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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: 더툴랩 더스타일래쉬 4종리얼/내츄럴/볼륨/맥스 중 택1 002 내츄럴 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 >
브로우관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 브로우관리
- text: 속눈썹연장재료 래쉬홀릭 벨벳속눈썹 CC(D)컬 + 해면 1장_0.15_11mm (#M)홈>속눈썹연장>속눈썹 Naverstore > 화장품/미용
> 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제
- text: 더툴랩 더스타일래쉬 4종(리얼/내츄럴/볼륨/맥스) 중 택1 004 맥스 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품
> 속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리
- text: 더툴랩 더스타일래쉬 4종리얼/내츄럴/볼륨/맥스 중 택1 004 맥스 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 >
속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리
- text: 더툴랩 더스타일래쉬 4종(리얼/내츄럴/볼륨/맥스) 중 택1 004 맥스 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품
> 속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리
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: 0.9740634005763689
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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4 | <ul><li>'더툴랩 더스타일래쉬 4종(리얼/내츄럴/볼륨/맥스) 중 택1 003 볼륨 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리'</li><li>'미샤 시크릿 래쉬 - 1호 디어 (#M)홈>화장품/미용>뷰티소품>아이소품>속눈썹/속눈썹펌제 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제'</li><li>'아던샵 시즌1 부분 인조속눈썹 11mm 다크브라운 × 1개 LotteOn > 뷰티 > 스킨케어 > 아이케어/넥케어 LotteOn > 뷰티 > 스킨케어 > 아이케어/넥케어'</li></ul> |
| 1 | <ul><li>'트위저맨 스테인리스 브로우 셰이핑 시져 브러쉬 70238 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머'</li><li>'트위저맨 - 스텐리스 스틸 브로우 셰이핑 가위 & 브러쉬 (스튜디오 컬렉션) 2pcs ssg > 뷰티 > 헤어/바디 > 헤어기기/소품 > 드라이기 ssg > 뷰티 > 헤어/바디 > 헤어기기/소품 > 드라이기'</li><li>'트위저맨 Tweezerman 스테인리스 브로우 쉐이핑 가위 및 브러시 521626 (#M)홈>화장품/미용>뷰티소품>헤어소품>미용가위 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 > 미용가위'</li></ul> |
| 0 | <ul><li>'필리밀리 속눈썹 접착제 (블랙) 필리밀리 속눈썹 접착제 (블랙) 홈>미용소품>얼굴소품>속눈썹;(#M)홈>미용소품>아이>속눈썹/쌍꺼풀 OLIVEYOUNG > 미용소품 > 아이 > 속눈썹/쌍꺼풀'</li><li>'에뛰드하우스 마이뷰티툴 쌍꺼풀 액 속눈썹 접착제 MinSellAmount (#M)화장품/향수>이미용소품>쌍꺼풀 Gmarket > 뷰티 > 화장품/향수 > 이미용소품 > 쌍꺼풀'</li><li>'마이뷰티툴 속눈썹 빗 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬'</li></ul> |
| 2 | <ul><li>'히팅 아이래쉬 컬러 듀얼 콤 LotteOn > 뷰티 > 뷰티소품 > 아이소품 LotteOn > 뷰티 > 뷰티소품 > 아이소품 > 속눈썹관리소품'</li><li>'트위저맨 슈퍼 컬 아이래쉬 컬러 1개 1개 LotteOn > 뷰티 > 뷰티소품 > 페이스소품 > 브러쉬 LotteOn > 뷰티 > 뷰티소품 > 페이스소품 > 브러쉬'</li><li>'아이래쉬 컬러 (EYELASH CURLER) - 리필 패드 6819925000200 블랙_F ssg > 뷰티 > 메이크업 > 베이스메이크업 > BB/CC크림 ssg > 뷰티 > 메이크업 > 베이스메이크업 > BB/CC크림'</li></ul> |
| 3 | <ul><li>'맥펜슬 샤프너[백화점 제품] ( /선물포장가능) (#M)홈>맥>백화점 정품 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 샤프너'</li><li>'e.l.f. 듀얼 펜슬 샤프너 4세트 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>족집게/샤프너 Coupang > 뷰티 > 뷰티소품 > 아이소품 > 족집게/샤프너'</li><li>'e.l.f. 듀얼 펜슬 샤프너 5세트 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>족집게/샤프너 Coupang > 뷰티 > 뷰티소품 > 아이소품 > 족집게/샤프너'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9741 |
## 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_bt6_3_test_flat")
# Run inference
preds = model("속눈썹연장재료 래쉬홀릭 벨벳속눈썹 CC(D)컬 + 해면 1장_0.15_11mm (#M)홈>속눈썹연장>속눈썹 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제")
```
<!--
### 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 | 13 | 19.4972 | 47 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 9 |
| 2 | 50 |
| 3 | 22 |
| 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0035 | 1 | 0.4006 | - |
| 0.1767 | 50 | 0.4188 | - |
| 0.3534 | 100 | 0.3674 | - |
| 0.5300 | 150 | 0.3054 | - |
| 0.7067 | 200 | 0.2262 | - |
| 0.8834 | 250 | 0.089 | - |
| 1.0601 | 300 | 0.0198 | - |
| 1.2367 | 350 | 0.0042 | - |
| 1.4134 | 400 | 0.0019 | - |
| 1.5901 | 450 | 0.0009 | - |
| 1.7668 | 500 | 0.0005 | - |
| 1.9435 | 550 | 0.0005 | - |
| 2.1201 | 600 | 0.0003 | - |
| 2.2968 | 650 | 0.0006 | - |
| 2.4735 | 700 | 0.0012 | - |
| 2.6502 | 750 | 0.0002 | - |
| 2.8269 | 800 | 0.0001 | - |
| 3.0035 | 850 | 0.0001 | - |
| 3.1802 | 900 | 0.0 | - |
| 3.3569 | 950 | 0.0 | - |
| 3.5336 | 1000 | 0.0 | - |
| 3.7102 | 1050 | 0.0 | - |
| 3.8869 | 1100 | 0.0 | - |
| 4.0636 | 1150 | 0.0 | - |
| 4.2403 | 1200 | 0.0 | - |
| 4.4170 | 1250 | 0.0 | - |
| 4.5936 | 1300 | 0.0 | - |
| 4.7703 | 1350 | 0.0 | - |
| 4.9470 | 1400 | 0.0 | - |
| 5.1237 | 1450 | 0.0 | - |
| 5.3004 | 1500 | 0.0 | - |
| 5.4770 | 1550 | 0.0 | - |
| 5.6537 | 1600 | 0.0 | - |
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| 6.8905 | 1950 | 0.0002 | - |
| 7.0671 | 2000 | 0.0003 | - |
| 7.2438 | 2050 | 0.0026 | - |
| 7.4205 | 2100 | 0.0005 | - |
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| 29.3286 | 8300 | 0.0 | - |
| 29.5053 | 8350 | 0.0 | - |
| 29.6820 | 8400 | 0.0 | - |
| 29.8587 | 8450 | 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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