sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:11668 loss:MultipleNegativesRankingLoss ko dataset:klue/klue arxiv:1908.10084 arxiv:1705.00652 base_model:klue/bert-base base_model:finetune:klue/bert-base autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: klue/bert-base
datasets:
- klue/klue
language:
- ko
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:11668
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: klue-sts-v1_dev_00238
  sentences:
  - 이것은 7월 15일에 열린 주요 국가의 외무 장관들 간의 첫 번째 회담에 이은 것입니다.
  - policy-rtt
  - 이는 지난 15일 개최된 제1차 주요국 외교장관간 협의에 뒤이은 것이다.
- source_sentence: klue-sts-v1_dev_00135
  sentences:
  - 3000만원 이하 소액대출은 지역신용보증재단 심사를 기업은행에 위탁하기로 했다.
  - policy-rtt
  - 3,000만원 미만의 소규모 대출은 기업은행에 의해 국내 신용보증재단을 검토하도록 의뢰될 것입니다.
- source_sentence: klue-sts-v1_dev_00227
  sentences:
  - 그 공간은 4인 가족에게는 충분하지 않았습니다.
  - 공간은 4명의 성인 가족이 사용하기에 부족함이 없었고.
  - airbnb-rtt
- source_sentence: klue-sts-v1_dev_00224
  sentences:
  - 타이페이 메인 역까지 걸어서 10분 정도 걸립니다.
  - 클락키까지 걸어서 10분 정도 걸려요.
  - airbnb-sampled
- source_sentence: klue-sts-v1_dev_00159
  sentences:
  - 거실옆 작은 방에도 싱글 침대가 두개 있습니다.
  - 2층에 얇은 벽 하나 사이로 방이 두 개 있습니다.
  - airbnb-sampled
---

# SentenceTransformer based on klue/bert-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [klue/bert-base](https://huggingface.co/klue/bert-base) on the [klue](https://huggingface.co/datasets/klue) dataset. 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/bert-base](https://huggingface.co/klue/bert-base) <!-- at revision 77c8b3d707df785034b4e50f2da5d37be5f0f546 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [klue](https://huggingface.co/datasets/klue)
- **Language:** ko
<!-- - **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: BertModel 
  (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("kgmyh/klue_bert-base_finetuning")
# Run inference
sentences = [
    'klue-sts-v1_dev_00159',
    'airbnb-sampled',
    '2층에 얇은 벽 하나 사이로 방이 두 개 있습니다.',
]
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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## Training Details

### Training Dataset

#### klue

* Dataset: [klue](https://huggingface.co/datasets/klue) at [349481e](https://huggingface.co/datasets/klue/tree/349481ec73fff722f88e0453ca05c77a447d967c)
* Size: 11,668 training samples
* Columns: <code>guid</code>, <code>source</code>, <code>sentence1</code>, <code>sentence2</code>, and <code>labels</code>
* Approximate statistics based on the first 1000 samples:
  |         | guid                                                                               | source                                                                            | sentence1                                                                         | sentence2                                                                         | labels             |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------|
  | type    | string                                                                             | string                                                                            | string                                                                            | string                                                                            | dict               |
  | details | <ul><li>min: 17 tokens</li><li>mean: 17.91 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 10.01 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 19.55 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.35 tokens</li><li>max: 60 tokens</li></ul> | <ul><li></li></ul> |
* Samples:
  | guid                                 | source                       | sentence1                                                  | sentence2                                               | labels                                                                           |
  |:-------------------------------------|:-----------------------------|:-----------------------------------------------------------|:--------------------------------------------------------|:---------------------------------------------------------------------------------|
  | <code>klue-sts-v1_train_00000</code> | <code>airbnb-rtt</code>      | <code>숙소 위치는 찾기 쉽고 일반적인 한국의 반지하 숙소입니다.</code>              | <code>숙박시설의 위치는 쉽게 찾을 수 있고 한국의 대표적인 반지하 숙박시설입니다.</code> | <code>{'label': 3.7, 'real-label': 3.714285714285714, 'binary-label': 1}</code>  |
  | <code>klue-sts-v1_train_00001</code> | <code>policy-sampled</code>  | <code>위반행위 조사 등을 거부·방해·기피한 자는 500만원 이하 과태료 부과 대상이다.</code> | <code>시민들 스스로 자발적인 예방 노력을 한 것은 아산 뿐만이 아니었다.</code>      | <code>{'label': 0.0, 'real-label': 0.0, 'binary-label': 0}</code>                |
  | <code>klue-sts-v1_train_00002</code> | <code>paraKQC-sampled</code> | <code>회사가 보낸 메일은 이 지메일이 아니라 다른 지메일 계정으로 전달해줘.</code>       | <code>사람들이 주로 네이버 메일을 쓰는 이유를 알려줘</code>                 | <code>{'label': 0.3, 'real-label': 0.3333333333333333, 'binary-label': 0}</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### klue

