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	---
	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
	widget:
	- source_sentence: 백마를 이끄는 여자
	sentences:
	- 갈색 말을 타고 있는 여자
	- 남자와 여자가 키스하고 있다.
	- 남자가 칼로 물병을 썰고 있다
	- source_sentence: 꽤 괜찮은데
	sentences:
	- 아주 좋아요.
	- 개가 옷을 입고 있다.
	- 아무도 무대에 서지 않는다.
	- source_sentence: 지루하군요.
	sentences:
	- 힘드네요! 정말 힘드네요!
	- 여자는 아이를 돕는다.
	- 사람들이 손을 내밀고 있다
	- source_sentence: 인간의 지위
	sentences:
	- 인간의 지위.
	- 그것은 비열하지 않다.
	- 아무도 해고당하지 않는다.
	- source_sentence: 인간의 지적
	sentences:
	- 인간 관찰
	- 사람들이 안에 서 있다
	- 아무도 앉아 있지 않다
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on klue/roberta-small
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.848109514939322
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8469617889194193
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8290541524988974
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.832916353112548
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8296914939989355
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8335696459808043
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7961861998493428
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7996870460025013
	name: Spearman Dot
	- type: pearson_max
	value: 0.848109514939322
	name: Pearson Max
	- type: spearman_max
	value: 0.8469617889194193
	name: Spearman Max
	---


	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Maximum Sequence Length: 256 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': 256, '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 = [
	'인간의 지적',
	'인간 관찰',
	'사람들이 안에 서 있다',
	]
	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 \| 0.8481 \|
	\| spearman_cosine \| 0.847 \|
	\| pearson_manhattan \| 0.8291 \|
	\| spearman_manhattan \| 0.8329 \|
	\| pearson_euclidean \| 0.8297 \|
	\| spearman_euclidean \| 0.8336 \|
	\| pearson_dot \| 0.7962 \|
	\| spearman_dot \| 0.7997 \|
	\| pearson_max \| 0.8481 \|
	\| spearman_max \| 0.847 \|

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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: 19.02 tokens</li><li>max: 156 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 18.36 tokens</li><li>max: 95 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.31 tokens</li><li>max: 35 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:------------------------------------------\|:-----------------------------------------------------------------------\|:------------------------------------------\|
	\| <code>악기를 연주하는 사람.</code> \| <code>여자 옆에서 백파이프를 연주하는 잘 차려입은 남자</code> \| <code>노숙자가 잔돈을 구걸한다.</code> \|
	\| <code>셔츠에 이벤트 번호를 새긴 남자들은 길을 걸어간다.</code> \| <code>멘스 셔츠에 숫자가 적혀 있다.</code> \| <code>남자들이 길에서 자고 있다.</code> \|
	\| <code>군인들은 기지에서 함께 어울린다.</code> \| <code>한 무리의 군인들이 그늘을 입고 방에 함께 앉아 있었고, 벽에 있는 작은 틈으로 빛이 최고조에 달했다.</code> \| <code>한 무리의 민간인들이 적의 공격으로부터 움츠러든다.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	768,
	256
	],
	"matryoshka_weights": [
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	#### Unnamed Dataset


	* Size: 5,749 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: 5 tokens</li><li>mean: 17.15 tokens</li><li>max: 71 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 16.86 tokens</li><li>max: 76 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>남자가 기타를 치고 있다.</code> \| <code>소뇌는 기타를 치고 있다.</code> \| <code>0.72</code> \|
	\| <code>고양이가 빨판을 핥고 있다.</code> \| <code>한 여성이 오이를 자르고 있다.</code> \| <code>0.0</code> \|
	\| <code>누군가가 파워 드릴로 나무 조각에 구멍을 뚫는다.</code> \| <code>한 남자가 나무 조각에 구멍을 뚫는다.</code> \| <code>0.64</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "CosineSimilarityLoss",
	"matryoshka_dims": [
	768,
	256
	],
	"matryoshka_weights": [
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	### 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 \|
	\|:------:\|:----:\|:-------------:\|:--------------------:\|
	\| 0.3477 \| 500 \| 0.931 \| - \|
	\| 0.6954 \| 1000 \| 0.7062 \| 0.8313 \|
	\| 1.0007 \| 1439 \| - \| 0.8379 \|
	\| 1.0424 \| 1500 \| 0.5893 \| - \|
	\| 1.3901 \| 2000 \| 0.3406 \| 0.8343 \|
	\| 1.7378 \| 2500 \| 0.2514 \| - \|
	\| 2.0007 \| 2878 \| - \| 0.8450 \|
	\| 2.0848 \| 3000 \| 0.2252 \| 0.8470 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.0
	- Transformers: 4.41.2
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.30.1
	- Datasets: 2.19.2
	- 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",
	}
	```

	#### MatryoshkaLoss
	```bibtex
	@misc{kusupati2024matryoshka,
	title={Matryoshka Representation Learning},
	author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
	year={2024},
	eprint={2205.13147},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	```

	#### 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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