---
language:
- ko
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:11668
- loss:MultipleNegativesRankingLoss
base_model: klue/bert-base
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
datasets:
- klue/klue
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# 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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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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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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