* Dataset: [klue](https://huggingface.co/datasets/klue) at [349481e](https://huggingface.co/datasets/klue/tree/349481ec73fff722f88e0453ca05c77a447d967c)
* Size: 519 evaluation samples
* Columns: <code>guid</code>, <code>source</code>, <code>sentence1</code>, <code>sentence2</code>, and <code>labels</code>
* Approximate statistics based on the first 519 samples:
  |         | guid                                                                               | source                                                                           | sentence1                                                                         | sentence2                                                                         | labels             |
  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------|
  | type    | string                                                                             | string                                                                           | string                                                                            | string                                                                            | dict               |
  | details | <ul><li>min: 17 tokens</li><li>mean: 17.82 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 9.72 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.47 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 19.42 tokens</li><li>max: 58 tokens</li></ul> | <ul><li></li></ul> |
* Samples:
  | guid                               | source                      | sentence1                                                                                     | sentence2                                                                                | labels                                                                          |
  |:-----------------------------------|:----------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
  | <code>klue-sts-v1_dev_00000</code> | <code>airbnb-rtt</code>     | <code>무엇보다도 호스트분들이 너무 친절하셨습니다.</code>                                                         | <code>무엇보다도, 호스트들은 매우 친절했습니다.</code>                                                     | <code>{'label': 4.9, 'real-label': 4.857142857142857, 'binary-label': 1}</code> |
  | <code>klue-sts-v1_dev_00001</code> | <code>airbnb-sampled</code> | <code>주요 관광지 모두 걸어서 이동가능합니다.</code>                                                           | <code>위치는 피렌체 중심가까지 걸어서 이동 가능합니다.</code>                                                 | <code>{'label': 1.4, 'real-label': 1.428571428571429, 'binary-label': 0}</code> |
  | <code>klue-sts-v1_dev_00002</code> | <code>policy-sampled</code> | <code>학생들의 균형 있는 영어능력을 향상시킬 수 있는 학교 수업을 유도하기 위해 2018학년도 수능부터 도입된 영어 영역 절대평가는 올해도 유지한다.</code> | <code>영어 영역의 경우 학생들이 한글 해석본을 암기하는 문제를 해소하기 위해 2016학년도부터 적용했던 EBS 연계 방식을 올해도 유지한다.</code> | <code>{'label': 1.3, 'real-label': 1.285714285714286, 'binary-label': 0}</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 64
- `weight_decay`: 0.01
- `num_train_epochs`: 1
- `warmup_steps`: 100
- `load_best_model_at_end`: True

