sentence-transformers safetensors roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:7634 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:jhgan/ko-sroberta-multitask base_model:finetune:jhgan/ko-sroberta-multitask autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: jhgan/ko-sroberta-multitask
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7634
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 필름디지털 SLR카메라를 다루는 방법을 익혀 창의력있는 사진 촬영부터 기초적인 컴퓨터
  sentences:
  - 회진 매일 월 오리엔테이션 POMR케이스발표 외래참관 가정의학 총론 강의 화 외래참관
  - 이해하기 퀴즈 배열 이해하기 이미지와 카메라의 활용 본인 작업 계획 발표
  - 익히면서 통계학의 기초 개념과 원리를 익혀보는 것은 이론적으로 접근하는 것만큼 중요한
- source_sentence: 선택 삽입정렬 구현 실습과제 상향식 힙 생성 구현 힙 정렬 구현유일키
  sentences:
  - 서양의학사 세기 의학의 발전 지역사회의학 실험실의학바이넘 서양의학사 현충일 우리나라 현대 의학의
  - 섬유예술의 기초기법 실습 위빙 표현연구 섬유예술의 기초기법 기법 소개 페이퍼 메이킹Paper
  - 개념 힙의 구현과 연산 우선순위큐 힙 연산의 복잡도 분석 힙 정렬
- source_sentence: 군밤타령 경복궁 타령 실습 강원도 아리랑
  sentences:
  - 추가 강의 동영상으로 보강 대체 밀집성 천체물리학 Ch 창립 주년 기념일
  - 분석 보고서 제출마감시한 월 일 밤 시 기획기사 분석 보고서 발표
  - 또한 극한점 수렴성 거리 연속성 연결성 컴팩트성
- source_sentence: 이야기가 담긴 몸 창작 작업 스토리텔링 일상에서 발견한 소소한 움직임들 창작
  sentences:
  - 이해 및 실습 사전 훈련 모델Pretrained Model 활용 실습 Object Detection
  - 본인의 관심사를 공유하고 이야기를 나누고 덧붙이고 덜어내고 편집하여 거대한 이야기로 완성합니다
  - 트랜스미디어 스토리텔링의 유형소형 금 트랜스미디어 스토리텔링의 두 가지 유형 중 소형에
- source_sentence: 행위의 개념 로서의 조형 실습 시멘트 석고 모래 물 입체덩어리 세우기
  sentences:
  - 움직임을 강건하게 만드는 체력운동과 적극적인 공 방 기술을 익힌다
  - 특히 르네상스 종교개혁 시민혁명 내셔널리즘과 통일 국가의 형성 등의 주요
  - 추석 연휴 회전축에 대한 관성 모멘트 관성모멘트 측정 중등물리실험 전류가 만드는
---

# 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("jh8416/my_ewha_model_2024_1")
# Run inference
sentences = [
    '행위의 개념 로서의 조형 실습 시멘트 석고 모래 물 입체덩어리 세우기',
    '추석 연휴 회전축에 대한 관성 모멘트 관성모멘트 측정 중등물리실험 전류가 만드는',
    '움직임을 강건하게 만드는 체력운동과 적극적인 공 방 기술을 익힌다',
]
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: 7,634 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 24.61 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 25.39 tokens</li><li>max: 73 tokens</li></ul> |
* Samples:
  | sentence_0                               | sentence_1                                               |
  |:-----------------------------------------|:---------------------------------------------------------|
  | <code>딥러닝은 머신러닝 기법의 일부로 사물이나 데이터를</code> | <code>밸런스를 향상을 위한 트레이닝 방법 트레이닝실습 팀 프로젝트 트레이닝 실습 팀</code> |
  | <code>딥러닝은 머신러닝 기법의 일부로 사물이나 데이터를</code> | <code>딥러닝 딥러닝의 역사 프로젝트 발표 인공</code>                      |
  | <code>딥러닝은 머신러닝 기법의 일부로 사물이나 데이터를</code> | <code>딥러닝 기반 의료영상 응용연구 동향 신태훈</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"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `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`: 1
- `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>

### Framework Versions
- Python: 3.12.0
- Sentence Transformers: 3.0.1
- Transformers: 4.43.3
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- 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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