---
base_model: jhgan/ko-sroberta-multitask
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:93961
- loss:CosineSimilarityLoss
widget:
- source_sentence: 송구함
  sentences:
  - 연민
  - 질색
  - 약오름
- source_sentence: 갈등
  sentences:
  - 죄송스러움
  - 흥분
  - 친애
- source_sentence: 동경
  sentences:
  - 울적함
  - 영광스러움
  - 연모
- source_sentence: 망설임
  sentences:
  - 역겨움
  - 황송함
  - 질투
- source_sentence: 겁남
  sentences:
  - 선호
  - 역정
  - 후련함
---

# SentenceTransformer based on jhgan/ko-sroberta-multitask

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jhgan/ko-sroberta-multitask](https://huggingface.co/jhgan/ko-sroberta-multitask). 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:** [jhgan/ko-sroberta-multitask](https://huggingface.co/jhgan/ko-sroberta-multitask) <!-- at revision ab957ae6a91e99c4cad36d52063a2a9cf1bf4419 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 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:

```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("sentence_transformers_model_id")
# Run inference
sentences = [
    '겁남',
    '선호',
    '후련함',
]
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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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 93,961 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                      | sentence_1                                                                      | label                                                            |
  |:--------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-----------------------------------------------------------------|
  | type    | string                                                                          | string                                                                          | float                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 3.96 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.92 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 0.13</li><li>mean: 0.31</li><li>max: 0.93</li></ul> |
* Samples:
  | sentence_0       | sentence_1       | label                           |
  |:-----------------|:-----------------|:--------------------------------|
  | <code>자만심</code> | <code>처량함</code> | <code>0.3356956711091378</code> |
  | <code>실망</code>  | <code>연민</code>  | <code>0.3267507385483022</code> |
  | <code>감동</code>  | <code>이뻐함</code> | <code>0.6483006382304752</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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `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

</details>

### Training Logs
| Epoch  | Step  | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0851 | 500   | 0.0114        |
| 0.1703 | 1000  | 0.0065        |
| 0.2554 | 1500  | 0.0048        |
| 0.3405 | 2000  | 0.0039        |
| 0.4257 | 2500  | 0.0032        |
| 0.5108 | 3000  | 0.003         |
| 0.5959 | 3500  | 0.0026        |
| 0.6811 | 4000  | 0.0026        |
| 0.7662 | 4500  | 0.0025        |
| 0.8514 | 5000  | 0.0022        |
| 0.9365 | 5500  | 0.0022        |
| 1.0216 | 6000  | 0.0021        |
| 1.1068 | 6500  | 0.0019        |
| 1.1919 | 7000  | 0.0017        |
| 1.2770 | 7500  | 0.0017        |
| 1.3622 | 8000  | 0.0017        |
| 1.4473 | 8500  | 0.0015        |
| 1.5324 | 9000  | 0.0015        |
| 1.6176 | 9500  | 0.0014        |
| 1.7027 | 10000 | 0.0014        |
| 1.7878 | 10500 | 0.0014        |
| 1.8730 | 11000 | 0.0012        |
| 1.9581 | 11500 | 0.0012        |
| 2.0432 | 12000 | 0.0013        |
| 2.1284 | 12500 | 0.0011        |
| 2.2135 | 13000 | 0.0011        |
| 2.2987 | 13500 | 0.0011        |
| 2.3838 | 14000 | 0.001         |
| 2.4689 | 14500 | 0.001         |
| 2.5541 | 15000 | 0.001         |
| 2.6392 | 15500 | 0.0009        |
| 2.7243 | 16000 | 0.0009        |
| 2.8095 | 16500 | 0.001         |
| 2.8946 | 17000 | 0.0009        |
| 2.9797 | 17500 | 0.0008        |
| 3.0649 | 18000 | 0.0008        |
| 3.1500 | 18500 | 0.0008        |
| 3.2351 | 19000 | 0.0007        |
| 3.3203 | 19500 | 0.0007        |
| 3.4054 | 20000 | 0.0008        |
| 3.4905 | 20500 | 0.0007        |
| 3.5757 | 21000 | 0.0007        |
| 3.6608 | 21500 | 0.0007        |
| 3.7460 | 22000 | 0.0007        |
| 3.8311 | 22500 | 0.0006        |
| 3.9162 | 23000 | 0.0006        |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.1.0
- Tokenizers: 0.19.1

## 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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