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---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 1분완성 네일팁 모음인조손톱 인조팁 붙이는네일팁 웨딩네 13)샤인네일팁-화이트 LotteOn > 뷰티 > 네일 > 네일스티커/네일팁
LotteOn > 뷰티 > 네일 > 네일스티커/네일팁
- text: 오피아이 인피니트샤인2 매니큐어 MI12 × 1개 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일
> 일반네일 > 컬러 매니큐어
- text: 오피아이 젤 네일 컬러 GCV33 x 1개 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일 > 일반네일
> 컬러 매니큐어
- text: 디올 베르니 212 튀튀 LotteOn > 뷰티 > 메이크업 > 메이크업세트 LotteOn > 뷰티 > 메이크업 > 메이크업세트
- text: OPI 인피니트샤인 HRL31 LETS BE FRIENDS HRL31 - LETS BE FRIENDS! LotteOn > 뷰티 > 헤어/바디
> 헤어스타일링 > 염색/매니큐어 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 염색/매니큐어
inference: true
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: 0.5301810865191147
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:** 4 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3 | <ul><li>'네일팁 실크익스텐션 311160L1720771597 티타늄금 물방울 （풀값 ） LotteOn > 뷰티 > 네일케어 > 네일케어도구 > 손톱깎이 LotteOn > 뷰티 > 네일케어 > 네일케어도구 > 손톱깎이'</li><li>'엔비베베 어린이 화장품 선물세트 어린이 썬쿠션+키즈네일스티커+워시패드 1개 (#M)쿠팡 홈>뷰티>어린이화장품>세트/키트 Coupang > 뷰티 > 어린이화장품 > 세트/키트'</li><li>'래쉬톡 원터치 인조 속눈썹 섹시 걸 × 3개입 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리'</li></ul> |
| 0 | <ul><li>'오피아이 넌아세톤 리무버 빨강 30ml × 5개 (#M)쿠팡 홈>뷰티>네일>일반네일>리무버 Coupang > 뷰티 > 네일 > 일반네일 > 리무버'</li><li>'[OPI][리무버] 넌아세톤리무버 30ml ssg > 뷰티 > 메이크업 > 네일 ssg > 뷰티 > 메이크업 > 네일'</li><li>'포먼트 젤네일 O.4 블러쉬 뷰티 × 1개 (#M)쿠팡 홈>뷰티>네일>젤네일>컬러 젤 Coupang > 뷰티 > 네일 > 젤네일 > 컬러 젤'</li></ul> |
| 2 | <ul><li>'오피아이 프로스파 오일투고 큐티클 오일2197877 1 7.5ml x 1개2197877 1 (#M)SSG.COM/메이크업/베이스메이크업/컨실러 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 컨실러'</li><li>'구찌 뷰티 [구찌] 베르니 아 옹글 하이 샤인 네일 라커 712 멜린다 그린 × 선택완료 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일 > 일반네일 > 컬러 매니큐어'</li><li>'OPI ProSpa 각질 제거 큐티클 크림, 27ml SSG.COM/메이크업/베이스메이크업/메이크업베이스;ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스'</li></ul> |
| 1 | <ul><li>'르 베르니 루쥬 느와르 DepartmentLotteOn > 뷰티 > 헤어/바디 > 핸드/풋케어 > 네일케어 DepartmentLotteOn > 뷰티 > 헤어/바디 > 핸드/풋케어 > 네일케어'</li><li>'베씨 베이스젤 + 탑젤 + 지브라파일 2p 세트 베이스젤, 탑젤, 지브라파일(100/150) × 1세트 LotteOn > 뷰티 > 네일 > 네일아트소품 LotteOn > 뷰티 > 네일 > 네일아트소품'</li><li>'OPI OPI Chrome Effects Nail Lacquer Top Coat CPT31 - 0.5 oz 상세내용참조 × 상세내용참조 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스/프라이머 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스/프라이머'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.5302 |
## 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_bt1_test_flat_top_cate")
# Run inference
preds = model("디올 베르니 212 튀튀 LotteOn > 뷰티 > 메이크업 > 메이크업세트 LotteOn > 뷰티 > 메이크업 > 메이크업세트")
```
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### Downstream Use
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### Out-of-Scope Use
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## 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.*
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### Recommendations
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 13 | 22.7236 | 41 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 49 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
### Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 100
- 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0032 | 1 | 0.4603 | - |
| 0.1608 | 50 | 0.4502 | - |
| 0.3215 | 100 | 0.4315 | - |
| 0.4823 | 150 | 0.3996 | - |
| 0.6431 | 200 | 0.365 | - |
| 0.8039 | 250 | 0.2954 | - |
| 0.9646 | 300 | 0.2647 | - |
| 1.1254 | 350 | 0.2378 | - |
| 1.2862 | 400 | 0.2257 | - |
| 1.4469 | 450 | 0.2165 | - |
| 1.6077 | 500 | 0.213 | - |
| 1.7685 | 550 | 0.1999 | - |
| 1.9293 | 600 | 0.1838 | - |
| 2.0900 | 650 | 0.1614 | - |
| 2.2508 | 700 | 0.1164 | - |
| 2.4116 | 750 | 0.0553 | - |
| 2.5723 | 800 | 0.0366 | - |
| 2.7331 | 850 | 0.0279 | - |
| 2.8939 | 900 | 0.0219 | - |
| 3.0547 | 950 | 0.0166 | - |
| 3.2154 | 1000 | 0.0111 | - |
| 3.3762 | 1050 | 0.0067 | - |
| 3.5370 | 1100 | 0.0084 | - |
| 3.6977 | 1150 | 0.0066 | - |
| 3.8585 | 1200 | 0.0048 | - |
| 4.0193 | 1250 | 0.0028 | - |
| 4.1801 | 1300 | 0.0005 | - |
| 4.3408 | 1350 | 0.0003 | - |
| 4.5016 | 1400 | 0.0004 | - |
| 4.6624 | 1450 | 0.0001 | - |
| 4.8232 | 1500 | 0.0001 | - |
| 4.9839 | 1550 | 0.0001 | - |
| 5.1447 | 1600 | 0.0001 | - |
| 5.3055 | 1650 | 0.0001 | - |
| 5.4662 | 1700 | 0.0002 | - |
| 5.6270 | 1750 | 0.0 | - |
| 5.7878 | 1800 | 0.0 | - |
| 5.9486 | 1850 | 0.0 | - |
| 6.1093 | 1900 | 0.0001 | - |
| 6.2701 | 1950 | 0.0 | - |
| 6.4309 | 2000 | 0.0 | - |
| 6.5916 | 2050 | 0.0 | - |
| 6.7524 | 2100 | 0.0 | - |
| 6.9132 | 2150 | 0.0002 | - |
| 7.0740 | 2200 | 0.0002 | - |
| 7.2347 | 2250 | 0.0 | - |
| 7.3955 | 2300 | 0.0 | - |
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| 12.8617 | 4000 | 0.0001 | - |
| 13.0225 | 4050 | 0.0002 | - |
| 13.1833 | 4100 | 0.0009 | - |
| 13.3441 | 4150 | 0.0037 | - |
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| 29.7428 | 9250 | 0.0 | - |
| 29.9035 | 9300 | 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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