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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:10501
  - loss:CosineSimilarityLoss
base_model: klue/roberta-base
widget:
  - source_sentence: 조명등 낮에 키려고 하지마
    sentences:
      - 아침 샤워는 꼭 찬물 말고 더운물로 해줘
      - 일단 숙소는 4인가족이 머무르기 충분한공간입니다
      - 올드 시티의 그랜드 마스터 궁전, 고고학 박물관 등을 주로 구경한다면 최고의 위치입니다.
  - source_sentence: 요즘 네가 즐겨 보는 뉴스 채널이 뭐야?
    sentences:
      - 농협이랑 신한 중 청구서를 달마다 메일로 보내게 해둔 곳이 어디지?
      - 쓰레기,설거지,빨래를 처리하기에도 아주 좋았구요
      - 예능말고 네가 좋아하는 뉴스 채널로 알려줘요
  - source_sentence: 일인분 밥 짓는 방법 좀 알려줘
    sentences:
      - 올해 추석 연휴 날짜가 며칠부터 며칠까지에요?
      - 음악 들을 거면 스피커말고 헤드폰으로 듣지 그래
      - 더울 때 오래된 음식은 먹지 않도록 해.
  - source_sentence: 60년 전, 이 땅에 위대한 민주주의의 역사를 심어주신 주역들께 깊은 존경과 감사 인사를 드립니다.
    sentences:
      - 60년 전, 저는 이 땅에 민주주의의 위대한 역사를 창조한 사람들에게 깊은 존경과 감사를 표하고 싶습니다.
      - 호스트와 양호한 연결 지점입니다.
      - 골프치러 내일 만나기로 한 데가 어디야?
  - source_sentence: 삼월 메일은 삭제되어선 안돼
    sentences:
      - 중요한 메일이니 스팸으로 분류하지 말고 삭제금지 설정해줘
      - 무드등말고 백열등 켜주세요!
      - 나한테 침실에 무드등 밝기 적당한 정도 좀 알려줄래?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on klue/roberta-base
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: pearson_cosine
            value: 0.9617068435868263
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.9210402694151972
            name: Spearman Cosine

SentenceTransformer based on klue/roberta-base

This is a sentence-transformers model finetuned from klue/roberta-base. 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/roberta-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) 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:

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 = [
    '삼월 메일은 삭제되어선 안돼',
    '중요한 메일이니 스팸으로 분류하지 말고 삭제금지 설정해줘',
    '무드등말고 백열등 켜주세요!',
]
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]

Evaluation

Metrics

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9617
spearman_cosine	0.921

Training Details

Training Dataset

Unnamed Dataset

Size: 10,501 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 6 tokens
mean: 19.36 tokens
max: 60 tokens

min: 7 tokens
mean: 18.96 tokens
max: 65 tokens

min: 0.0
mean: 0.44
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 6 tokens mean: 19.36 tokens max: 60 tokens	min: 7 tokens mean: 18.96 tokens max: 65 tokens	min: 0.0 mean: 0.44 max: 1.0

Samples:

sentence_0	sentence_1	label
`아울러 가명처리 등 개인정보 보호 기술 개발과 RD를 위한 협력을 강화하고 지역정보보호센터 등을 활용한 개인정보 보호 전문 인력양성 및 중소기업 개인정보 보호 강화도 추진한다.`	`이와 함께 가명처리, RD 등 개인정보보호 기술개발 협력을 강화하고, 지역정보보호센터를 활용한 개인정보보호 전문가와 중소기업을 육성할 계획입니다.`	`0.6599999999999999`
`다음 메일은 사용자의 메일을 최대 몇 기가까지 저장하죠?`	`다음 메일을 사용할 때 메일이 저장되는 최대 용량은 얼마죠?`	`0.7`
`그들이 당신을 데리러 지하철역으로 올 것입니다.`	`그들의 조언과 도움이 없었다면, 이렇게까지 좋은 여행을 할수없었을것입니다.`	`0.02`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 4
multi_dataset_batch_sampler: round_robin

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: 16
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: 4
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}
tp_size: 0
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
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: round_robin

Training Logs

Epoch	Step	Training Loss	spearman_cosine
0.7610	500	0.0281	-
1.0	657	-	0.9101
1.5221	1000	0.008	0.9185
2.0	1314	-	0.9185
2.2831	1500	0.0049	-
3.0	1971	-	0.9201
3.0441	2000	0.0034	0.9207
3.8052	2500	0.0026	-
4.0	2628	-	0.9210

Framework Versions

Python: 3.9.15
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Accelerate: 1.6.0
Datasets: 3.5.0
Tokenizers: 0.21.1

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",
}