sentence-transformers safetensors roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:10501 loss:CosineSimilarityLoss arxiv:1908.10084 base_model:klue/roberta-base base_model:finetune:klue/roberta-base model-index co2_eq_emissions autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: klue/roberta-base
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: 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
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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## 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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