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:574408
  - loss:MultipleNegativesRankingLoss
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: 한 여성이 뜨거운 물 냄비에 음식을 넣고 있다.
    sentences:
      - 한 여성이 고기를 튀기고 있다.
      - '세계 브리핑 아시아 : 미얀마 : 치명적인 반 무슬림 폭력 사태가 폭발했다.'
      - 아기가 잠들고 있다.
  - source_sentence: 러시아 비행기 추락 사고로 사망자 수 증가
    sentences:
      - 이탈리아 코치 추락으로 사망자 수가 39명으로 증가
      - 헬리콥터 펍 추락 후 사망 두려워하는 세 명
      - 흑백 개는 입에 갈색 물체를 물고 헤엄친다.
  - source_sentence: 거울에 비친 한 여자가 옆에 있는 다른 여자와 함께 카메라를 외면하고 앉아 있었다.
    sentences:
      - 보도 위를 걷는 여자와 함께 길을 건너는 흑인 여성의 뒷모습.
      - 여자가 거울을 응시하고 있다
      - 한 여성이 햄버거를 응시하고 있다
  - source_sentence: 스키를 탄 사람이 공중으로 뛰어오른다.
    sentences:
      - 밖에 한 남자가 있다.
      - 그는 눈 위를 달리고 있다.
      - 그는 스키를 타고 공중으로 뛰어올랐다.
  - source_sentence: 내 옆이나 내 뒤에, 경외심을 느끼며 언더톤으로 말했다.
    sentences:
      - 그는 나와는 거리가 멀었다.
      - FSIS는 접수된 의견과 기관의 요구 사항 재평가를 고려하여 연간 부담을 8,053,319시간으로 줄였습니다.
      - 그는 나와 가까웠다.
model-index:
  - name: SentenceTransformer based on klue/roberta-base
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.8657393669442817
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.866343037897214
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8568809906017532
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8621129068016818
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8569880055215549
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8620159980137003
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.8382433069709427
            name: Pearson Dot
          - type: spearman_dot
            value: 0.8359003576467027
            name: Spearman Dot
          - type: pearson_max
            value: 0.8657393669442817
            name: Pearson Max
          - type: spearman_max
            value: 0.866343037897214
            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: 128 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': 128, '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 = [
    '내 옆이나 내 뒤에, 경외심을 느끼며 언더톤으로 말했다.',
    '그는 나와 가까웠다.',
    '그는 나와는 거리가 멀었다.',
]
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

Dataset: sts-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8657
spearman_cosine	0.8663
pearson_manhattan	0.8569
spearman_manhattan	0.8621
pearson_euclidean	0.857
spearman_euclidean	0.862
pearson_dot	0.8382
spearman_dot	0.8359
pearson_max	0.8657
spearman_max	0.8663

Training Details

Training Datasets

Unnamed Dataset

Size: 568,640 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: 4 tokens mean: 19.2 tokens max: 128 tokens	min: 3 tokens mean: 18.3 tokens max: 93 tokens	min: 4 tokens mean: 14.64 tokens max: 54 tokens

Samples:

sentence_0	sentence_1	sentence_2
`발생 부하가 함께 5% 적습니다.`	`발생 부하의 5% 감소와 함께 11.`	`발생 부하가 5% 증가합니다.`
`어떤 행사를 위해 음식과 옷을 배급하는 여성들.`	`여성들은 음식과 옷을 나눠줌으로써 난민들을 돕고 있다.`	`여자들이 사막에서 오토바이를 운전하고 있다.`
`어린 아이들은 그 지식을 얻을 필요가 있다.`	`응, 우리 젊은이들 중 많은 사람들이 그걸 배워야 할 것 같아.`	`젊은 사람들은 배울 필요가 없다.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Unnamed Dataset

Size: 5,768 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: 4 tokens
mean: 17.14 tokens
max: 53 tokens

min: 3 tokens
mean: 17.21 tokens
max: 56 tokens

min: 0.0
mean: 0.54
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 4 tokens mean: 17.14 tokens max: 53 tokens	min: 3 tokens mean: 17.21 tokens max: 56 tokens	min: 0.0 mean: 0.54 max: 1.0

Samples:

sentence_0	sentence_1	label
`식품의약품관리국은 셔디 리서치를 인용하여 2001년 IMClone의 에르비턱스 판매 신청을 거절했다.`	`미국 식품의약국은 2001년 12월 이 재판이 부실하게 진행되었다고 말하면서 이클론의 원래 신청을 거부했다.`	`0.5599999999999999`
`이슬람 주도의 이집트 , 콥트 교회 이름은 새로운 교황이다`	`이집트 기독교인들은 새로운 교황을 선택한다`	`0.64`
`시리아 주지사는 공격을 중단하지 않는다`	`시리아 야당, '학살' 보고`	`0.2`

Loss: CosineSimilarityLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
num_train_epochs: 5
batch_sampler: no_duplicates
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: 8
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 5
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	sts-dev_spearman_max
0.3467	500	0.419	-
0.6935	1000	0.3032	0.8516
1.0007	1443	-	0.8605
1.0395	1500	0.2705	-
1.3863	2000	0.1368	0.8509
1.7330	2500	0.0906	-
2.0007	2886	-	0.8663

Framework Versions

Python: 3.11.9
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.2.2+cu121
Accelerate: 0.31.0
Datasets: 2.20.0
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",
}

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}
}