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---
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:574418
- loss:MultipleNegativesRankingLoss
- loss:CosineSimilarityLoss
widget:
- source_sentence: 두 마리의 개가 해변을 달려 내려갔다.
sentences:
- '아프가니스탄 폭탄 공격으로 적어도 18명이 사망했다 : 관리들'
- 해변에서 달리는 개 두 마리
- 눈 속에서 노는 개 네 마리
- source_sentence: 한 여성이 남자와 게임을 하고 있다.
sentences:
- 한 남자가 피아노를 치고 있다.
- 기차 마당의 선로에 앉아 있는 기차
- 에콰도르는 아직 어샌지의 망명을 결정하지 않았다.
- source_sentence: 젊은 남자는 화려한 액세서리를 가지고 있다.
sentences:
- 다채로운 꽃무늬 리와 다채로운 팔찌를 든 청년이 깃발을 들고 있다.
- 한 남자가 서핑 보드 위에 있다.
- 화려한 옷을 입은 젊은이가 총을 들고 있다.
- source_sentence: 그들은 서로 가까이 있지 않다.
sentences:
- 그리고 나는 내 돈을 돌봐야 했다. 나는 내 자신의 생명보험에 지불하는 내 자신의 당좌예금 계좌를 가지고 있고, 내가 무슨 뜻인지조차 모르는
많은 아이들을 알고 있다. 나는 그들에게 내가 내 생명보험에 지불한다고 말하고 그들의 입이 그냥 바닥에 떨어진다.
- 그들은 샤토와 매우 가깝다.
- 그들은 샤토와 서로 어느 정도 떨어져 있다.
- source_sentence: 딱딱한 모자를 쓴 남자가 건물 프레임 앞에 주차된 빨간 트럭의 침대를 쳐다본다.
sentences:
- 남자가 자고 있다.
- 2. 알코올문제의 규모와 다른 방법으로 치료를 받지 않을 수 있는 환자를 식별할 수 있는 응급부서의 능력을 감안할 때, 자금조달기관은 ED의
알코올문제 연구에 높은 우선순위를 두어야 한다.
- 한 남자가 트럭을 보고 있다.
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: 0.8610601836184975
name: Pearson Cosine
- type: spearman_cosine
value: 0.8634197198921464
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8544694872859289
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8590618059127191
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8548774854000663
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8593350742997908
name: Spearman Euclidean
- type: pearson_dot
value: 0.8331606248521055
name: Pearson Dot
- type: spearman_dot
value: 0.8324300838050938
name: Spearman Dot
- type: pearson_max
value: 0.8610601836184975
name: Pearson Max
- type: spearman_max
value: 0.8634197198921464
name: Spearman Max
---
# SentenceTransformer based on klue/roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [klue/roberta-base](https://huggingface.co/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](https://huggingface.co/klue/roberta-base) <!-- at revision 02f94ba5e3fcb7e2a58a390b8639b0fac974a8da -->
- **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 = [
'딱딱한 모자를 쓴 남자가 건물 프레임 앞에 주차된 빨간 트럭의 침대를 쳐다본다.',
'한 남자가 트럭을 보고 있다.',
'남자가 자고 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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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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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 | 0.8611 |
| spearman_cosine | 0.8634 |
| pearson_manhattan | 0.8545 |
| spearman_manhattan | 0.8591 |
| pearson_euclidean | 0.8549 |
| spearman_euclidean | 0.8593 |
| pearson_dot | 0.8332 |
| spearman_dot | 0.8324 |
| pearson_max | 0.8611 |
| **spearman_max** | **0.8634** |
<!--
## Bias, Risks and Limitations
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## Training Details
### Training Datasets
#### Unnamed Dataset
* Size: 568,640 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 19.18 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.31 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.58 tokens</li><li>max: 54 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:----------------------------------------|:-------------------------------------------------|:--------------------------------------|
| <code>발생 부하가 함께 5% 적습니다.</code> | <code>발생 부하의 5% 감소와 함께 11.</code> | <code>발생 부하가 5% 증가합니다.</code> |
| <code>어떤 행사를 위해 음식과 옷을 배급하는 여성들.</code> | <code>여성들은 음식과 옷을 나눠줌으로써 난민들을 돕고 있다.</code> | <code>여자들이 사막에서 오토바이를 운전하고 있다.</code> |
| <code>어린 아이들은 그 지식을 얻을 필요가 있다.</code> | <code>응, 우리 젊은이들 중 많은 사람들이 그걸 배워야 할 것 같아.</code> | <code>젊은 사람들은 배울 필요가 없다.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### Unnamed Dataset
* Size: 5,778 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: 16.98 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.88 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:---------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|:--------------------------------|
| <code>다우존스 산업평균지수는 9011.53으로 98.32, 즉 약 1.1% 하락했다.</code> | <code>다우존스 산업평균지수는 9,011.53으로 98.32포인트 하락했다.</code> | <code>0.6799999999999999</code> |
| <code>미군 특수부대는 콜롬비아에서 두 번째로 큰 유전에서 원유를 운반하는 파이프라인을 보호하기 위해 이 지역의 군사기지에서 콜롬비아 군인들을 훈련시키고 있다.</code> | <code>미군 특수부대는 이 지역의 군사기지에서 콜롬비아 군인들을 훈련시켜 파이프라인을 보호하고 있다.</code> | <code>0.64</code> |
| <code>한 사람은 또한 영어/터키어 사전에서 난민이라는 단어를 지적했다.</code> | <code>한 남자는 영어-터키 사전을 휘두르고 "피난민"이라는 단어를 가리켰다.</code> | <code>0.76</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`: steps
- `num_train_epochs`: 5
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `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
</details>
### Training Logs
| Epoch | Step | Training Loss | sts-dev_spearman_max |
|:------:|:----:|:-------------:|:--------------------:|
| 0.3458 | 500 | 0.4169 | - |
| 0.6916 | 1000 | 0.2952 | 0.8533 |
| 1.0007 | 1447 | - | 0.8581 |
| 1.0367 | 1500 | 0.2744 | - |
| 1.3824 | 2000 | 0.1415 | 0.8520 |
| 1.7282 | 2500 | 0.0886 | - |
| 2.0007 | 2894 | - | 0.8634 |
### 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
```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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@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}
}
```
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