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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: 센카 퍼펙트 휩 클렌징 폼 리뉴얼 120g × 10개 (#M)쿠팡 홈>싱글라이프>샤워/세안>클렌징>폼/젤/비누 Coupang > 뷰티
> 클렌징/필링 > 클렌징 폼
- text: 프로필링 소프트젤 100ml 피부 세안제 클렌징 필링 (#M)홈>화장품/미용>클렌징>스크럽/필링 Naverstore > 화장품/미용
> 클렌징 > 스크럽/필링
- text: 센카 퍼펙트 휩 페이셜 워시 대용량 클렌징 폼 150g × 3개 (#M)쿠팡 홈>싱글라이프>샤워/세안>클렌징>폼/젤/비누 Coupang
> 뷰티 > 클렌징/필링 > 클렌징 폼
- text: 센카 퍼펙트휩 2개+아크네케어 2개 센카 퍼펙트휩 2개+아크네케어 2개 LotteOn > 뷰티 > 남성화장품 > 클렌징 LotteOn
> 뷰티 > 남성화장품 > 클렌징
- text: '[20% ]한스킨 모공앰플체험딜 2500원 91% 外 비비크림/컨실러/클렌징오일/선크림/기초 전품목 32.블랙헤드 클렌징 티슈_블랙헤드
클렌징 티슈 100매 [GH990850] 쇼킹딜 홈>뷰티>선케어/메이크업>페이스메이크업;11st>뷰티>선케어/메이크업>페이스메이크업;11st>메이크업>페이스메이크업>BB크림;11st
> 뷰티 > 메이크업 > 페이스메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 페이스메이크업'
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: 0.9232323232323232
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:** 7 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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6 | <ul><li>'남자클렌징폼 알로에성분 수분밸런스 세면도구 미셀라 클클 워터 미셀라워터100ml (#M)위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저 위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저'</li><li>'차앤박 CNP 에이클린 퓨리파잉 포밍 클렌저 145mL LotteOn > 뷰티 > 남성화장품 > 남성화장품세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트'</li><li>'[클린앤드클리어] 딥 액션 블랙헤드 데일리 클렌저 100gx2 CC딥액션블랙헤드클렌저100gx2 (#M)뷰티>화장품/향수>스킨케어>로션/에멀전 CJmall > 뷰티 > 화장품/향수 > 스킨케어 > 에센스/세럼/오일'</li></ul> |
| 2 | <ul><li>'(키엘) 미드나잇 리커버리 보태니컬 클렌징 오일 - 모든 피부용 --85ml/2.8oz ssg > 뷰티 > 스킨케어 > 스킨/토너/미스트 > 스킨/토너 LOREAL > Ssg > 키엘 > Branded > 키엘'</li><li>'마녀공장 퓨어 클렌징 오일 141238 200ml x 3개 (#M)11st>바디케어>바디미스트>바디미스트 11st > 뷰티 > 바디케어 > 바디미스트'</li><li>'[정품 세럼쿠션 & 비타민 크림 샘플 증정] 인텐시브 세럼 파운데이션 세트 쿨 아이보리 ssg > 뷰티 > 메이크업 > 립메이크업;ssg > 뷰티 > 메이크업 > 베이스메이크업;ssg > 뷰티 > 스킨케어 > 스킨/토너;ssg > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션;SSG.COM/메이크업/베이스메이크업/리퀴드파운데이션;ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이섀도우;ssg > 뷰티 > 명품화장품 > 메이크업 ssg > 뷰티 > 메이크업 > 아이메이크업'</li></ul> |
| 5 | <ul><li>'[라끄베르] 딥 앤 모이스트 클렌징 티슈 05_클렌징 티슈 80매 홈>5월 행사;홈>6월 행사!;홈>전체상품;(#M)홈>라끄베르 Naverstore > 화장품/미용 > 클렌징 > 클렌징티슈'</li><li>'토니모리 프로클린 소프트 클렌징 티슈 1+1 (#M)홈>화장품/미용>클렌징>클렌징티슈 Naverstore > 화장품/미용 > 클렌징 > 클렌징티슈'</li><li>'[소미Pick! 코스알엑스] 원스텝 스킨패드 3종 / NEW 더 비타민C 세럼 外 포어리스 패드 11st>뷰티>스킨케어>스킨/로션;11st>스킨케어>스킨/토너>스킨/토너;11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 스킨/로션 11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 스킨/로션'</li></ul> |
