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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: 밀크바오밥 오리지널 샴푸 베이비파우더 1L 09_트리트먼트 화이트머스크 1000ml (#M)화장품/미용>헤어케어>샴푸 AD > Naverstore
> 화장품/미용 > 헤어케어 > 샴푸 > 약산성샴푸
- text: 무코타염색제 7박스+3박스+정품 트리트먼트 50g 1.카키브라운 (#M)바디/헤어>바디케어>바디케어세트 Gmarket > 뷰티 > 바디/헤어
> 바디케어 > 바디케어세트
- text: 1+1세트~(컨센+릴렉스마스크100ml) 에스테티카 데미지 케어 컨센트레이트 120ml (열활성 열보호 에센스) 정품 + 릴렉스마스크100ml
1개 (#M)쿠팡 홈>싱글라이프>샤워/세안>헤어에센스 Coupang > 뷰티 > 헤어 > 헤어에센스/오일 > 헤어에센스
- text: 헤드스파7 트리트먼트 더 프리미엄 210ml + 210ml MinSellAmount (#M)바디/헤어>헤어케어>헤어트리트먼트 Gmarket
> 뷰티 > 바디/헤어 > 헤어케어 > 헤어트리트먼트
- text: 헤어플러스 실크 코팅 트리트먼트 50ml 4개 실크 코팅 트리트먼트 50ml 4개 위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어
> 트리트먼트;위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어;위메프 > 뷰티 > 바디/헤어 > 샴푸/린스/헤어케어 >
샴푸/린스;(#M)위메프 > 생활·주방용품 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트 위메프 > 뷰티 > 바디/헤어 > 샴푸/린스/헤어케어
> 트리트먼트
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: 0.8206115779645191
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:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### 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 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | <ul><li>'로레알파리 토탈리페어5 트리트먼트 헤어팩 170ml × 1개 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩'</li><li>'아모스 녹차실감 인텐시브 팩 250ml 녹차실감 인텐시브팩250g 홈>전체상품;(#M)홈>녹차실감 Naverstore > 화장품/미용 > 헤어케어 > 헤어팩'</li><li>'프리미엄 헤어클리닉 헤어팩 258ml 베이비파우더 LotteOn > 뷰티 > 헤어케어 > 헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩'</li></ul> |
| 0 | <ul><li>'퓨어시카 트리트먼트 베이비파우더향 1000ml 1개 MinSellAmount 스마일배송 홈>뷰티>바디케어>바디워시;스마일배송 홈>;(#M)스마일배송 홈>뷰티>헤어케어/스타일링>트리트먼트/팩 Gmarket > 뷰티 > 바디/헤어 > 바디케어 > 바디클렌저'</li><li>'1+1 살림백서 탈모 샴푸 엑티브B7 맥주효모 앤 비오틴 1000ml 남자 여자 바이오틴 4)오푼티아 트리트먼트 유칼립투스 1L (#M)화장품/미용>헤어케어>탈모케어 AD > Naverstore > 화장품/미용 > 가을뷰티 > 각질관리템 > 탈모샴푸'</li><li>'1+1 살림백서 오푼티아 퍼퓸 샴푸 500ml 약산성 비듬 지성 두피 볼륨 유칼립투스향 13.유칼립투스 트리트먼트 1+1 500ml (#M)화장품/미용>헤어케어>샴푸 AD > Naverstore > 화장품/미용 > 머스크 > 샴푸'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8206 |
## 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_9")
# Run inference
preds = model("무코타염색제 7박스+3박스+정품 트리트먼트 50g 1.카키브라운 (#M)바디/헤어>바디케어>바디케어세트 Gmarket > 뷰티 > 바디/헤어 > 바디케어 > 바디케어세트")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 14 | 23.76 | 98 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0064 | 1 | 0.4326 | - |
| 0.3185 | 50 | 0.3579 | - |
| 0.6369 | 100 | 0.2616 | - |
| 0.9554 | 150 | 0.0326 | - |
| 1.2739 | 200 | 0.0 | - |
| 1.5924 | 250 | 0.0 | - |
| 1.9108 | 300 | 0.0 | - |
| 2.2293 | 350 | 0.0 | - |
