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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: '[라 메르]루미너스 리프팅 쿠션 파운데이션 SPF 20 [00003] 로즈 아이보리 (#M)11st>메이크업>페이스메이크업>파운데이션
11st > 뷰티 > 메이크업 > 페이스메이크업 > 파운데이션'
- text: 바비브라운 인텐시브 스킨 세럼 파운데이션 30ml (SPF40) 샌드 (#M)화장품/미용>베이스메이크업>파운데이션>리퀴드형 Naverstore
> 화장품/미용 > 베이스메이크업 > 파운데이션 > 리퀴드형
- text: 입생로랑 뚜쉬 에끌라 글로우-팩트 쿠션 B20 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트 LotteOn >
뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트
- text: IOPE 퍼펙트 커버 베이스 35ml 메이크업 컨실러 비비 1호 라이트그린 (#M)SSG.COM/메이크업/베이스메이크업/메이크업베이스
ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스
- text: 에스쁘아 프로 테일러 파운데이션 비글로우 SPF25 PA++ 1호 포슬린 × 1개 (#M)SSG.COM/메이크업/베이스메이크업/쿠션파운데이션
ssg > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션파운데이션
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: 0.9587109768378651
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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2 | <ul><li>'랑콤 뗑 이돌 울트라 웨어 스틱 SF 15 No. 04 Beige N 216699 LotteOn > 뷰티 > 베이스메이크업 > 파우더 LotteOn > 뷰티 > 베이스메이크업 > 파우더'</li><li>'조성아 슈퍼핏 메가프루프 스틱파데 1+1+에센스 미스트 100mlx1 21호 (#M)위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션 위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션'</li><li>'에스쁘아 가을 메이크업 빅세일 ~50% / 신상 스틱 파운데이션, 밋츠 그레이 립스틱, 룩북 팔레트 등 총 출동 비글로우스틱파데(아이보리)_611123517 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이섀도우 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이섀도우'</li></ul> |
| 1 | <ul><li>'[백화점정품/당일출고] 맥 스트롭 크림 50ml/핑크라이트(오리지널) (#M)홈>화장품/미용>베이스메이크업>메이크업베이스 Naverstore > 화장품/미용 > 베이스메이크업 > 메이크업베이스'</li><li>'에브리데이 IOPE 퍼펙트 커버 베이스35ml 메이크업 컨실러 비비 13579EA ◎＆쿠팡 본상품선택 × ◎＆쿠팡 2호 라이트퍼플 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스 메이크업 세트 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스 메이크업 세트'</li><li>'맥 스트롭 크림 50ml 실버라이트 (#M)위메프 > 뷰티 > 남성화장품 > 남성 메이크업 > 남성 베이스메이크업 위메프 > 뷰티 > 남성화장품 > 남성 메이크업 > 남성 베이스메이크업'</li></ul> |
| 0 | <ul><li>'[본사직영][기획]NEW 헤라 실키 스테이 파운데이션+파운데이션 브러쉬(48000원상당 본품동일사양)+블러셔 21W1 (#M)홈>메이크업>베이스메이크업 HMALL > 뷰티 > 메이크업 > 베이스메이크업'</li><li>'CHANEL 레 베쥬 뚜쉬 드 뗑 BR12 (#M)홈>화장품/미용>베이스메이크업>파운데이션>리퀴드형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 리퀴드형'</li><li>'에스티로더 [단독] 더블웨어 파운데이션 세트 (+마스카라 정품 ) 226792 1C0 쉘 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션'</li></ul> |
| 4 | <ul><li>'에스티 로더 더블 웨어 spf 10 - 36 샌드 30ml LotteOn > 뷰티 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션'</li><li>'Maybelline New York Maybelline New York Dream Smooth Mousse Foundation, Pure Beige, 0.49 Ounce LotteOn > 뷰티 > 색조메이크업 > 색조메이크업세트 LotteOn > 뷰티 > 색조메이크업 > 색조메이크업세트'</li><li>'수블리마지 르 뗑 10 베쥬 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션'</li></ul> |
| 3 | <ul><li>'[포렌코즈] 필 워터 쿠션 상세 설명 참조_피부타입:23호 내추럴베이지 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>파운데이션 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션'</li><li>'[현대백화점][시세이도]UV 프로텍티브 컴팩트 파운데이션 본품 SPF35/PA+++ [00004] 미디엄 아이보리 홈>화장품/미용>베이스메이크업>파운데이션>쿠션형;홈>화장품/미용>선케어>선파우더/쿠션;(#M)홈>화장품/미용>베이스메이크업>파운데이션>리퀴드형 Naverstore > 화장품/미용 > 선케어 > 선파우더/쿠션'</li><li>'롬앤 제로쿠션 spf20pa++ 0g발림성 세미매트쿠션 내추럴21호본품 화장품/향수>에어쿠션/팩트>에어쿠션;(#M)화장품/향수>베이스메이크업>파우더/트윈케이크 Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > 파우더/트윈케이크'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9587 |
## 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_top_bt5_4_test_flat")
# Run inference
preds = model("입생로랑 뚜쉬 에끌라 글로우-팩트 쿠션 B20 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트")
```
<!--