| 0 | <ul><li>'[일리윤] 프레쉬 모이스춰 립앤아이리무버 100ml 3개 단일상품 (#M)위메프 > 뷰티 > 네일케어 > 네일리무버 > 네일리무버 위메프 > 뷰티 > 네일케어 > 네일리무버 > 네일리무버'</li><li>'키스미 히로인메이크 스피디 마스카라 리무버 6.마스카라 리무버(K407A) (#M)화장품/향수>색조메이크업>마스카라 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 > 마스카라'</li><li>'랑콤 비파실 200ml ssg > 뷰티 > 스킨케어 > 스킨/토너 ssg > 뷰티 > 스킨케어 > 스킨/토너'</li></ul> |
| 4 | <ul><li>'라떼 다 토일레테 250ml 화이트_Free (#M)뷰티>헤어/바디/미용기기>바디케어>바디로션/크림 CJmall > 뷰티 > 화장품/향수 > 향수/홈프래그런스 > 디퓨저/방향제'</li><li>'마몽드 트리플 멀티 클렌징 크림 190ml (#M)GSSHOP>뷰티>스킨케어>스킨케어세트 GSSHOP > 뷰티 > 스킨케어 > 스킨케어세트'</li><li>'마몽드 트리플 멀티 클렌징 크림 190ml MinSellAmount (#M)화장품/향수>클렌징/필링>클렌징크림 Gmarket > 뷰티 > 화장품/향수 > 클렌징/필링 > 클렌징크림'</li></ul> |
| 1 | <ul><li>'클라란스 컴포트 스크럽 - 너리싱 오일 스크럽50ml/1.7oz (#M)홈>스트로베리넷>향수|디퓨저>향수|디퓨저 전체보기 HMALL > 뷰티 > 스킨케어 > 스크럽/필링'</li><li>'닥터지 레드 블레미쉬 수딩 크림 토너 폼 에멀전 필링 젤 03.브라이트닝 필링 젤 120g (#M)11st>스킨케어>앰플>앰플 11st > 뷰티 > 스킨케어 > 앰플'</li><li>'데쌍브르 올인원 각질 트러블 흔적 시카 미백 아하 바하 스피큘 해초 약초 니들필링50g 30데이즈(필링크림30g+앰플30ea) (#M)화장품/미용>클렌징>스크럽/필링 Naverstore > 화장품/미용 > 클렌징 > 스크럽/필링'</li></ul> |
| 3 | <ul><li>'산타마리아노벨라 아쿠아 디 로즈 미셀라 워터 200ml 투명_F (#M)화장품/미용>클렌징>클렌징워터 Naverstore > 화장품/미용 > 클렌징 > 클렌징워터'</li><li>'[쿠폰+T11%] 라네즈 퍼펙트리뉴 유스 레티놀 프로 꿀잠 잠옷 증정!/1밤1레티놀/라네즈레티놀 35. 라네즈 워터뱅크 아이젤 25ml_선택완료 쇼킹딜 홈>뷰티>스킨케어>스킨/로션;11st>스킨케어>스킨/토너>스킨/토너;쇼킹딜 홈>뷰티>선케어/메이크업>선블록;11st>뷰티>선케어/메이크업>선블록;11st > 뷰티 > 스킨케어 > 스킨/토너;(#M)11st>뷰티>스킨케어>스킨/로션 11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 스킨/로션'</li><li>'[클린앤클리어] 미셀라 워터 100ml x2 (#M)GSSHOP>뷰티>클렌징>클렌징폼 GSSHOP > 뷰티 > 클렌징 > 클렌징폼'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9232 |
## 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_bt_top10_test")
# Run inference
preds = model("프로필링 소프트젤 100ml 피부 세안제 클렌징 필링 (#M)홈>화장품/미용>클렌징>스크럽/필링 Naverstore > 화장품/미용 > 클렌징 > 스크럽/필링")
```
<!--
### 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 | 11 | 21.9571 | 61 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
| 4 | 50 |
| 5 | 50 |
| 6 | 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 |
|:-------:|:-----:|:-------------:|:---------------:|
| 0.0018 | 1 | 0.4528 | - |
| 0.0914 | 50 | 0.4525 | - |
| 0.1828 | 100 | 0.4612 | - |
| 0.2742 | 150 | 0.4424 | - |
| 0.3656 | 200 | 0.4291 | - |
| 0.4570 | 250 | 0.3832 | - |
| 0.5484 | 300 | 0.3246 | - |
| 0.6399 | 350 | 0.2943 | - |
| 0.7313 | 400 | 0.2745 | - |
