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multifuly
/
cert-contrasitive-embedding

Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
dense
Generated from Trainer
dataset_size:1556
loss:TripletLoss
text-embeddings-inference
Model card Files
xet
Community

SentenceTransformer based on jhgan/ko-sroberta-multitask

This is a sentence-transformers model finetuned from jhgan/ko-sroberta-multitask. 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: jhgan/ko-sroberta-multitask
  • Maximum Sequence Length: 256 tokens
  • Output Dimensionality: 768 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': '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:

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("sentence_transformers_model_id")
# Run inference
sentences = [
    '전공: 경영정보학과 학년: 4학년 희망직무: 사무 관심 자격증: 없음 취득 자격증: 없음 목적: 취업 준비',
    '[자격증명: 컴퓨터활용능력 2급] 자격증명: 컴퓨터활용능력 2급 | 유형: 국가기술자격 | 분야: 사업관리 | NCS분류: 사업관리 | 관련전공: IT학부, AI・데이터공학부, AI・빅데이터학과, AI・컴퓨터공학과, AI빅데이터전공',
    '[자격증명: 종자기사] 자격증명: 종자기사 | 유형: 국가기술자격 | 분야: 농업 | NCS분류: 농림어업 | 시행기관: 한국산업인력공단 | 등급: 기사 | 관련전공: 식량자원과학과, 식량생명공학과, 생물자원과학부, 자원공학과, 식의약자원개발학과',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.3410, -0.1606],
#         [ 0.3410,  1.0000, -0.0415],
#         [-0.1606, -0.0415,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

  • Size: 1,556 training samples
  • Columns: sentence_0, sentence_1, and sentence_2
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1 sentence_2
    type string string string
    details
    • min: 5 tokens
    • mean: 25.75 tokens
    • max: 58 tokens
    • min: 35 tokens
    • mean: 94.18 tokens
    • max: 119 tokens
    • min: 48 tokens
    • mean: 83.42 tokens
    • max: 110 tokens
  • Samples:
    sentence_0 sentence_1 sentence_2
    전공: 컴퓨터공학과 학년: 3학년 희망직무: 데이터분석 관심 자격증: 정보처리기사 취득 자격증: 없음 목적: 직무 관련 자격증 추천 [자격증명: SQL개발자(SQLD)] 자격증명: SQL개발자(SQLD) | 유형: 국가민간자격 | 분야: 정보기술 | NCS분류: 정보통신 | 시행기관: 한국데이터산업진흥원 | 등급: 1 | 관련전공: IT미디어공학과, IT·디자인융합학부, ICT융합콘텐츠전공, 인공지능융합전공, 인공지능소프트웨어전공 [자격증명: 측량및지형공간정보기술사] 자격증명: 측량및지형공간정보기술사 | 유형: 국가기술자격 | 분야: 정보기술 | NCS분류: 정보통신 | 등급: 기술사
    IT 쪽 취업하고 싶어 [자격증명: SQL개발자(SQLD)] 자격증명: SQL개발자(SQLD) | 유형: 국가민간자격 | 분야: 정보기술 | NCS분류: 정보통신 | 시행기관: 한국데이터산업진흥원 | 등급: 1 | 관련전공: IT미디어공학과, IT·디자인융합학부, ICT융합콘텐츠전공, 인공지능융합전공, 인공지능소프트웨어전공 [자격증명: 종자기사] 자격증명: 종자기사 | 유형: 국가기술자격 | 분야: 농업 | NCS분류: 농림어업 | 시행기관: 한국산업인력공단 | 등급: 기사 | 관련전공: 식량자원과학과, 식량생명공학과, 생물자원과학부, 자원공학과, 식의약자원개발학과
    전공: 산업데이터공학 학년: 2학년 희망직무: 개발 관심 자격증: 없음 취득 자격증: 없음 목적: 직무 관련 자격증 추천 [자격증명: 데이터분석준전문가(ADsP)] 자격증명: 데이터분석준전문가(ADsP) | 유형: 국가민간자격 | 분야: 정보기술 | NCS분류: 정보통신 | 시행기관: (재)한국데이터진흥원 | 등급: 1 | 관련전공: 모바일시스템공학과, 모바일융합공학과, 전자공학부모바일공학전공, 전자정보공학부, IT융합학부 [자격증명: 측량및지형공간정보기사] 자격증명: 측량및지형공간정보기사 | 유형: 국가기술자격 | 분야: 정보기술 | NCS분류: 정보통신 | 시행기관: 한국산업인력공단 | 등급: 기사 | 관련전공: 지리학과, 지리학과(자연계열), 지적학전공, 지적학과, 위치정보시스템학과
  • Loss: TripletLoss with these parameters:
    {
    "distance_metric": "TripletDistanceMetric.COSINE",
    "triplet_margin": 0.2
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • num_train_epochs: 1
  • multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • 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: 1
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: None
  • warmup_ratio: None
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • enable_jit_checkpoint: False
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • use_cpu: False
  • seed: 42
  • data_seed: None
  • bf16: False
  • fp16: False
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: -1
  • ddp_backend: None
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • parallelism_config: None
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch_fused
  • optim_args: None
  • group_by_length: False
  • length_column_name: length
  • project: huggingface
  • trackio_space_id: trackio
  • 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
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • hub_revision: None
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_for_metrics: []
  • eval_do_concat_batches: True
  • auto_find_batch_size: False
  • full_determinism: False
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • include_num_input_tokens_seen: no
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • use_liger_kernel: False
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: True
  • use_cache: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin
  • router_mapping: {}
  • learning_rate_mapping: {}

Framework Versions

  • Python: 3.12.12
  • Sentence Transformers: 5.2.3
  • Transformers: 5.0.0
  • PyTorch: 2.10.0+cu128
  • Accelerate: 1.12.0
  • Datasets: 4.0.0
  • Tokenizers: 0.22.2

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",
}

TripletLoss 

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
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