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---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 지푸드박스 제이엔제이트레이드 코코엘 유기농 엑스트라버진 코코넛오일 필리핀산 415ml 헬시푸드몰
- text: CJ 백설 2통 이상 구매시 할인 쿠폰 콩기름 식용유 대두유 18L 이츠웰 해표 오뚜기 롯데 식용유 말통 전국 최저가판매 식용유_오뚜기식용유
주식회사 황금알에프앤오
- text: 올리타리아 엑스트라버진 올리브오일 1L 카비스
- text: 커클랜드 시그니춰 카놀라유 오일 2.83L 커클랜드 카놀라유2.83L 베이비파크
- text: 해표)고추맛기름 1.8L 에스엠(SM)식자재도매센터
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: metric
value: 0.9926900584795322
name: Metric
---
# 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:** 9 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 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6.0 | <ul><li>'사조해표 해표 고급유 2호 선물세트 풀문'</li><li>'CJ 백설 프리미엄 23호 형제종합물류'</li><li>'노브랜드 카놀라유 1L 노브랜드 카놀라유2L 주식회사 유일글로벌'</li></ul> |
| 3.0 | <ul><li>'오타비오 아보카도오일 2L 이탈리아 코스트코 포시즌'</li><li>'건강한오늘 아보카도오일 500ml 건강한오늘 아보카도오일 500ml 잇츠설렘'</li><li>'아보퍼시픽 아보카도오일 1L 코스트코 1021670 굿데이'</li></ul> |
| 4.0 | <ul><li>'만능 올리브유 900ml 청정원 가을 식재료 추석 설날 제사 드레싱 샐러드 파스타 모두감동해'</li><li>'CJ제일제당 백설 압착 올리브유 900ml 준스토리'</li><li>'오로바일렌 엑스트라버진 올리브오일 아르베키나 500ml 500ml (주)운우'</li></ul> |
| 7.0 | <ul><li>'사조 해표 포도씨유 250ML 주식회사 킴벌리마스타'</li><li>'오뚜기 프레스코 포도씨유 900ml 주식회사 삼부'</li><li>'대상 청정원 포도씨유 900ml 주식회사 당장만나'</li></ul> |
| 2.0 | <ul><li>'국산 저온압착 들기름 300ml 국내산 아기들기름 저온압착 저온들기름 300ml 농부창고 영농조합법인'</li><li>'미식상회 생들기름 대용량 350ml 에프유니마켓'</li><li>'오뚜기 향긋한 들기름 160ml 1개 (주)하우'</li></ul> |
| 1.0 | <ul><li>'대용량 업소용 식용유 해표 콩 식용유 18L 선택04)오뚜기 콩 식용유 18L 소유앳홈(SO:YOU@Home)'</li><li>'CJ 백설 식용유 1.8L 해표 식용유 1.8L 주식회사 경일종합식품 케이마트몰'</li><li>'CJ 해피스푼 콩식용유 18L 업소용 대용량 저가 식용유 광주 말통 주식회사 케이제이플러스'</li></ul> |
| 0.0 | <ul><li>'캘리포니아골드뉴트리션 슈퍼푸드 오가닉 엑스트라 버진 코코넛 오일 473ml 액상 코코넛기름 에스지샵(SGshop)'</li><li>'참미정 파기름 1.8L 대파 맛기름 참미정 마늘기름 1.8L 주식회사 팜'</li><li>'시아스 불맛기름 화유 500ml 시아스 불맛 고추기름 500ml (주) 식자재민족'</li></ul> |
| 5.0 | <ul><li>'50년전통 대현상회 저온압착 참기름 350ml 돌려따는 BIG 아빠의주스 양배추사과즙 180 네오카트'</li><li>'오뚜기 고소한 참기름 450ml 오뚜기 고소한 참기름 320ml(병) 삼영유통'</li><li>'국산 저온압착 참기름 180ml 선물세트 이삭방앗간 당일착유 국산 저온압착 참기름_250ml 이삭방앗간'</li></ul> |
| 8.0 | <ul><li>'백설 해바라기씨유 500ml 당일 출발 (주) 바쿰'</li><li>'사조해표 해바라기유 500ml 1개 (주)해피상사'</li><li>'사조 해표 해바라기유 500ml (유통기한 24.01까지) ★유통기한임박특가(24년1월까지) 주식회사 킴벌리마스타'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.9927 |
## 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_fd12")
# Run inference
preds = model("올리타리아 엑스트라버진 올리브오일 1L 카비스")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 8.5356 | 22 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 50 |
| 1.0 | 50 |
| 2.0 | 50 |
| 3.0 | 50 |
| 4.0 | 50 |
| 5.0 | 50 |
| 6.0 | 50 |
| 7.0 | 50 |
| 8.0 | 50 |
### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0141 | 1 | 0.4844 | - |
| 0.7042 | 50 | 0.3408 | - |
| 1.4085 | 100 | 0.0769 | - |
| 2.1127 | 150 | 0.0298 | - |
| 2.8169 | 200 | 0.023 | - |
| 3.5211 | 250 | 0.0251 | - |
| 4.2254 | 300 | 0.0291 | - |
| 4.9296 | 350 | 0.0156 | - |
| 5.6338 | 400 | 0.0137 | - |
| 6.3380 | 450 | 0.0029 | - |
| 7.0423 | 500 | 0.0001 | - |
| 7.7465 | 550 | 0.0001 | - |
| 8.4507 | 600 | 0.0001 | - |
| 9.1549 | 650 | 0.0 | - |
| 9.8592 | 700 | 0.0 | - |
| 10.5634 | 750 | 0.0 | - |
| 11.2676 | 800 | 0.0 | - |
| 11.9718 | 850 | 0.0 | - |
| 12.6761 | 900 | 0.0 | - |
| 13.3803 | 950 | 0.0 | - |
| 14.0845 | 1000 | 0.0 | - |
| 14.7887 | 1050 | 0.0 | - |
| 15.4930 | 1100 | 0.0 | - |
| 16.1972 | 1150 | 0.0 | - |
| 16.9014 | 1200 | 0.0 | - |
| 17.6056 | 1250 | 0.0 | - |
| 18.3099 | 1300 | 0.0 | - |
| 19.0141 | 1350 | 0.0 | - |
| 19.7183 | 1400 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.46.1
- PyTorch: 2.4.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.20.0
## 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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