README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 45T PVC 원톤파티션 사무실파티션 책상 칸막이 패브릭 천파티션 가림막 W600 H1000 가구/인테리어>서재/사무용가구>사무/교구용가구>파티션
- text: GOYA 고야 크맘 곰 자작나무 책상 파티션 600 학교 칸막이 가구/인테리어>서재/사무용가구>사무/교구용가구>파티션
- text: 와이디 로아 모던 책상 미드센츄리 테이블  800 가구/인테리어>서재/사무용가구>책상>일자형 책상
- text: 컴퓨터 의자 가정용 앉은 기숙사 대학생 소파 사무실 거짓말 가구/인테리어>서재/사무용가구>의자>하이팩의자
- text: 한샘 레그핏 쿠션형 책상 발받침대 의자발받침 다리받침대 가구/인테리어>서재/사무용가구>의자>의자발받침대
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:** 5 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                                                                                                                                                                                                       |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4.0   | <ul><li>'스코나 밀러튼 LPM 1400 멀티 교구장 책장 가구/인테리어>서재/사무용가구>책장'</li><li>'이케아 BILLY 빌리 3단 책장 40cm 가구/인테리어>서재/사무용가구>책장'</li><li>'에보니아 로엠 600 3단 하부 도어 책장 가구/인테리어>서재/사무용가구>책장'</li></ul>                                 |
| 2.0   | <ul><li>'선반 철제 책꽂이 수납 타공판 책상위정리 책장 세트-후크 3 흰색 단층 홀 보드 가구/인테리어>서재/사무용가구>책꽂이'</li><li>'델리 2단 서랍 겸 책꽂이 데스크 손잡이 오거나이저 가구/인테리어>서재/사무용가구>책꽂이'</li><li>'북케이스 책장 수납 선반 북 보관 책꽂이 가구/인테리어>서재/사무용가구>책꽂이'</li></ul>        |
| 3.0   | <ul><li>'209애비뉴 제로데스크 에보 멀티 컴퓨터책상 1600x800 가구/인테리어>서재/사무용가구>책상>컴퓨터책상'</li><li>'한샘 티오 일자책상세트 5단 120x60cm 콘센트형 조명 가구/인테리어>서재/사무용가구>책상>일자형 책상'</li><li>'아씨방 마일드 모션데스크 120cm 가구/인테리어>서재/사무용가구>책상>스탠딩책상'</li></ul>  |
| 0.0   | <ul><li>'하이솔로몬 강의대 LS13 가구/인테리어>서재/사무용가구>사무/교구용가구>사무용책상'</li><li>'사무실쇼파 제논 2인용 소파 가구/인테리어>서재/사무용가구>사무/교구용가구>사무용소파'</li><li>'스테인리스 서랍장 캐비닛 미용실 매장용 사물함 스텐 가구/인테리어>서재/사무용가구>사무/교구용가구>캐비닛'</li></ul>              |
| 1.0   | <ul><li>'접이식 썬베드 간이 낮잠 의자 휴대용 리클라이너 경량 가구/인테리어>서재/사무용가구>의자>안락의자'</li><li>'체스좌식의자 엠보싱 가구/인테리어>서재/사무용가구>의자>좌식의자'</li><li>'나른인 쇼파 손잡이가 달린 침대 위 나부끼창 커밋의자 껴안다 건산수유 의자와 다다미 좌석 가구/인테리어>서재/사무용가구>의자>하이팩의자'</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_fi3")
# Run inference
preds = model("와이디 로아 모던 책상 미드센츄리 테이블  800 가구/인테리어>서재/사무용가구>책상>일자형 책상")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 8.5543 | 22  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |
| 2.0   | 70                    |
| 3.0   | 70                    |
| 4.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0145  | 1    | 0.4825        | -               |
| 0.7246  | 50   | 0.4985        | -               |
| 1.4493  | 100  | 0.4783        | -               |
| 2.1739  | 150  | 0.1925        | -               |
| 2.8986  | 200  | 0.0024        | -               |
| 3.6232  | 250  | 0.0001        | -               |
| 4.3478  | 300  | 0.0001        | -               |
| 5.0725  | 350  | 0.0001        | -               |
| 5.7971  | 400  | 0.0           | -               |
| 6.5217  | 450  | 0.0           | -               |
| 7.2464  | 500  | 0.0           | -               |
| 7.9710  | 550  | 0.0           | -               |
| 8.6957  | 600  | 0.0           | -               |
| 9.4203  | 650  | 0.0           | -               |
| 10.1449 | 700  | 0.0           | -               |
| 10.8696 | 750  | 0.0           | -               |
| 11.5942 | 800  | 0.0           | -               |
| 12.3188 | 850  | 0.0           | -               |
| 13.0435 | 900  | 0.0           | -               |
| 13.7681 | 950  | 0.0           | -               |
| 14.4928 | 1000 | 0.0           | -               |
| 15.2174 | 1050 | 0.0           | -               |
| 15.9420 | 1100 | 0.0           | -               |
| 16.6667 | 1150 | 0.0           | -               |
| 17.3913 | 1200 | 0.0           | -               |
| 18.1159 | 1250 | 0.0           | -               |
| 18.8406 | 1300 | 0.0           | -               |
| 19.5652 | 1350 | 0.0           | -               |
| 20.2899 | 1400 | 0.0           | -               |
| 21.0145 | 1450 | 0.0           | -               |
| 21.7391 | 1500 | 0.0           | -               |
| 22.4638 | 1550 | 0.0           | -               |
| 23.1884 | 1600 | 0.0           | -               |
| 23.9130 | 1650 | 0.0           | -               |
| 24.6377 | 1700 | 0.0           | -               |
| 25.3623 | 1750 | 0.0           | -               |
| 26.0870 | 1800 | 0.0           | -               |
| 26.8116 | 1850 | 0.0           | -               |
| 27.5362 | 1900 | 0.0           | -               |
| 28.2609 | 1950 | 0.0           | -               |
| 28.9855 | 2000 | 0.0           | -               |
| 29.7101 | 2050 | 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->