metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:550152
  - loss:CosineSimilarityLoss
base_model: x2bee/KoModernBERT-base-mlm_v02
widget:
  - source_sentence: 한 남자가 다리가 허벅지에 있고 자전거 헬멧이 두 개 뒤에 있는 여자 옆에 앉아 있다.
    sentences:
      - 그 어린 소년은 야외에서 장난감 비행기를 날리고 있었다.
      - 사람들은 더 잘 보기 위해 서 있다.
      - 남자가 여자의 허벅지에 다리를 얹고 있다.
  - source_sentence: 도끼로 구조물을 무너뜨리는 남자.
    sentences:
      - 소년이 당나귀를 타고 있다.
      - 남자는 새들의 사진을 찍을 준비를 한다.
      - 한 남자가 수갑을 찬 채 감옥을 통과하고 있다.
  - source_sentence: 오토바이를 탄 스폰서를 입은 남자가 손을 들고 오토바이에 앉아 있다.
    sentences:
      - 남자는 오토바이 경주를 준비한다.
      - 한 여성이 라켓을 허공에 대고 라켓 볼 코트 모퉁이를 가로질러 걸어간다.
      - 어떤 남자들은 발레리나 옷을 입고 있다.
  - source_sentence: 경기를 볼 수 있는 스포츠 바.
    sentences:
      - 럭비를 하는 사람
      - 스포츠 바는 게임을 보기에 인기 있는 곳이다.
      - 두 여자 모두 들고 있는 안경으로 술을 마시고 있다.
  - source_sentence: 한 여자와 소년이 경찰 오토바이에 앉아 있다.
    sentences:
      - 여자와 소년이 밖에 있다.
      - 한 남자가 총으로 아기를 쐈다.
      - 한 남자가 물 위에 밧줄을 매고 있다.
datasets:
  - x2bee/Korean_NLI_dataset
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_euclidean
  - spearman_euclidean
  - pearson_manhattan
  - spearman_manhattan
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
model-index:
  - name: SentenceTransformer based on x2bee/KoModernBERT-base-mlm_v02
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts_dev
        metrics:
          - type: pearson_cosine
            value: 0.6374494482799764
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6328250180270107
            name: Spearman Cosine
          - type: pearson_euclidean
            value: 0.6326629869012427
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.612232056020112
            name: Spearman Euclidean
          - type: pearson_manhattan
            value: 0.6346199347508898
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.615448809374675
            name: Spearman Manhattan
          - type: pearson_dot
            value: 0.5941390124399774
            name: Pearson Dot
          - type: spearman_dot
            value: 0.5741507526998049
            name: Spearman Dot
          - type: pearson_max
            value: 0.6374494482799764
            name: Pearson Max
          - type: spearman_max
            value: 0.6328250180270107
            name: Spearman Max

SentenceTransformer based on x2bee/KoModernBERT-base-mlm_v02

This is a sentence-transformers model finetuned from x2bee/KoModernBERT-base-mlm_v02 on the korean_nli_dataset dataset. 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: x2bee/KoModernBERT-base-mlm_v02
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- korean_nli_dataset

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: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

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("x2bee/KoModernBERT_SBERT_compare_mlmlv5")
# Run inference
sentences = [
    '한 여자와 소년이 경찰 오토바이에 앉아 있다.',
    '여자와 소년이 밖에 있다.',
    '한 남자가 물 위에 밧줄을 매고 있다.',
]
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	0.6374
spearman_cosine	0.6328
pearson_euclidean	0.6327
spearman_euclidean	0.6122
pearson_manhattan	0.6346
spearman_manhattan	0.6154
pearson_dot	0.5941
spearman_dot	0.5742
pearson_max	0.6374
spearman_max	0.6328

