README.md

metadata

base_model: klue/roberta-base
datasets: []
language: []
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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:10501
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: 기업은 생존 문제에 직면하고, 자영업자와 소상공인의 고통은 이루 말할 수 없을 정도입니다.
    sentences:
      - 자유무역은 기업이 서로를 신뢰하고, 미래의 불확실성을 낮추는 안전장치입니다.
      - >-
        국가 임상연구 승인, 시행기관 지정, 장기 추적조사 등 안전관리체계를 구축하고 치료 개발 및 임상연구 수행을 위한 RD 투자를
        확대합니다.
      - 중심가와 거리가 조금 먼 점 빼고는 정말 모든게 너무 좋았던 숙소입니다!
  - source_sentence: 타이페이를 다시 간다면 여기 또 올거예요.
    sentences:
      - 사진으로 봤던것보다 훨씬 더 좋았습니다
      - 겨울에 난방 온도 이십오도 이상으로 올리지마라고 경고했어
      - 만약 내가 다시 타이페이에 간다면, 나는 여기에 다시 올 것입니다.
  - source_sentence: 호주의 좋은 가정집에서 묵는 느낌이었어요.
    sentences:
      - >-
        어린이 교통사고 위험지역에 CCTV 2087대, 신호등 2146개를 올해 상반기 중으로 설치하고 옐로카펫과 노란발자국 등을 올해
        하반기에 초등학교 100곳에 시범 설치한다.
      - 호주에 있는 좋은 집에서 지내는 것 같았어요.
      - 그러나 호텔업계 노사가 가장 어려운 시기에, 가장 모범적으로 함께 마음을 모았습니다.
  - source_sentence: 그들덕분에 우리는 4일간 편안히 쉴 수 있었습니다.
    sentences:
      - 그들 덕분에, 우리는 4일 동안 쉴 수 있었어요.
      - 주변에 두 개의 지하철역이 있습니다. 큰 공원, 큰 슈퍼마켓, 그리고 편의점이 있습니다.
      - 방은 쾌적하고 에어컨도 아주 잘 나와요.
  - source_sentence: 테라스에서 봤던 뷰와 그곳에서 먹었던 식사가 그리울 것 같아요.
    sentences:
      - 테라스에서 본 풍경과 거기서 먹었던 음식이 그리울 것 같아요.
      - 이쪽 주변에서 여행할 계획이라면 추천합니다!
      - 저희 할아버지는 매우 친절하고 친절하십니다.
co2_eq_emissions:
  emissions: 7.379414346751554
  energy_consumed: 0.016863301234344347
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700
  ram_total_size: 62.56697463989258
  hours_used: 0.057
  hardware_used: 1 x NVIDIA GeForce RTX 4090
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: 0.34770704341988723
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.35560473197486514
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.3673846313946801
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.36460670798564826
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.3607451203867209
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.35482778401649034
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.21251167982120983
            name: Pearson Dot
          - type: spearman_dot
            value: 0.20063256899469895
            name: Spearman Dot
          - type: pearson_max
            value: 0.3673846313946801
            name: Pearson Max
          - type: spearman_max
            value: 0.36460670798564826
            name: Spearman Max
          - type: pearson_cosine
            value: 0.961968864970919
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.9196100863981246
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.9530332430579778
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.9186168431687389
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.9532923011007042
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.9190754386835427
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.9493179101338206
            name: Pearson Dot
          - type: spearman_dot
            value: 0.8999468521869318
            name: Spearman Dot
          - type: pearson_max
            value: 0.961968864970919
            name: Pearson Max
          - type: spearman_max
            value: 0.9196100863981246
            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 = [
    '테라스에서 봤던 뷰와 그곳에서 먹었던 식사가 그리울 것 같아요.',
    '테라스에서 본 풍경과 거기서 먹었던 음식이 그리울 것 같아요.',
    '이쪽 주변에서 여행할 계획이라면 추천합니다!',
]
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	0.962
spearman_cosine	0.9196
pearson_manhattan	0.953
spearman_manhattan	0.9186
pearson_euclidean	0.9533
spearman_euclidean	0.9191
pearson_dot	0.9493
spearman_dot	0.8999
pearson_max	0.962
spearman_max	0.9196

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: 20.23 tokens max: 64 tokens	min: 5 tokens mean: 19.94 tokens max: 63 tokens	min: 0.0 mean: 0.44 max: 1.0

• Samples:

  sentence_0	sentence_1	label
  지하철 역 내려서 1분정도의 아주 가까운 거리입니다.	지하철역에서 1분 정도 아주 가까운 거리입니다.	0.86
  그것빼곤 2인여행자들에게는 좋은숙소에요!	계단이 많다는거 빼곤 완벽한 숙소에요!	0.27999999999999997
  이어 현금이 286만 가구(13.2%) 1조3007억원, 선불카드가 75만 가구(3.5%) 4990억원, 지역사랑상품권은 63만 가구(2.9%) 4171억원으로 각각 집계됐다.	이어 현금 286만 가구(13.2%), 현금 1조337억 원, 선불카드 75만 가구(3.5%), 4990억 원, 지역사랑상품권 63만 가구(2.9%), 4171억 원 순이었습니다.	0.86

• 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
• 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	0.0283	-
1.0	657	-	0.9075
1.5221	1000	0.0082	0.9148
2.0	1314	-	0.9148
2.2831	1500	0.0047	-
3.0	1971	-	0.9180
3.0441	2000	0.0034	0.9168
3.8052	2500	0.0027	-
4.0	2628	-	0.9196

Environmental Impact

Carbon emissions were measured using CodeCarbon.

• Energy Consumed: 0.017 kWh
• Carbon Emitted: 0.007 kg of CO2
• Hours Used: 0.057 hours

Training Hardware

• On Cloud: No
• GPU Model: 1 x NVIDIA GeForce RTX 4090
• CPU Model: 13th Gen Intel(R) Core(TM) i7-13700
• RAM Size: 62.57 GB

Framework Versions

• Python: 3.9.0
• Sentence Transformers: 3.0.1
• Transformers: 4.44.1
• PyTorch: 2.3.1+cu121
• Accelerate: 0.33.0
• Datasets: 2.19.1
• Tokenizers: 0.19.1

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",
}