metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:10501
  - loss:CosineSimilarityLoss
base_model: klue/roberta-base
widget:
  - source_sentence: "이어 내년 4월부터 전자증명서는 건강보험자격확인서와 건강보험료 납부확인서 등 13종으로 늘어나고 사용처도 중앙부처는 물론 은행과 보험사 등으로도\_확대된다."
    sentences:
      - 4대 보험료 납부유예 및 감면조치는 4월에 납부해야 하는 3월 보험료부터 적용된다.
      - 그 외에는 모든 것에 만족했습니다.
      - 영하의 추운 날씨에는 장갑 잊지 말고 꼭 끼렴.
  - source_sentence: 야생동물 질병관리를 전담할 국가기관인 국립야생동물질병관리원이 올해 광주광역시 광산구 삼거동 일원에 개원 예정이다.
    sentences:
      - 위치는 좋으나 생활하기 좀 불편합니다.
      - 역에서 매우 가깝고, 쇼핑몰과 쇼핑몰 사이에는 숙소가 있습니다.
      - 추후 인도네시아와도 화상회의 및 온라인 세미나를 개최할 예정이다.
  - source_sentence: 작은 먹거리는 숙소 들어오게 전에 사는걸 추천해요.
    sentences:
      - 제일 최근에 스팸이 도착한 시간을 알려줘
      - 저는 당신이 숙소에 들어오기 전에 작은 음식을 사는 것을 추천합니다.
      - 올해는 황사 며칠동안 왔어?
  - source_sentence: 언제 만나는 것이 더 좋으실까요, 저녁 일곱시? 여덟시?
    sentences:
      - 이번주 일요일 약속 언제인지 궁금해.
      - 전자레인지와 가스레인지 중에 요리하고 싶은 걸로 알려줘
      - 뜨거운물말고 찬물로 세탁하고 더운물로 헹궈야될 것 같지 않아?
  - source_sentence: 지금까지 이탈리아 여행중에 가장 좋은 숙소였습니다
    sentences:
      - 지금까지 가본 호텔보다 더 좋은 숙소였습니다.
      - ‘코로나 아세안 대응기금’, ‘필수의료물품 비축제도’는 아세안+3가 함께 만들어낸 의미 있는 결과입니다.
      - 하루에 삼십분보단 한 시간 이상은 라디오 들어
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
co2_eq_emissions:
  emissions: 13.607209111220918
  energy_consumed: 0.0310949426904377
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 12th Gen Intel(R) Core(TM) i5-12400
  ram_total_size: 31.784194946289062
  hours_used: 0.154
  hardware_used: 1 x NVIDIA GeForce RTX 3060
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: 0.34770715374416716
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.35560473197486514
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.3673847148331908
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.36460670798564826
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.36074518113660536
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.35482778401649034
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.21251176317804726
            name: Pearson Dot
          - type: spearman_dot
            value: 0.20063256899469895
            name: Spearman Dot
          - type: pearson_max
            value: 0.3673847148331908
            name: Pearson Max
          - type: spearman_max
            value: 0.36460670798564826
            name: Spearman Max
          - type: pearson_cosine
            value: 0.9591996448990093
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.9206205258325634
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.9531423622288514
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.920406431818358
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.9532828644532834
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.9201721809761834
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.9482313505749467
            name: Pearson Dot
          - type: spearman_dot
            value: 0.9016036223997308
            name: Spearman Dot
          - type: pearson_max
            value: 0.9591996448990093
            name: Pearson Max
          - type: spearman_max
            value: 0.9206205258325634
            name: Spearman Max

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 tokens
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 = [
    '지금까지 이탈리아 여행중에 가장 좋은 숙소였습니다',
    '지금까지 가본 호텔보다 더 좋은 숙소였습니다.',
    '‘코로나 아세안 대응기금’, ‘필수의료물품 비축제도’는 아세안+3가 함께 만들어낸 의미 있는 결과입니다.',
]
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	0.3477
spearman_cosine	0.3556
pearson_manhattan	0.3674
spearman_manhattan	0.3646
pearson_euclidean	0.3607
spearman_euclidean	0.3548
pearson_dot	0.2125
spearman_dot	0.2006
pearson_max	0.3674
spearman_max	0.3646

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9592
spearman_cosine	0.9206
pearson_manhattan	0.9531
spearman_manhattan	0.9204
pearson_euclidean	0.9533
spearman_euclidean	0.9202
pearson_dot	0.9482
spearman_dot	0.9016
pearson_max	0.9592
spearman_max	0.9206

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: 7 tokens
mean: 20.14 tokens
max: 59 tokens

min: 7 tokens
mean: 19.71 tokens
max: 68 tokens

min: 0.0
mean: 0.44
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 7 tokens mean: 20.14 tokens max: 59 tokens	min: 7 tokens mean: 19.71 tokens max: 68 tokens	min: 0.0 mean: 0.44 max: 1.0

Samples:

sentence_0	sentence_1	label
`가스레인지 사용하지 않도록 유의해주세요`	`가스레인지 사용은 삼가주세요`	`0.74`
`이번주하고 다음주 중에 언제 동기 모임이 있어?`	`언제 자연어처리 학회 논문 접수가 마감되나요?`	`0.02`
`또한 각 부처는 생활방역 관련 업무를 종합·체계적으로 수행하기 위해 기관별로 생활방역 전담팀(TF)을 구성한다.`	`또한 생활방지와 관련된 업무를 종합적이고 체계적으로 수행하기 위하여 각 부서별로 생활방역 전담 태스크포스(TF)를 구성하여야 합니다.`	`0.72`

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}
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	spearman_max
0	0	-	0.3646
0.7610	500	0.0278	-
1.0	657	-	0.9187
1.5221	1000	0.0085	0.9117
2.0	1314	-	0.9201
2.2831	1500	0.0044	-
3.0	1971	-	0.9186
3.0441	2000	0.0034	0.9199
3.8052	2500	0.0027	-
4.0	2628	-	0.9206

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.031 kWh
Carbon Emitted: 0.014 kg of CO2
Hours Used: 0.154 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3060
CPU Model: 12th Gen Intel(R) Core(TM) i5-12400
RAM Size: 31.78 GB

Framework Versions

Python: 3.12.4
Sentence Transformers: 3.2.1
Transformers: 4.45.2
PyTorch: 2.4.0+cu121
Accelerate: 0.29.3
Datasets: 2.19.0
Tokenizers: 0.20.1

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",
}