Training Details

Training Dataset

korean_nli_dataset

Dataset: korean_nli_dataset at 51cc968
Size: 550,152 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 8 tokens
mean: 21.76 tokens
max: 76 tokens

min: 4 tokens
mean: 14.36 tokens
max: 44 tokens

min: 0.0
mean: 0.49
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 8 tokens mean: 21.76 tokens max: 76 tokens	min: 4 tokens mean: 14.36 tokens max: 44 tokens	min: 0.0 mean: 0.49 max: 1.0

Samples:

sentence1	sentence2	score
`몸에 맞지 않는 노란색 셔츠와 파란색 플래드 스커트를 입은 나이든 여성이 두 개의 통 옆에 앉아 있다.`	`여자가 역기를 들어올리고 있다.`	`0.0`
`갈색 코트를 입은 선글라스를 쓴 한 남성이 담배를 피우며 손님들이 길거리 스탠드에서 물건을 구입하자 코를 긁는다.`	`갈색 코트를 입은 선글라스를 쓴 청년이 담배를 피우며 손님들이 스테이트 스탠드에서 구매하고 있을 때 코를 긁는다.`	`0.5`
`소녀들은 물을 뿌리며 놀면서 킥킥 웃는다.`	`수도 본관이 고장나서 큰길이 범람했다.`	`0.0`

Loss: CosineSimilarityLoss with these parameters:

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

Evaluation Dataset

korean_nli_dataset

Dataset: korean_nli_dataset at 51cc968
Size: 550,152 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 4 tokens
mean: 21.88 tokens
max: 76 tokens

min: 5 tokens
mean: 14.14 tokens
max: 38 tokens

min: 0.0
mean: 0.52
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 4 tokens mean: 21.88 tokens max: 76 tokens	min: 5 tokens mean: 14.14 tokens max: 38 tokens	min: 0.0 mean: 0.52 max: 1.0

Samples:

sentence1	sentence2	score
`한 역사학자와 그의 친구는 연구를 위해 더 많은 화석을 찾기 위해 광산을 파고 있다.`	`역사가는 공부를 위해 친구와 함께 땅을 파고 있다.`	`0.5`
`소년은 회전목마에 도움을 받는다.`	`소년이 당나귀를 타고 있다.`	`0.0`
`세탁실에서 사색적인 포즈를 취하고 있는 남자.`	`한 남자가 파티오 밖에 있다.`	`0.0`

Loss: CosineSimilarityLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
gradient_accumulation_steps: 2
learning_rate: 1e-05
num_train_epochs: 2
warmup_ratio: 0.3
push_to_hub: True
hub_model_id: x2bee/KoModernBERT_SBERT_compare_mlmlv5
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 1e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.3
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: True
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: True
resume_from_checkpoint: None
hub_model_id: x2bee/KoModernBERT_SBERT_compare_mlmlv5
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
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
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	sts_dev_spearman_max
0	0	-	-	0.3994
0.0980	100	0.3216	-	-
0.1960	200	0.2019	-	-
0.2940	300	0.1451	-	-
0.3920	400	0.1327	-	-
0.4900	500	0.1231	-	-
0.5879	600	0.1138	-	-
0.6859	700	0.1091	-	-
0.7839	800	0.106	-	-
0.8819	900	0.1047	-	-
0.9799	1000	0.1029	-	-
1.0	1021	-	0.1003	0.6352
1.0774	1100	0.0999	-	-
1.1754	1200	0.0994	-	-
1.2734	1300	0.0989	-	-
1.3714	1400	0.0974	-	-
1.4694	1500	0.0975	-	-
1.5674	1600	0.0945	-	-
1.6654	1700	0.0933	-	-
1.7634	1800	0.0922	-	-
1.8613	1900	0.0928	-	-
1.9593	2000	0.0928	-	-
1.9985	2040	-	0.0955	0.6328

Framework Versions

Python: 3.11.10
Sentence Transformers: 3.3.1
Transformers: 4.48.0.dev0
PyTorch: 2.5.1+cu124
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}