| 2.5478 | 400 | 0.0 | - |
| 2.8662 | 450 | 0.0 | - |
| 3.1847 | 500 | 0.0 | - |
| 3.5032 | 550 | 0.0 | - |
| 3.8217 | 600 | 0.0 | - |
| 4.1401 | 650 | 0.0 | - |
| 4.4586 | 700 | 0.0 | - |
| 4.7771 | 750 | 0.0 | - |
| 5.0955 | 800 | 0.0 | - |
| 5.4140 | 850 | 0.0 | - |
| 5.7325 | 900 | 0.0 | - |
| 6.0510 | 950 | 0.0 | - |
| 6.3694 | 1000 | 0.0 | - |
| 6.6879 | 1050 | 0.0 | - |
| 7.0064 | 1100 | 0.0 | - |
| 7.3248 | 1150 | 0.0 | - |
| 7.6433 | 1200 | 0.0 | - |
| 7.9618 | 1250 | 0.0 | - |
| 8.2803 | 1300 | 0.0 | - |
| 8.5987 | 1350 | 0.0 | - |
| 8.9172 | 1400 | 0.0 | - |
| 9.2357 | 1450 | 0.0 | - |
| 9.5541 | 1500 | 0.0 | - |
| 9.8726 | 1550 | 0.0 | - |
| 10.1911 | 1600 | 0.0 | - |
| 10.5096 | 1650 | 0.0 | - |
| 10.8280 | 1700 | 0.0 | - |
| 11.1465 | 1750 | 0.0 | - |
| 11.4650 | 1800 | 0.0 | - |
| 11.7834 | 1850 | 0.0 | - |
| 12.1019 | 1900 | 0.0 | - |
| 12.4204 | 1950 | 0.0 | - |
| 12.7389 | 2000 | 0.0 | - |
| 13.0573 | 2050 | 0.0 | - |
| 13.3758 | 2100 | 0.0 | - |
| 13.6943 | 2150 | 0.0 | - |
| 14.0127 | 2200 | 0.0 | - |
| 14.3312 | 2250 | 0.0 | - |
| 14.6497 | 2300 | 0.0 | - |
| 14.9682 | 2350 | 0.0 | - |
| 15.2866 | 2400 | 0.0 | - |
| 15.6051 | 2450 | 0.0 | - |
| 15.9236 | 2500 | 0.0 | - |
| 16.2420 | 2550 | 0.0 | - |
| 16.5605 | 2600 | 0.0 | - |
| 16.8790 | 2650 | 0.0 | - |
| 17.1975 | 2700 | 0.0 | - |
| 17.5159 | 2750 | 0.0 | - |
| 17.8344 | 2800 | 0.0 | - |
| 18.1529 | 2850 | 0.0 | - |
| 18.4713 | 2900 | 0.0 | - |
| 18.7898 | 2950 | 0.0 | - |
| 19.1083 | 3000 | 0.0 | - |
| 19.4268 | 3050 | 0.0 | - |
| 19.7452 | 3100 | 0.0 | - |
| 20.0637 | 3150 | 0.0 | - |
| 20.3822 | 3200 | 0.0 | - |
| 20.7006 | 3250 | 0.0 | - |
| 21.0191 | 3300 | 0.0 | - |
| 21.3376 | 3350 | 0.0 | - |
| 21.6561 | 3400 | 0.0 | - |
| 21.9745 | 3450 | 0.0 | - |
| 22.2930 | 3500 | 0.0 | - |
| 22.6115 | 3550 | 0.0 | - |
| 22.9299 | 3600 | 0.0 | - |
| 23.2484 | 3650 | 0.0 | - |
| 23.5669 | 3700 | 0.0 | - |
| 23.8854 | 3750 | 0.0 | - |
| 24.2038 | 3800 | 0.0 | - |
| 24.5223 | 3850 | 0.0 | - |
| 24.8408 | 3900 | 0.0 | - |
| 25.1592 | 3950 | 0.0 | - |
| 25.4777 | 4000 | 0.0 | - |
| 25.7962 | 4050 | 0.0 | - |
| 26.1146 | 4100 | 0.0 | - |
| 26.4331 | 4150 | 0.0 | - |
| 26.7516 | 4200 | 0.0 | - |
| 27.0701 | 4250 | 0.0 | - |
| 27.3885 | 4300 | 0.0 | - |
| 27.7070 | 4350 | 0.0 | - |
| 28.0255 | 4400 | 0.0 | - |
| 28.3439 | 4450 | 0.0 | - |
| 28.6624 | 4500 | 0.0 | - |
| 28.9809 | 4550 | 0.0 | - |
| 29.2994 | 4600 | 0.0 | - |
| 29.6178 | 4650 | 0.0 | - |
| 29.9363 | 4700 | 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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