### 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 | 10 | 23.036 | 53 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
| 4 | 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 |
|:-------:|:-----:|:-------------:|:---------------:|
| 0.0026 | 1 | 0.3977 | - |
| 0.1279 | 50 | 0.4192 | - |
| 0.2558 | 100 | 0.3842 | - |
| 0.3836 | 150 | 0.3421 | - |
| 0.5115 | 200 | 0.3111 | - |
| 0.6394 | 250 | 0.2705 | - |
| 0.7673 | 300 | 0.2216 | - |
| 0.8951 | 350 | 0.1494 | - |
| 1.0230 | 400 | 0.0875 | - |
| 1.1509 | 450 | 0.0521 | - |
| 1.2788 | 500 | 0.0321 | - |
| 1.4066 | 550 | 0.004 | - |
| 1.5345 | 600 | 0.0019 | - |
| 1.6624 | 650 | 0.0011 | - |
| 1.7903 | 700 | 0.0005 | - |
| 1.9182 | 750 | 0.0004 | - |
| 2.0460 | 800 | 0.0004 | - |
| 2.1739 | 850 | 0.0003 | - |
| 2.3018 | 900 | 0.0003 | - |
| 2.4297 | 950 | 0.0003 | - |
| 2.5575 | 1000 | 0.0001 | - |
| 2.6854 | 1050 | 0.0003 | - |
| 2.8133 | 1100 | 0.0005 | - |
| 2.9412 | 1150 | 0.0003 | - |
| 3.0691 | 1200 | 0.0001 | - |
| 3.1969 | 1250 | 0.0001 | - |
| 3.3248 | 1300 | 0.0 | - |
| 3.4527 | 1350 | 0.0001 | - |
| 3.5806 | 1400 | 0.0 | - |
| 3.7084 | 1450 | 0.0 | - |
| 3.8363 | 1500 | 0.0 | - |
| 3.9642 | 1550 | 0.0 | - |
| 4.0921 | 1600 | 0.0 | - |
| 4.2199 | 1650 | 0.0 | - |
| 4.3478 | 1700 | 0.0 | - |
| 4.4757 | 1750 | 0.0 | - |
| 4.6036 | 1800 | 0.0 | - |
| 4.7315 | 1850 | 0.0 | - |
| 4.8593 | 1900 | 0.0 | - |
| 4.9872 | 1950 | 0.0002 | - |
| 5.1151 | 2000 | 0.0018 | - |
| 5.2430 | 2050 | 0.0009 | - |
| 5.3708 | 2100 | 0.0003 | - |
| 5.4987 | 2150 | 0.0 | - |
| 5.6266 | 2200 | 0.0002 | - |
| 5.7545 | 2250 | 0.0 | - |
| 5.8824 | 2300 | 0.0002 | - |
| 6.0102 | 2350 | 0.0 | - |
| 6.1381 | 2400 | 0.0007 | - |
| 6.2660 | 2450 | 0.0 | - |
| 6.3939 | 2500 | 0.0 | - |
| 6.5217 | 2550 | 0.0 | - |
| 6.6496 | 2600 | 0.0 | - |
| 6.7775 | 2650 | 0.0 | - |
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| 7.0332 | 2750 | 0.0 | - |
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| 7.6726 | 3000 | 0.0 | - |
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| 8.0563 | 3150 | 0.0 | - |
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| 9.8465 | 3850 | 0.0 | - |
| 9.9744 | 3900 | 0.0029 | - |
| 10.1023 | 3950 | 0.0026 | - |
| 10.2302 | 4000 | 0.0037 | - |
| 10.3581 | 4050 | 0.0001 | - |
| 10.4859 | 4100 | 0.0 | - |
| 10.6138 | 4150 | 0.0003 | - |
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| 17.9028 | 7000 | 0.0 | - |
| 18.0307 | 7050 | 0.0 | - |
| 18.1586 | 7100 | 0.0 | - |
| 18.2864 | 7150 | 0.0 | - |
| 18.4143 | 7200 | 0.0 | - |
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| 18.7980 | 7350 | 0.0 | - |
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| 19.0537 | 7450 | 0.0 | - |
| 19.1816 | 7500 | 0.0 | - |
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| 19.4373 | 7600 | 0.0 | - |
| 19.5652 | 7650 | 0.0 | - |
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| 19.9488 | 7800 | 0.0 | - |
| 20.0767 | 7850 | 0.0 | - |
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| 20.3325 | 7950 | 0.0 | - |
| 20.4604 | 8000 | 0.0 | - |
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| 24.9361 | 9750 | 0.0 | - |
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| 25.1918 | 9850 | 0.0 | - |
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| 25.5754 | 10000 | 0.0 | - |
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| 29.7954 | 11650 | 0.0 | - |
| 29.9233 | 11700 | 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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