README.md

---
base_model: jh8416/my_ewha_model_2024_1
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:97764
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 미디어 언어 중간시험 중간시험 강평 제부
  sentences:
  - 적분의 정의교육관B동 호 교시이 강의에서는 리만적분의 정의와 유용한 여러 가지 적분법
  - 창립 주년 기념일 성격심리학 입문 제부 성향적 영역 제부 성향적 영역
  - career paths for the DIS graduates Ewha cyber campus How to
- source_sentence: hierarchies through relationality in the ethics of care International
    Journal of
  sentences:
  - economy culture and law to ethics
  - Instructor Bae Movie WIT Values ethics and advocacy Lecture group discussion
  - 깊이 이해할 수 있는 지름길일 것입니다
- source_sentence: 주차별 강의 내용은 사정에 따라 변동될
  sentences:
  - 조순경 여성직종의 외주화와 간접차별 KTX 승무원 간접고용을 통해 본
  - 욕구를 만족시키기 위해 기본적인 마케팅전략의 개념과 이론을 학습하고 여러 성공적인 마케팅전략의
  - 속 리더십 성공사례 연구 내용 정신전력교육구술평가 임관종합평가 대비 정신전력 교육 조직속에서
- source_sentence: 상형미를 기반으로 하는 조형미를 이해하여 궁극적으로는 서화동원을 이해하고 동양예술에서 추구한 획과
  sentences:
  - 장흔들리는 마음 수업자료스타트업얼라이언스가이드북시리즈초보창업자를위한 HR가이드북 부 장초기 단계 재무관리 핵심 공략 장초기
  - 중간시험 리만적분 연습문제
  - 같은 다층적이며 종합적인 접근을 통해 궁극적으로는 생태계가 유지되고 작동하는 원리 그리고
- source_sentence: 제작 과정 이해 및 실습 석고 몰드 캐스팅 기법을 이용한 개별
  sentences:
  - 선거 Mould 제작 Slip Casting 석고원형 제작 원형완성 및 검사 Project
  - 역사적 고찰 세기 교수법 초급 피아노 교수법 기초 및 유아과정 중급
  - 발전과제 학교문화와 풍토 주교재 장 학교문화의 개념 및 특징 조직문화 이론Ouchi의
---

# SentenceTransformer based on jh8416/my_ewha_model_2024_1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jh8416/my_ewha_model_2024_1](https://huggingface.co/jh8416/my_ewha_model_2024_1). 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:** [jh8416/my_ewha_model_2024_1](https://huggingface.co/jh8416/my_ewha_model_2024_1) <!-- at revision b97ea2a0717427f085226fd9284f2d5b4a7c1b8c -->
- **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 = [
    '제작 과정 이해 및 실습 석고 몰드 캐스팅 기법을 이용한 개별',
    '선거 Mould 제작 Slip Casting 석고원형 제작 원형완성 및 검사 Project',
    '발전과제 학교문화와 풍토 주교재 장 학교문화의 개념 및 특징 조직문화 이론Ouchi의',
]
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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You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 97,764 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: 6 tokens</li><li>mean: 17.88 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.09 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
  | sentence_0                                            | sentence_1                                                   |
  |:------------------------------------------------------|:-------------------------------------------------------------|
  | <code>자신을 닫아놓으면서도 다른 한 편으론 보석처럼 반짝이는 돌은 매력이 있다</code> | <code>작품의 용도 설정 보석함 필구함 기타 IV</code>                         |
  | <code>자신을 닫아놓으면서도 다른 한 편으론 보석처럼 반짝이는 돌은 매력이 있다</code> | <code>발표 및 제출인쇄물A포맷 도안밑그림 이미지 크기보석함과 기타 함의 크기를 기준으로 함</code> |
  | <code>자신을 닫아놓으면서도 다른 한 편으론 보석처럼 반짝이는 돌은 매력이 있다</code> | <code>밑그림에 채색 및 기법 표시 보석함 크기외경 xx</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>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0818 | 500  | 1.0712        |
| 0.1636 | 1000 | 0.9295        |
| 0.2455 | 1500 | 0.8423        |
| 0.3273 | 2000 | 0.8157        |
| 0.4091 | 2500 | 0.794         |
| 0.4909 | 3000 | 0.7058        |
| 0.5727 | 3500 | 0.6726        |
| 0.6546 | 4000 | 0.6664        |
| 0.7364 | 4500 | 0.6302        |
| 0.8182 | 5000 | 0.6029        |
| 0.9000 | 5500 | 0.5936        |
| 0.9818 | 6000 | 0.5873        |


### 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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