README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10501
- loss:CosineSimilarityLoss
base_model: klue/roberta-base
widget:
- source_sentence: 아침마다 제가 원하는 시간에 맛있는 조식도 먹을 수 있었어요.
  sentences:
  - 매일 아침 내가 원하는 시간에 맛있는 아침식사를 먹을 수 있었습니다.
  - 태풍과 폭염 중 어떤 것이 올까요?
  - 떼르미니 역에서 5분 이내고 주변에 마트 식당 빵집 등등 편의시설도 가득합니다.
- source_sentence: 아무리 우수한 방역체계도 신뢰 없이는 작동하기 어렵습니다.
  sentences:
  - 좋은 위치와 좋은 숙소와 좋은 호스트가 있습니다.
  - 위치도 룸도 모든 기 완벽한 곳이었다!
  - 콜센터 시설 내외부 방역도 철저히 실시하기로 했다.
- source_sentence: 굳이 모든 메일을 다 가지고 있을 필요는 없어. 중요하지 않은 학회 홍보 메일은 지워도 돼.
  sentences:
  - 바르셀로나에 가실 거면 시내에 안 계셔도 된다면 이 숙소를 추천해 드릴게요!
  - 학교에서 온 메일 말고 학회 홍보메일만 삭제해줘
  - 사그라다 파밀리아까지는 걸어서 10분거리구요.
- source_sentence: 더운물로 세탁하자.
  sentences:
  - 네가 시간 떼울 때 보고싶은 오락 프로그램 이름 알려주면 찾아볼께
  - 장인어른과의 약속에 정시에 가지 말고 일찍 나오세요.
  - 안방 취침등 또는 형광등은 어떻게 켜?
- source_sentence: 또한 숙소는 청결하고 아늑한 장소입니다.
  sentences:
  - 또한, 숙소는 깨끗하고 아늑한 곳입니다.
  - 깜빡하고 백화점 세일 일정 잊어버리면 안된다.
  - 전체적으로 집 내부가 너무 예뻤어요.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
co2_eq_emissions:
  emissions: 6.29574616666927
  energy_consumed: 0.014386922744112848
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: Intel(R) Core(TM) i7-14700KF
  ram_total_size: 63.83439254760742
  hours_used: 0.044
  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: 0.3477070403258199
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.35560473197486514
      name: Spearman Cosine
    - type: pearson_cosine
      value: 0.9624051736790307
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.922152297127282
      name: Spearman Cosine
---

# 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 dimensions
- **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 = [
    '또한 숙소는 청결하고 아늑한 장소입니다.',
    '또한, 숙소는 깨끗하고 아늑한 곳입니다.',
    '깜빡하고 백화점 세일 일정 잊어버리면 안된다.',
]
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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You can finetune this model on your own dataset.

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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** |

#### 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      | 0.9624     |
| **spearman_cosine** | **0.9222** |

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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: 6 tokens</li><li>mean: 19.8 tokens</li><li>max: 81 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.36 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.46</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                      | sentence_1                                                   | label             |
  |:----------------------------------------------------------------|:-------------------------------------------------------------|:------------------|
  | <code>아울러, 4월 9일부터 5월말까지 EBS 교육사이트를 데이터 걱정 없이 이용할 수 있습니다</code> | <code>현장방문 신청 둘째 주인 11월 2일부터 11월 6일까지는 구분없이 신청할 수 있다.</code> | <code>0.08</code> |
  | <code>내일 오전에 있는 수업 몇 시에 시작하더라?</code>                           | <code>남자친구 생일이 언제야?</code>                                   | <code>0.0</code>  |
  | <code>아무리 우수한 방역체계도 신뢰 없이는 작동하기 어렵습니다.</code>                   | <code>콜센터 시설 내외부 방역도 철저히 실시하기로 했다.</code>                    | <code>0.12</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

- `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
- `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
- `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
- `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, '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
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss | spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|
| 0      | 0    | -             | 0.3556          |
| 0.7610 | 500  | 0.0279        | -               |
| 1.0    | 657  | -             | 0.9086          |
| 1.5221 | 1000 | 0.0087        | 0.9158          |
| 2.0    | 1314 | -             | 0.9177          |
| 2.2831 | 1500 | 0.0046        | -               |
| 3.0    | 1971 | -             | 0.9191          |
| 3.0441 | 2000 | 0.0034        | 0.9199          |
| 3.8052 | 2500 | 0.0027        | -               |
| 4.0    | 2628 | -             | 0.9222          |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.014 kWh
- **Carbon Emitted**: 0.006 kg of CO2
- **Hours Used**: 0.044 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 4090
- **CPU Model**: Intel(R) Core(TM) i7-14700KF
- **RAM Size**: 63.83 GB

### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.3.1
- Transformers: 4.40.1
- PyTorch: 2.5.1+cu118
- Accelerate: 0.29.3
- 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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