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klue_bert-base_finetuning

Sentence Similarity
sentence-transformers
Safetensors
Korean
bert
feature-extraction
Generated from Trainer
dataset_size:11668
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Inference Endpoints
Model card Files Community
klue_bert-base_finetuning / README.md
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metadata
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 model finetuned from klue/bert-base on the 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
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 dimensions
  • Similarity Function: Cosine Similarity
  • Training Dataset:
  • Language: ko

Model Sources

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

Training Details

Training Dataset

klue

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

Evaluation Dataset

klue

  • Dataset: klue at 349481e
  • Size: 519 evaluation samples
  • Columns: guid, source, sentence1, sentence2, and labels
  • Approximate statistics based on the first 519 samples:
    guid source sentence1 sentence2 labels
    type string string string string dict
    details
    • min: 17 tokens
    • mean: 17.82 tokens
    • max: 18 tokens
    • min: 7 tokens
    • mean: 9.72 tokens
    • max: 11 tokens
    • min: 6 tokens
    • mean: 19.47 tokens
    • max: 61 tokens
    • min: 7 tokens
    • mean: 19.42 tokens
    • max: 58 tokens
  • Samples:
    guid source sentence1 sentence2 labels
    klue-sts-v1_dev_00000 airbnb-rtt 무엇보다도 호스트분들이 너무 친절하셨습니다. 무엇보다도, 호스트들은 매우 친절했습니다. {'label': 4.9, 'real-label': 4.857142857142857, 'binary-label': 1}
    klue-sts-v1_dev_00001 airbnb-sampled 주요 관광지 모두 걸어서 이동가능합니다. 위치는 피렌체 중심가까지 걸어서 이동 가능합니다. {'label': 1.4, 'real-label': 1.428571428571429, 'binary-label': 0}
    klue-sts-v1_dev_00002 policy-sampled 학생들의 균형 있는 영어능력을 향상시킬 수 있는 학교 수업을 유도하기 위해 2018학년도 수능부터 도입된 영어 영역 절대평가는 올해도 유지한다. 영어 영역의 경우 학생들이 한글 해석본을 암기하는 문제를 해소하기 위해 2016학년도부터 적용했던 EBS 연계 방식을 올해도 유지한다. {'label': 1.3, 'real-label': 1.285714285714286, 'binary-label': 0}
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "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

Click to expand
  • 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

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 

@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 

@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}
}