---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 베리네이처 유기농 이유식 큐브 야채 토핑 초기 다진 단호박 45g 유기농_★단품 후기 90g_05.다진 적양배추 출산/육아 > 이유식
    > 이유식재료
- text: 처음요리 이유식 유아식 밀키트 세트 초기 중기 후기 완료기 유아식 식단세트 다진야채큐브 05.진죽1_10팩 매일한우식단 1번_베이직(쌀/육수
    제외) 출산/육아 > 이유식 > 이유식재료
- text: 루솔 튼튼 어린이 볶음밥 8가지맛 (1팩) LU0723.버섯볶음밥 출산/육아 > 이유식 > 가공이유식
- text: 프레벨롱 국산과일 퓨레 6팩세트 아기퓨레 아기간식 블루베리 2팩+비트 2팩+고구마 2팩 출산/육아 > 이유식 > 가공이유식
- text: 알렉스앤필 6종 스웨덴 유기농 아기 이유식 과일퓨레 당근&망고 출산/육아 > 이유식 > 가공이유식
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain
model-index:
- name: SetFit with mini1013/master_domain
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1.0
      name: Accuracy
---

# SetFit with mini1013/master_domain

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

### Model Labels
| Label | Examples                                                                                                                                                                                                                                                                                   |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0   | <ul><li>'이유식 야채 큐브 다진야채 적양배추_유아기 출산/육아 > 이유식 > 이유식재료'</li><li>'오뚜기 어린이카레 80g  출산/육아 > 이유식 > 이유식재료'</li><li>'라온킴 다진야채 매일 만드는 이유식큐브 토핑 초기 중기 후기 완료 연근(껍질제거)_중기 출산/육아 > 이유식 > 이유식재료'</li></ul>                                                                                                |
| 0.0   | <ul><li>'[1+1 ] 아기퓨레 과일 무럭무럭 키즈죽 간식 중기 후기 파우치 실온이유식 12개월 단호박 1박스 + 바나나단호박 1박스 출산/육아 > 이유식 > 가공이유식'</li><li>'푸드트리 아기카레 덮밥소스 돌 두돌 아기반찬 유아반찬 유아식 소고기커리 아기덮밥 소스) A07 소고기 순한짜장 출산/육아 > 이유식 > 가공이유식'</li><li>'퓨어잇 아이김 3+3팩 골라담기 파래김/김과자 오가닉 아이김자반 3봉_유기농 김100% 3팩 출산/육아 > 이유식 > 가공이유식'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_bc25")
# Run inference
preds = model("알렉스앤필 6종 스웨덴 유기농 아기 이유식 과일퓨레 당근&망고 출산/육아 > 이유식 > 가공이유식")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 8   | 15.4286 | 23  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |

### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch   | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0357  | 1    | 0.4786        | -               |
| 1.7857  | 50   | 0.2484        | -               |
| 3.5714  | 100  | 0.0           | -               |
| 5.3571  | 150  | 0.0           | -               |
| 7.1429  | 200  | 0.0           | -               |
| 8.9286  | 250  | 0.0           | -               |
| 10.7143 | 300  | 0.0           | -               |
| 12.5    | 350  | 0.0           | -               |
| 14.2857 | 400  | 0.0           | -               |
| 16.0714 | 450  | 0.0           | -               |
| 17.8571 | 500  | 0.0           | -               |
| 19.6429 | 550  | 0.0           | -               |
| 21.4286 | 600  | 0.0           | -               |
| 23.2143 | 650  | 0.0           | -               |
| 25.0    | 700  | 0.0           | -               |
| 26.7857 | 750  | 0.0           | -               |
| 28.5714 | 800  | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- Datasets: 3.2.0
- Tokenizers: 0.19.1

## Citation

### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
```

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