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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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### Out-of-Scope Use
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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** |
<!--
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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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