| 0.8227 | 450 | 0.2655 | - |
| 0.9141 | 500 | 0.2604 | - |
| 1.0055 | 550 | 0.253 | - |
| 1.0969 | 600 | 0.2367 | - |
| 1.1883 | 650 | 0.228 | - |
| 1.2797 | 700 | 0.2115 | - |
| 1.3711 | 750 | 0.1976 | - |
| 1.4625 | 800 | 0.1786 | - |
| 1.5539 | 850 | 0.1609 | - |
| 1.6453 | 900 | 0.1472 | - |
| 1.7367 | 950 | 0.13 | - |
| 1.8282 | 1000 | 0.1213 | - |
| 1.9196 | 1050 | 0.1079 | - |
| 2.0110 | 1100 | 0.1058 | - |
| 2.1024 | 1150 | 0.0985 | - |
| 2.1938 | 1200 | 0.0824 | - |
| 2.2852 | 1250 | 0.0546 | - |
| 2.3766 | 1300 | 0.039 | - |
| 2.4680 | 1350 | 0.0202 | - |
| 2.5594 | 1400 | 0.0089 | - |
| 2.6508 | 1450 | 0.0044 | - |
| 2.7422 | 1500 | 0.004 | - |
| 2.8336 | 1550 | 0.0045 | - |
| 2.9250 | 1600 | 0.0016 | - |
| 3.0165 | 1650 | 0.0005 | - |
| 3.1079 | 1700 | 0.0004 | - |
| 3.1993 | 1750 | 0.0003 | - |
| 3.2907 | 1800 | 0.0002 | - |
| 3.3821 | 1850 | 0.0002 | - |
| 3.4735 | 1900 | 0.0001 | - |
| 3.5649 | 1950 | 0.0002 | - |
| 3.6563 | 2000 | 0.0003 | - |
| 3.7477 | 2050 | 0.0002 | - |
| 3.8391 | 2100 | 0.0001 | - |
| 3.9305 | 2150 | 0.0001 | - |
| 4.0219 | 2200 | 0.0002 | - |
| 4.1133 | 2250 | 0.0002 | - |
| 4.2048 | 2300 | 0.0003 | - |
| 4.2962 | 2350 | 0.0001 | - |
| 4.3876 | 2400 | 0.0003 | - |
| 4.4790 | 2450 | 0.0001 | - |
| 4.5704 | 2500 | 0.0001 | - |
| 4.6618 | 2550 | 0.0006 | - |
| 4.7532 | 2600 | 0.0002 | - |
| 4.8446 | 2650 | 0.0001 | - |
| 4.9360 | 2700 | 0.0022 | - |
| 5.0274 | 2750 | 0.0046 | - |
| 5.1188 | 2800 | 0.0028 | - |
| 5.2102 | 2850 | 0.0033 | - |
| 5.3016 | 2900 | 0.0025 | - |
| 5.3931 | 2950 | 0.0023 | - |
| 5.4845 | 3000 | 0.002 | - |
| 5.5759 | 3050 | 0.004 | - |
| 5.6673 | 3100 | 0.0044 | - |
| 5.7587 | 3150 | 0.004 | - |
| 5.8501 | 3200 | 0.0027 | - |
| 5.9415 | 3250 | 0.0032 | - |
| 6.0329 | 3300 | 0.0002 | - |
| 6.1243 | 3350 | 0.0003 | - |
| 6.2157 | 3400 | 0.0 | - |
| 6.3071 | 3450 | 0.0006 | - |
| 6.3985 | 3500 | 0.0005 | - |
| 6.4899 | 3550 | 0.0035 | - |
| 6.5814 | 3600 | 0.0053 | - |
| 6.6728 | 3650 | 0.004 | - |
| 6.7642 | 3700 | 0.0042 | - |
| 6.8556 | 3750 | 0.0046 | - |
| 6.9470 | 3800 | 0.0038 | - |
| 7.0384 | 3850 | 0.0017 | - |
| 7.1298 | 3900 | 0.0015 | - |
| 7.2212 | 3950 | 0.0001 | - |
| 7.3126 | 4000 | 0.0 | - |
| 7.4040 | 4050 | 0.0 | - |
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| 29.9817 | 16400 | 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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