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---
base_model: klue/roberta-base
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '[시세이도프로페셔널/손상모발용] 서브리믹 아쿠아 인텐시브 샴푸 (리필) 450ml DepartmentSsg > 명품화장품 > 향수/바디/헤어
> 헤어케어 > 샴푸/트리트먼트 DepartmentSsg > 명품화장품 > 향수/바디/헤어 > 헤어케어 > 샴푸/트리트먼트'
- text: 제이숲 컬러제이 오로라 보색 샴푸 핑크 380ml C02 오로라 보색 샴푸 (핑크) (#M)화장품/미용>헤어케어>샴푸 AD > traverse
> Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 보색샴푸
- text: '정샘물 살롱집 단백질 리차징 샴푸 1,000ml [ : 트리트먼트] 리차징 샴푸 1000ml+트리트먼트 (#M)화장품/미용>헤어케어>샴푸
AD > Naverstore > 화장품/미용 > 시트러스 > 샴푸'
- text: '[유니크앤몰] 미쟝센 에이징 케어 파워베리 샴푸 1000ml 1000ml × 3개 (#M)쿠팡 홈>생활용품>헤어/바디/세안>샴푸/린스>샴푸>일반샴푸
Coupang > 뷰티 > 헤어 > 샴푸 > 일반샴푸'
- text: 히말라야 허브나드 두피 쿨링 샴푸 900ml x2개 (#M)11st>헤어케어>샴푸>기능성 11st > 뷰티 > 헤어케어 > 샴푸 >
기능성
inference: true
model-index:
- name: SetFit with klue/roberta-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8699763593380615
name: Accuracy
---
# SetFit with klue/roberta-base
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) 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:** [klue/roberta-base](https://huggingface.co/klue/roberta-base)
- **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>'라보에이치 탈모증상완화 두피강화 샴푸 페어앤프리지아 400ml × 2개 (#M)쿠팡 홈>생활용품>헤어/바디/세안>샴푸/린스>샴푸>기능성샴푸 Coupang > 뷰티 > 헤어 > 샴푸 > 기능성샴푸'</li><li>'[라보에이치] 탈모증상완화 샴푸 댄드러프클리닉 일반건성비듬 400ml (#M)11st>헤어케어>탈모/두피관리제>헤어토닉 11st > 뷰티 > 헤어케어 > 탈모/두피관리제 > 헤어토닉'</li><li>'탈모완화 기능성 헤모나후코이단샴푸 460ml 300ml 헤모나샴푸 460ml (#M)11st>헤어케어>샴푸>기능성 11st > 뷰티 > 헤어케어 > 샴푸 > 기능성'</li></ul> |
| 0 | <ul><li>'코랩 드라이 샴푸 200ml2개 50ml2개 물없이감는 사춘기 초등학생 청소년 올리브영 유니콘 (#M)홈>화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸'</li><li>'1+1 코랩 올리브영 드라이샴푸 프레쉬 200ml 오리지널_유니콘 (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 드라이샴푸'</li><li>'바티스트 드라이샴푸 11종 중 택1 04_스위티 200ml 11st>헤어케어>샴푸>일반;11st > 뷰티 > 헤어케어 > 샴푸 11st > 뷰티 > 헤어케어 > 샴푸 > 일반'</li></ul> |
| 2 | <ul><li>'엘라스틴 실크리페어 퍼펙트 샤이닝 샴푸 1200ml x2개 ssg > 뷰티 > 미용기기/소품 > 바디관리기기;ssg > 뷰티 > 헤어/바디 > 헤어케어 > 샴푸;ssg > 뷰티 > 헤어/바디 > 헤어케어 ssg > 뷰티 > 헤어/바디 > 세정/입욕용품 > 바디워시'</li><li>'오가니스트 티트리 비듬 세정 샴푸 500ml x3개 MinSellAmount (#M)바디/헤어>헤어케어>샴푸/린스 Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 샴푸/린스'</li><li>'오가니스트 체리블라썸 샴푸 500ml x 3개 LotteOn > 뷰티 > 헤어케어 > 샴푸 > 샴푸 LotteOn > 뷰티 > 헤어케어 > 샴푸 > 드라이샴푸'</li></ul> |
| 1 | <ul><li>'자연이랑 약산성 샴푸바2+린스바1 특별가/제로웨이스트 올인원 고체샴푸/고체린스 청대오일샴푸바2+린스바1_선택안함 (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 샴푸바'</li><li>'케세리 샴푸바 2개입 100g+100g 비건 퍼퓸 약산성 올인원 비누 [모발영양]딥 너리싱 샴푸바 200g (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 시트러스 > 샴푸'</li><li>'어성초자소엽녹차 샴푸바 로즈마리 자연 샴푸바 1개 (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 샴푸바'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8700 |
## 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_bt13_3")
# Run inference
preds = model("히말라야 허브나드 두피 쿨링 샴푸 900ml x2개 (#M)11st>헤어케어>샴푸>기능성 11st > 뷰티 > 헤어케어 > 샴푸 > 기능성")
```
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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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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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 11 | 24.02 | 122 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 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 | 0.4979 | - |
| 0.1597 | 50 | 0.4236 | - |
| 0.3195 | 100 | 0.3685 | - |
| 0.4792 | 150 | 0.2863 | - |
| 0.6390 | 200 | 0.1407 | - |
| 0.7987 | 250 | 0.0265 | - |
| 0.9585 | 300 | 0.0059 | - |
| 1.1182 | 350 | 0.0046 | - |
| 1.2780 | 400 | 0.0003 | - |
| 1.4377 | 450 | 0.0001 | - |
| 1.5974 | 500 | 0.0002 | - |
| 1.7572 | 550 | 0.0001 | - |
| 1.9169 | 600 | 0.0001 | - |
| 2.0767 | 650 | 0.0001 | - |
| 2.2364 | 700 | 0.0001 | - |
| 2.3962 | 750 | 0.0 | - |
| 2.5559 | 800 | 0.0 | - |
| 2.7157 | 850 | 0.0 | - |
| 2.8754 | 900 | 0.0 | - |
| 3.0351 | 950 | 0.0 | - |
| 3.1949 | 1000 | 0.0 | - |
| 3.3546 | 1050 | 0.0 | - |
| 3.5144 | 1100 | 0.0 | - |
| 3.6741 | 1150 | 0.0 | - |
| 3.8339 | 1200 | 0.0 | - |
| 3.9936 | 1250 | 0.0 | - |
| 4.1534 | 1300 | 0.0 | - |
| 4.3131 | 1350 | 0.0 | - |
| 4.4728 | 1400 | 0.0 | - |
| 4.6326 | 1450 | 0.0 | - |
| 4.7923 | 1500 | 0.0 | - |
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| 7.0288 | 2200 | 0.0007 | - |
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### 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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