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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:10501
	- loss:CosineSimilarityLoss
	widget:
	- source_sentence: 기업은 생존 문제에 직면하고, 자영업자와 소상공인의 고통은 이루 말할 수 없을 정도입니다.
	sentences:
	- 자유무역은 기업이 서로를 신뢰하고, 미래의 불확실성을 낮추는 안전장치입니다.
	- 국가 임상연구 승인, 시행기관 지정, 장기 추적조사 등 안전관리체계를 구축하고 치료 개발 및 임상연구 수행을 위한 RD 투자를 확대합니다.
	- 중심가와 거리가 조금 먼 점 빼고는 정말 모든게 너무 좋았던 숙소입니다!
	- source_sentence: 타이페이를 다시 간다면 여기 또 올거예요.
	sentences:
	- 사진으로 봤던것보다 훨씬 더 좋았습니다
	- 겨울에 난방 온도 이십오도 이상으로 올리지마라고 경고했어
	- 만약 내가 다시 타이페이에 간다면, 나는 여기에 다시 올 것입니다.
	- source_sentence: 호주의 좋은 가정집에서 묵는 느낌이었어요.
	sentences:
	- 어린이 교통사고 위험지역에 CCTV 2087대, 신호등 2146개를 올해 상반기 중으로 설치하고 옐로카펫과 노란발자국 등을 올해 하반기에 초등학교
	100곳에 시범 설치한다.
	- 호주에 있는 좋은 집에서 지내는 것 같았어요.
	- 그러나 호텔업계 노사가 가장 어려운 시기에, 가장 모범적으로 함께 마음을 모았습니다.
	- source_sentence: 그들덕분에 우리는 4일간 편안히 쉴 수 있었습니다.
	sentences:
	- 그들 덕분에, 우리는 4일 동안 쉴 수 있었어요.
	- 주변에 두 개의 지하철역이 있습니다. 큰 공원, 큰 슈퍼마켓, 그리고 편의점이 있습니다.
	- 방은 쾌적하고 에어컨도 아주 잘 나와요.
	- source_sentence: 테라스에서 봤던 뷰와 그곳에서 먹었던 식사가 그리울 것 같아요.
	sentences:
	- 테라스에서 본 풍경과 거기서 먹었던 음식이 그리울 것 같아요.
	- 이쪽 주변에서 여행할 계획이라면 추천합니다!
	- 저희 할아버지는 매우 친절하고 친절하십니다.
	co2_eq_emissions:
	emissions: 7.379414346751554
	energy_consumed: 0.016863301234344347
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: 13th Gen Intel(R) Core(TM) i7-13700
	ram_total_size: 62.56697463989258
	hours_used: 0.057
	hardware_used: 1 x NVIDIA GeForce RTX 4090
	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: 0.34770704341988723
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.35560473197486514
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.3673846313946801
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.36460670798564826
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.3607451203867209
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.35482778401649034
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.21251167982120983
	name: Pearson Dot
	- type: spearman_dot
	value: 0.20063256899469895
	name: Spearman Dot
	- type: pearson_max
	value: 0.3673846313946801
	name: Pearson Max
	- type: spearman_max
	value: 0.36460670798564826
	name: Spearman Max
	- type: pearson_cosine
	value: 0.961968864970919
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9196100863981246
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.9530332430579778
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.9186168431687389
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.9532923011007042
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.9190754386835427
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.9493179101338206
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8999468521869318
	name: Spearman Dot
	- type: pearson_max
	value: 0.961968864970919
	name: Pearson Max
	- type: spearman_max
	value: 0.9196100863981246
	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 = [
	'테라스에서 봤던 뷰와 그곳에서 먹었던 식사가 그리울 것 같아요.',
	'테라스에서 본 풍경과 거기서 먹었던 음식이 그리울 것 같아요.',
	'이쪽 주변에서 여행할 계획이라면 추천합니다!',
	]
	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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	</details>
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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 \| 0.962 \|
	\| spearman_cosine \| 0.9196 \|
	\| pearson_manhattan \| 0.953 \|
	\| spearman_manhattan \| 0.9186 \|
	\| pearson_euclidean \| 0.9533 \|
	\| spearman_euclidean \| 0.9191 \|
	\| pearson_dot \| 0.9493 \|
	\| spearman_dot \| 0.8999 \|
	\| pearson_max \| 0.962 \|
	\| spearman_max \| 0.9196 \|

	<!--
	## 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: 20.23 tokens</li><li>max: 64 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 19.94 tokens</li><li>max: 63 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>지하철 역 내려서 1분정도의 아주 가까운 거리입니다.</code> \| <code>지하철역에서 1분 정도 아주 가까운 거리입니다.</code> \| <code>0.86</code> \|
	\| <code>그것빼곤 2인여행자들에게는 좋은숙소에요!</code> \| <code>계단이 많다는거 빼곤 완벽한 숙소에요!</code> \| <code>0.27999999999999997</code> \|
	\| <code>이어 현금이 286만 가구(13.2%) 1조3007억원, 선불카드가 75만 가구(3.5%) 4990억원, 지역사랑상품권은 63만 가구(2.9%) 4171억원으로 각각 집계됐다.</code> \| <code>이어 현금 286만 가구(13.2%), 현금 1조337억 원, 선불카드 75만 가구(3.5%), 4990억 원, 지역사랑상품권 63만 가구(2.9%), 4171억 원 순이었습니다.</code> \| <code>0.86</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
	- `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 \| 0.0283 \| - \|
	\| 1.0 \| 657 \| - \| 0.9075 \|
	\| 1.5221 \| 1000 \| 0.0082 \| 0.9148 \|
	\| 2.0 \| 1314 \| - \| 0.9148 \|
	\| 2.2831 \| 1500 \| 0.0047 \| - \|
	\| 3.0 \| 1971 \| - \| 0.9180 \|
	\| 3.0441 \| 2000 \| 0.0034 \| 0.9168 \|
	\| 3.8052 \| 2500 \| 0.0027 \| - \|
	\| 4.0 \| 2628 \| - \| 0.9196 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.017 kWh
	- Carbon Emitted: 0.007 kg of CO2
	- Hours Used: 0.057 hours

	### Training Hardware
	- On Cloud: No
	- GPU Model: 1 x NVIDIA GeForce RTX 4090
	- CPU Model: 13th Gen Intel(R) Core(TM) i7-13700
	- RAM Size: 62.57 GB

	### Framework Versions
	- Python: 3.9.0
	- Sentence Transformers: 3.0.1
	- Transformers: 4.44.1
	- PyTorch: 2.3.1+cu121
	- Accelerate: 0.33.0
	- Datasets: 2.19.1
	- 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",
	}
	```

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