README.md

---
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: 추운날이니 외출은 자제해주시기 바랍니다.
  sentences:
  - 추운날인데 외출하지마
  - 소·돼지에 대해서만 실시하던 축산물이력제가 1월 1일부터 닭·오리·계란까지 확대·시행된다.
  - 광고메일함 비중이 에어비앤비가 더 높니 트립닷컴이 더 많니?
- source_sentence: 샤워기도 수압이 너무 약해서 불편해요.
  sentences:
  - 숙소 내부가 넓고 호스트도 1층에 있어 불편사항에 대한 피드백을 즉시 받으실 수 있습니다.
  - 그외에 물놀이를 하기위한 준비물들 파라솔 비치의자 어린이비치의자 아이스박스 핸드케리어 비치타월 모레놀이도구 등등 필요한 모든것이 완벽했습니다.
  - 샤워는 수압이 너무 약해서 불편해요.
- source_sentence: 조용한 분위기의 방을 구하시면 이 곳이 최고입니다!
  sentences:
  - 시험을 이번달에 본다고 했니 다음달에 본다고 했니?
  - 조용한 방을 찾는다면, 이곳이 최고예요!
  - 어른들과 만나는 자리에는 어른들보다 늦게 도착하지 말고 일찍 나가 있어라.
- source_sentence: 발코니쪽 창문은 3개중에 한개만 열수있습니다.
  sentences:
  - 많은 장비를 구매할 필요 없이 즐길 수 있습니다.
  - 우리는 그 숙소에서 호바트의 최상의 상태를 유지할 수 있었습니다.
  - 직장가입자의 급여명세서, 지역가입자의 건강보험 급여통지서를 확인하실 수 있습니다.
- source_sentence: 국민 추천으로 ‘금융규제 유연화로 선제적 금융권 지원역량 강화’도 우수 사례로 언급됐다.
  sentences:
  - 국민의 권고에 따라 '유연한 금융규제 등을 통해 선제적으로 금융분야 지원능력 강화'도 좋은 사례로 꼽혔습니다.
  - 사진으로 보이는거 보다 숙소는 넓었고요
  - 저는 다음에 대만을 간다면 무조건 재방문 할 예정입니다!
model-index:
- name: SentenceTransformer
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.9626619602187976
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9247880695962829
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9555167285690431
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.923408354022865
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9556439523907834
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.9235806565450854
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.957361957340705
      name: Pearson Dot
    - type: spearman_dot
      value: 0.9130155209197447
      name: Spearman Dot
    - type: pearson_max
      value: 0.9626619602187976
      name: Pearson Max
    - type: spearman_max
      value: 0.9247880695962829
      name: Spearman Max
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** [Unknown](https://huggingface.co/unknown) -->
- **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': True}) 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 = [
    '국민 추천으로 ‘금융규제 유연화로 선제적 금융권 지원역량 강화’도 우수 사례로 언급됐다.',
    "국민의 권고에 따라 '유연한 금융규제 등을 통해 선제적으로 금융분야 지원능력 강화'도 좋은 사례로 꼽혔습니다.",
    '사진으로 보이는거 보다 숙소는 넓었고요',
]
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>

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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     | 0.9627     |
| spearman_cosine    | 0.9248     |
| pearson_manhattan  | 0.9555     |
| spearman_manhattan | 0.9234     |
| pearson_euclidean  | 0.9556     |
| spearman_euclidean | 0.9236     |
| pearson_dot        | 0.9574     |
| spearman_dot       | 0.913      |
| pearson_max        | 0.9627     |
| **spearman_max**   | **0.9248** |

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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: 5 tokens</li><li>mean: 20.16 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.75 tokens</li><li>max: 58 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>단점을 꼽자면 엘베가 없다는 점 정도?</code>  | <code>굳이 단점을 꼽자면 늦은 밤에는 역 근처가 살짝 무섭다는 거?</code> | <code>0.2</code> |
  | <code>더울 때는 청량음료 말고 물 많이 마셔.</code> | <code>추울 때 손과 발은 내놓지 말자.</code>                 | <code>0.0</code> |
  | <code>위치, 시설, 호스팅 모두 만족했습니다.</code> | <code>위치, 시설, 호스팅 모두 만족스러웠습니다.</code>           | <code>1.0</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`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 7
- `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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 7
- `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 | sts-dev_spearman_max |
|:------:|:----:|:-------------:|:--------------------:|
| 1.0    | 329  | -             | 0.9218               |
| 1.5198 | 500  | 0.0096        | -                    |
| 2.0    | 658  | -             | 0.9218               |
| 3.0    | 987  | -             | 0.9215               |
| 3.0395 | 1000 | 0.0064        | 0.9218               |
| 4.0    | 1316 | -             | 0.9231               |
| 4.5593 | 1500 | 0.0055        | -                    |
| 5.0    | 1645 | -             | 0.9231               |
| 6.0    | 1974 | -             | 0.9235               |
| 6.0790 | 2000 | 0.0045        | 0.9226               |
| 7.0    | 2303 | -             | 0.9248               |


### 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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