README.md

---
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:7654
- loss:CosineSimilarityLoss
widget:
- source_sentence: 밥을 먹고 나서 운동하시겠어요, 먹기 전에 하시겠어요?
  sentences:
  - 제습기 조정하는 방법을 알려줘
  - 금요일에 놀러 가고 싶은지 토요일에 가고 싶은지 말해보겠니?
  - 이번에 임원들도 오시니 거래처 사람들과 만날 때 늦지 마세요.
- source_sentence: 올해 지원 대상에 선정된 42개사는 사업화 자금부터 사업화 촉진 진단, 민간투자 유치 등 기업 규모를 키울 수 있는
    각종 지원을 최대 15개월까지 받을 수 있다.
  sentences:
  - 체크인 아웃 할 때 소통이나 협조도도 매우 좋습니다
  - 작년 용평 지역 강설량은?
  - 긴급 사태가 선언된 7개 도부현의 지사는 법적인 근거 아래 외출자제와 휴교 등을 요청할 수 있다.
- source_sentence: 언제 할머니 칠순 잔치가 잡혀 있나요, 이번달입니까 다음달입니까?
  sentences:
  - 그리고 세탁세제와 식용유가 없으니 준비 하세요
  - 삼월에 태어난 친구 이름이 어떻게 됩니까?
  - 비 올 때는 다른 신발 말고 장화를 신었으면 합니다.
- source_sentence: 한메일 서비스를 사용할 수 있는 기한이 언제일까요?
  sentences:
  - 우리는 코로나19와의 투쟁에서 개발도상국들을 지원해야 할 필요성을 인정한다.
  - 비 올 때는 높은지대에 텐트 치도록 해. 낮은 지대는 별로야.
  - 한메일은 언제 서비스를 종료해?
- source_sentence: 오늘 제가 해야할 일이 무엇인가요!
  sentences:
  - 시내 중심에 위치한 깔끔하고 머무르기 좋은 숙소 입니다.
  - 가게로 들어가는 문 바로 옆에 오른쪽으로 올라가는 입구가 있어요.
  - 언제쯤 친구가 여행 갈 수 있겠니?
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: 0.3477070578392738
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.35560473197486514
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.36738467673522557
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.36460670798564826
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.36074511612166327
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.35482778401649034
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.21251170218646828
      name: Pearson Dot
    - type: spearman_dot
      value: 0.20063256899469895
      name: Spearman Dot
    - type: pearson_max
      value: 0.36738467673522557
      name: Pearson Max
    - type: spearman_max
      value: 0.36460670798564826
      name: Spearman Max
    - type: pearson_cosine
      value: 0.9611295434382598
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.922281644313147
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.95182850390749
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.9211213430736883
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9519510086799272
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.9217056450919558
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.9503136478175895
      name: Pearson Dot
    - type: spearman_dot
      value: 0.9045157489205089
      name: Spearman Dot
    - type: pearson_max
      value: 0.9611295434382598
      name: Pearson Max
    - type: spearman_max
      value: 0.922281644313147
      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 = [
    '오늘 제가 해야할 일이 무엇인가요!',
    '언제쯤 친구가 여행 갈 수 있겠니?',
    '시내 중심에 위치한 깔끔하고 머무르기 좋은 숙소 입니다.',
]
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     | 0.9611     |
| spearman_cosine    | 0.9223     |
| pearson_manhattan  | 0.9518     |
| spearman_manhattan | 0.9211     |
| pearson_euclidean  | 0.952      |
| spearman_euclidean | 0.9217     |
| pearson_dot        | 0.9503     |
| spearman_dot       | 0.9045     |
| pearson_max        | 0.9611     |
| **spearman_max**   | **0.9223** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 7,654 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: 19.59 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.37 tokens</li><li>max: 55 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>‘인공지능 반도체 산업 발전전략’의 차질 없는 이행 및 성과점검을 위해 정부와 산·학·연이 참여하는 ‘인공지능 반도체 산업 전략회의’를 구성·운영한다.</code> | <code>정부, 산업계, 학계, 연구기관이 참여하는 '인공지능 반도체산업전략회의'를 구성하여 '인공지능 반도체산업 발전전략'의 성과를 점검할 예정입니다.</code> | <code>0.6</code>  |
  | <code>예상했던대로 가성비 대비  최고의 위치였어요.</code>                                                            | <code>처음에 예상했던것보다 위치가 훨씬 좋았어요</code>                                                          | <code>0.54</code> |
  | <code>올해 처음 개최되는 투자유치설명회는 전문투자기관에 홍보할 기회를 얻기 힘든 1인 미디어 스타트업들의 민간 투자유치를 지원할 목적으로 마련됐다.</code>      | <code>이번 발사는 저궤도위성에 이어 정지궤도위성에서 실시간으로 환경 감시 업무를 수행하는 세계 최초의 위성으로 기록됐다.</code>                 | <code>0.04</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       |
| 1.0    | 479  | -             | 0.9133       |
| 1.0438 | 500  | 0.0281        | -            |
| 2.0    | 958  | -             | 0.9181       |
| 2.0877 | 1000 | 0.006         | 0.9217       |
| 3.0    | 1437 | -             | 0.9191       |
| 3.1315 | 1500 | 0.0036        | -            |
| 4.0    | 1916 | -             | 0.9223       |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- 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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