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---
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](https://www.SBERT.net) model finetuned from [x2bee/KoModernBERT-base-mlm_v02](https://huggingface.co/x2bee/KoModernBERT-base-mlm_v02) on the [korean_nli_dataset](https://huggingface.co/datasets/x2bee/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](https://huggingface.co/x2bee/KoModernBERT-base-mlm_v02) <!-- at revision e70a0396ecbe3f187762e0cb9ee5952fa42e6bb9 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [korean_nli_dataset](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
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]
```
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts_dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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** |
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## Training Details
### Training Dataset
#### korean_nli_dataset
* Dataset: [korean_nli_dataset](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset) at [51cc968](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset/tree/51cc968560f9600460d8af859c5b9a94849790a4)
* Size: 550,152 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 8 tokens</li><li>mean: 21.76 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.36 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:-----------------|
| <code>몸에 맞지 않는 노란색 셔츠와 파란색 플래드 스커트를 입은 나이든 여성이 두 개의 통 옆에 앉아 있다.</code> | <code>여자가 역기를 들어올리고 있다.</code> | <code>0.0</code> |
| <code>갈색 코트를 입은 선글라스를 쓴 한 남성이 담배를 피우며 손님들이 길거리 스탠드에서 물건을 구입하자 코를 긁는다.</code> | <code>갈색 코트를 입은 선글라스를 쓴 청년이 담배를 피우며 손님들이 스테이트 스탠드에서 구매하고 있을 때 코를 긁는다.</code> | <code>0.5</code> |
| <code>소녀들은 물을 뿌리며 놀면서 킥킥 웃는다.</code> | <code>수도 본관이 고장나서 큰길이 범람했다.</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### korean_nli_dataset
* Dataset: [korean_nli_dataset](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset) at [51cc968](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset/tree/51cc968560f9600460d8af859c5b9a94849790a4)
* Size: 550,152 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 4 tokens</li><li>mean: 21.88 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.14 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------|:------------------------------------------|:-----------------|
| <code>한 역사학자와 그의 친구는 연구를 위해 더 많은 화석을 찾기 위해 광산을 파고 있다.</code> | <code>역사가는 공부를 위해 친구와 함께 땅을 파고 있다.</code> | <code>0.5</code> |
| <code>소년은 회전목마에 도움을 받는다.</code> | <code>소년이 당나귀를 타고 있다.</code> | <code>0.0</code> |
| <code>세탁실에서 사색적인 포즈를 취하고 있는 남자.</code> | <code>한 남자가 파티오 밖에 있다.</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"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
<details><summary>Click to expand</summary>
- `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
</details>
### 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
```bibtex
@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",
}
```
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