#### 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`: 64
- `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`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 100
- `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`: True
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step    | Training Loss | Validation Loss |
|:----------:|:-------:|:-------------:|:---------------:|
| 0.0137     | 10      | 2.9128        | -               |
| 0.0274     | 20      | 2.8336        | -               |
| 0.0411     | 30      | 2.8053        | -               |
| 0.0548     | 40      | 2.7919        | -               |
| 0.0685     | 50      | 2.7815        | -               |
| 0.0822     | 60      | 2.7722        | -               |
| 0.0959     | 70      | 2.7779        | -               |
| 0.1096     | 80      | 2.7768        | -               |
| 0.1233     | 90      | 2.7846        | -               |
| 0.1370     | 100     | 2.7747        | -               |
| 0.1507     | 110     | 2.7786        | -               |
| 0.1644     | 120     | 2.7719        | -               |
| 0.1781     | 130     | 2.7745        | -               |
| 0.1918     | 140     | 2.7747        | -               |
| 0.2055     | 150     | 2.7749        | -               |
| 0.2192     | 160     | 2.7715        | -               |
| 0.2329     | 170     | 2.7863        | -               |
| 0.2466     | 180     | 2.7732        | -               |
| 0.2603     | 190     | 2.7744        | -               |
| 0.2740     | 200     | 2.7754        | -               |
| 0.2877     | 210     | 2.7726        | -               |
| 0.3014     | 220     | 2.7718        | -               |
| 0.3151     | 230     | 2.774         | -               |
| 0.3288     | 240     | 2.7748        | -               |
| 0.3425     | 250     | 2.7708        | -               |
| 0.3562     | 260     | 2.7728        | -               |
| 0.3699     | 270     | 2.7746        | -               |
| 0.3836     | 280     | 2.7739        | -               |
| 0.3973     | 290     | 2.7721        | -               |
| 0.4110     | 300     | 2.7747        | -               |
| 0.4247     | 310     | 2.7746        | -               |
| 0.4384     | 320     | 2.7732        | -               |
| 0.4521     | 330     | 2.7739        | -               |
| 0.4658     | 340     | 2.7724        | -               |
| 0.4795     | 350     | 2.7736        | -               |
| 0.4932     | 360     | 2.7736        | -               |
| 0.5068     | 370     | 2.7735        | -               |
| 0.5205     | 380     | 2.7734        | -               |
| 0.5342     | 390     | 2.7726        | -               |
| 0.5479     | 400     | 2.7734        | -               |
| 0.5616     | 410     | 2.7726        | -               |
| 0.5753     | 420     | 2.7731        | -               |
| 0.5890     | 430     | 2.7735        | -               |
| 0.6027     | 440     | 2.7734        | -               |
| 0.6164     | 450     | 2.7741        | -               |
| 0.6301     | 460     | 2.7737        | -               |
| 0.6438     | 470     | 2.7717        | -               |
| 0.6575     | 480     | 2.7739        | -               |
| 0.6712     | 490     | 2.7727        | -               |
| **0.6849** | **500** | **2.7729**    | **4.129**       |
| 0.6986     | 510     | 2.7723        | -               |
| 0.7123     | 520     | 2.7729        | -               |
| 0.7260     | 530     | 2.7736        | -               |
| 0.7397     | 540     | 2.7725        | -               |
| 0.7534     | 550     | 2.7735        | -               |
| 0.7671     | 560     | 2.7737        | -               |
| 0.7808     | 570     | 2.7731        | -               |
| 0.7945     | 580     | 2.7733        | -               |
| 0.8082     | 590     | 2.7725        | -               |
| 0.8219     | 600     | 2.773         | -               |
| 0.8356     | 610     | 2.7729        | -               |
| 0.8493     | 620     | 2.7724        | -               |
| 0.8630     | 630     | 2.7719        | -               |
| 0.8767     | 640     | 2.7719        | -               |
| 0.8904     | 650     | 2.7735        | -               |
| 0.9041     | 660     | 2.7731        | -               |
| 0.9178     | 670     | 2.7716        | -               |
| 0.9315     | 680     | 2.7736        | -               |
| 0.9452     | 690     | 2.7734        | -               |
| 0.9589     | 700     | 2.7728        | -               |
| 0.9726     | 710     | 2.7721        | -               |
| 0.9863     | 720     | 2.7726        | -               |
| 1.0        | 730     | 2.6339        | -               |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.12.7
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0
- PyTorch: 2.5.1+cpu
- Accelerate: 1.1.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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