
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8620159980137003
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.8382433069709427
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8359003576467027
	name: Spearman Dot
	- type: pearson_max
	value: 0.8657393669442817
	name: Pearson Max
	- type: spearman_max
	value: 0.866343037897214
	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: 128 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': 128, '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 = [
	'내 옆이나 내 뒤에, 경외심을 느끼며 언더톤으로 말했다.',
	'그는 나와 가까웠다.',
	'그는 나와는 거리가 멀었다.',
	]
	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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	## Evaluation

	### Metrics

	#### Semantic Similarity
	* Dataset: `sts-dev`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:-------------------\|:-----------\|
	\| pearson_cosine \| 0.8657 \|
	\| spearman_cosine \| 0.8663 \|
	\| pearson_manhattan \| 0.8569 \|
	\| spearman_manhattan \| 0.8621 \|
	\| pearson_euclidean \| 0.857 \|
	\| spearman_euclidean \| 0.862 \|
	\| pearson_dot \| 0.8382 \|
	\| spearman_dot \| 0.8359 \|
	\| pearson_max \| 0.8657 \|
	\| spearman_max \| 0.8663 \|

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

	### Training Datasets

	#### Unnamed Dataset


	* Size: 568,640 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 19.2 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 18.3 tokens</li><li>max: 93 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.64 tokens</li><li>max: 54 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:----------------------------------------\|:-------------------------------------------------\|:--------------------------------------\|
	\| <code>발생 부하가 함께 5% 적습니다.</code> \| <code>발생 부하의 5% 감소와 함께 11.</code> \| <code>발생 부하가 5% 증가합니다.</code> \|
	\| <code>어떤 행사를 위해 음식과 옷을 배급하는 여성들.</code> \| <code>여성들은 음식과 옷을 나눠줌으로써 난민들을 돕고 있다.</code> \| <code>여자들이 사막에서 오토바이를 운전하고 있다.</code> \|
	\| <code>어린 아이들은 그 지식을 얻을 필요가 있다.</code> \| <code>응, 우리 젊은이들 중 많은 사람들이 그걸 배워야 할 것 같아.</code> \| <code>젊은 사람들은 배울 필요가 없다.</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	#### Unnamed Dataset


	* Size: 5,768 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: 4 tokens</li><li>mean: 17.14 tokens</li><li>max: 53 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 17.21 tokens</li><li>max: 56 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:----------------------------------------------------------------------\|:--------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>식품의약품관리국은 셔디 리서치를 인용하여 2001년 IMClone의 에르비턱스 판매 신청을 거절했다.</code> \| <code>미국 식품의약국은 2001년 12월 이 재판이 부실하게 진행되었다고 말하면서 이클론의 원래 신청을 거부했다.</code> \| <code>0.5599999999999999</code> \|
	\| <code>이슬람 주도의 이집트 , 콥트 교회 이름은 새로운 교황이다</code> \| <code>이집트 기독교인들은 새로운 교황을 선택한다</code> \| <code>0.64</code> \|
	\| <code>시리아 주지사는 공격을 중단하지 않는다</code> \| <code>시리아 야당, '학살' 보고</code> \| <code>0.2</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
	- `num_train_epochs`: 5
	- `batch_sampler`: no_duplicates
	- `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`: 8
	- `per_device_eval_batch_size`: 8
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_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`: {}
	- `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
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| sts-dev_spearman_max \|
	\|:------:\|:----:\|:-------------:\|:--------------------:\|
	\| 0.3467 \| 500 \| 0.419 \| - \|
	\| 0.6935 \| 1000 \| 0.3032 \| 0.8516 \|
	\| 1.0007 \| 1443 \| - \| 0.8605 \|
	\| 1.0395 \| 1500 \| 0.2705 \| - \|
	\| 1.3863 \| 2000 \| 0.1368 \| 0.8509 \|
	\| 1.7330 \| 2500 \| 0.0906 \| - \|
	\| 2.0007 \| 2886 \| - \| 0.8663 \|


	### Framework Versions
	- Python: 3.11.9
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.2.2+cu121
	- Accelerate: 0.31.0
	- Datasets: 2.20.0
	- Tokenizers: 0.19.1

	## 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",
	}
	```

	#### MultipleNegativesRankingLoss
	```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}
	}
	```

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