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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: 듄 포 맨 오 드 뚜왈렛 (100ml) LotteOn > 뷰티 > 향수 > 남성향수 LotteOn > 뷰티 > 향수 > 남성향수
- text: 오랑쥬 상긴느 200ml (증정) 울랑 앙피니 30ml_마젠타 LotteOn > 뷰티 > 베이스메이크업 > 향수/디퓨저 > 공용향수
LotteOn > 뷰티 > 명품화장품 > 향수/디퓨저 > 공용향수
- text: 디올 블루밍 부케 롤러 펄 오 드 뚜왈렛 20ml LotteOn > 뷰티 > 향수 > 여성향수 LotteOn > 뷰티 > 향수 >
여성향수
- text: 포멜로 파라디 30ml +1.7ml 1종 마젠타 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded
> 아틀리에 코롱 DepartmentLotteOn > 뷰티 > 향수 > 향수세트
- text: 베르가모트 솔레이 200ml (증정) 울랑 앙피니 30ml_블랙 LOREAL > DepartmentLotteOn > 아틀리에 코롱 >
Branded > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱
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: 0.5334819796768769
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>'마타바 석고방향제 만들기 diy 재료모음 05_석고전용색소_01_석고전용색소50ml_노랑 (#M)11st>과자/간식>초콜릿>초콜릿DIY>도구 기타 11st > 식품 > 과자/간식 > 초콜릿 > 초콜릿DIY'</li><li>'A 시그니처 디퓨저 1+1 프로모션 네롤리바질_피오니 LotteOn > 생활/건강 > 세제/방향/살충 > 방향제 LotteOn > 생활/건강 > 세제/방향/살충 > 방향제'</li><li>'마타바 석고방향제 만들기 diy 재료모음 01_스위스G향료100ml_멋스럽고세련된향기_69_인투유 (#M)11st>과자/간식>초콜릿>초콜릿DIY>도구 기타 11st > 식품 > 과자/간식 > 초콜릿 > 초콜릿DIY'</li></ul> |
| 0 | <ul><li>'아틀리에 코롱 - 자스민 안젤리크 코롱 압솔뤼 스프레이 100ml/3.3oz LOREAL > Ssg > 아틀리에 코롱 > Branded > 아틀리에 코롱 LOREAL > Ssg > 아틀리에 코롱 > Branded > 아틀리에 코롱'</li><li>'베티베르 파탈 200ml (증정) 아이리스 리벨 30ml LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱'</li><li>'포멜로 파라디 30ml +1.7ml 1종 코랄 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱 DepartmentLotteOn > 뷰티 > 향수 > 향수세트'</li></ul> |
| 2 | <ul><li>'베르가모트 솔레이 200ml (증정) 러브 오스만투스 30ml_오랑쥬 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱'</li><li>'포멜로 파라디 100ml 코랄 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 포멜로 파라디 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 포멜로 파라디'</li><li>'베르가모트 솔레이 200ml (증정) 클레망틴 캘리포니아 30ml_마젠타 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱'</li></ul> |
| 1 | <ul><li>'불가리 뿌르 옴므 익스트림 EDT 50ml LotteOn > 뷰티 > 향수 > 남성향수 LotteOn > 뷰티 > 향수 > 남성향수'</li><li>'조르지오 아르마니 아쿠아 디 지오 옴므 세트 EDT 100ml + 트레블 15ml (#M)위메프 > 뷰티 > 명품화장품 > 메이크업 > 립메이크업 위메프 > 뷰티 > 명품화장품 > 스킨케어'</li><li>'불가리 뿌르옴므 익스트림 100ml 50ml 30ml 백화점정품 50ml 백화점정품 홈>화장품/미용>향수>남성향수;(#M)홈>남자향수>불가리 Naverstore > 화장품/미용 > 향수 > 남성향수'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.5335 |
## 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_item_top_bt11")
# Run inference
preds = model("듄 포 맨 오 드 뚜왈렛 (100ml) LotteOn > 뷰티 > 향수 > 남성향수 LotteOn > 뷰티 > 향수 > 남성향수")
```
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### Downstream Use
*List how someone could finetune this model on their own dataset.*
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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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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 11 | 26.41 | 45 |
| 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 | 0.395 | - |
| 0.1597 | 50 | 0.3286 | - |
| 0.3195 | 100 | 0.2663 | - |
| 0.4792 | 150 | 0.2215 | - |
| 0.6390 | 200 | 0.1928 | - |
| 0.7987 | 250 | 0.081 | - |
| 0.9585 | 300 | 0.0147 | - |
| 1.1182 | 350 | 0.0027 | - |
| 1.2780 | 400 | 0.0008 | - |
| 1.4377 | 450 | 0.0004 | - |
| 1.5974 | 500 | 0.0006 | - |
| 1.7572 | 550 | 0.0003 | - |
| 1.9169 | 600 | 0.0001 | - |
| 2.0767 | 650 | 0.0001 | - |
| 2.2364 | 700 | 0.0001 | - |
| 2.3962 | 750 | 0.0001 | - |
| 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 | - |
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| 29.8722 | 9350 | 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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