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master_cate_fd18

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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: 동성 순후추 1KG 주식회사 청춘에프앤비
- text: 오뚜기 2배사과식초 1.8L (주) 식자재민족
- text: 무화당 알룰로스 분말 250g (주)닥터다이어리
- text: 마이노멀 알룰로스 485g 메인루트
- text: 오뚜기 순후추 캔 100g 주식회사 두위드(Do With)
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: 0.9504337050805453
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:** 12 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 10.0 | <ul><li>'설탕대신 스테비아 1.2kg 주식회사 더 골든트리'</li><li>'자연지애 에리스리톨 1:1 눈꽃 스테비아 1kg 설탕대체 당뇨설탕 당류 제로 2. 1kg x 2개 주식회사 닥터스랩'</li><li>'바이오믹스 설탕대신 자일리톨 180g 주식회사 와식자재마트(민락지점)'</li></ul> |
| 8.0 | <ul><li>'커클랜드 발사믹 식초 1L 코스트코 ▶▶▶전국 초완벽 뽁뽁이 택배◀◀◀ 브라이튼'</li><li>'롯데 미림 18L 말통 (맛술, 요리용 요리주) 와사비푸드'</li><li>'롯데 미림 1.8L 맛술 요리용 요리주 1개 블레스(Bless)'</li></ul> |
| 5.0 | <ul><li>'정경아 생강 조청 550g 무설탕 생강청 차 즙 엿 속쓰림 수제조청 엿기름 쌀조청 답례품 2. 스틱형 생강조청 33개 (1만원 할인) 정드림'</li><li>'CJ 백설 요리당 2.45kg 조림 무침 구이 에스비푸드시스템'</li><li>'오뚜기 옛날 물엿 5kg 솔브이트코리아'</li></ul> |
| 0.0 | <ul><li>'태산식품 일회용 맛미 겨자소스 3g 200개 미니간장200개입 다온'</li><li>'오뚜기 오쉐프 연겨자 480g 튜브 주식회사 두위드(Do With)'</li><li>'오뚜기 오쉐프 연겨자 480g 주식회사 데일즈'</li></ul> |
| 6.0 | <ul><li>'［DA85］큐원 하얀설탕(실온 3Kg) 기화유통'</li><li>'CJ제일제당 백설 브라운 자일로스설탕 5kg 오늘의 컨셉'</li><li>'CJ 백설 하얀설탕 1kg 매실 대용량 청 제빵용 에스비푸드시스템'</li></ul> |
| 11.0 | <ul><li>'흑후추가루(서원 200g)/강황가루/후추1kg/가루쌀빵/햇고추가루/후추그라인더/후추가루1KG/tnsgncn/todnrkfn (주)큐원상사'</li><li>'오뚜기 직접갈아먹는 통후추(리필용) 소스 조미료 고기 삼겹살 목살 통후추 스테이크 35G 1세트 청주그릇주방설비'</li><li>'청정원 향신료 잡내제거 천연 순후추 100g 육류요리 생선요리 알싸한풍미 지니마켓'</li></ul> |
| 2.0 | <ul><li>'경상북도 영양 명가 고추가루 매운맛 1kg (2023년산) -인증 시안무역'</li><li>'델라미코 크러쉬드 레드페퍼 크러쉬드 레드페퍼 370g 두두유통'</li><li>'청정식품 23년 국산 고운 햇 고춧가루 1kg CJA001-99_(청양)고운 고추가루 1kg 유한킴벌리 에스와이'</li></ul> |
| 9.0 | <ul><li>'한라식품 프리미엄참치액500ml 11203420 프리미엄참치액 세론세론'</li><li>'CJ제일제당 백설 참치액 진 더 풍부한 맛 900g 둘레푸드'</li><li>'티파로스 피쉬소스 700ml (태국 멸치액젓 남쁠라 느억맘소스) 팝스이엔티'</li></ul> |
| 1.0 | <ul><li>'움트리 705 고추냉이 700g 청비 알맹이 생고추냉이 700g 주식회사 팜'</li><li>'청비 생고추냉이 700g 생와사비 와사비 청비 뿌리갈은 생고추냉이 700g 주식회사 팜'</li><li>'삼광999 생와사비 750g 제루통상'</li></ul> |
| 4.0 | <ul><li>'[나가타니엔] 오토나노 후리카케 미니 2종 컬리'</li><li>'일본 후리카케 밥 주먹밥 혼가쓰오 나가타니엔 일본 오차즈케가루 매크로온'</li><li>'마루미야 노리타마 후리카케 28g 오차즈케 1초재팬'</li></ul> |
| 3.0 | <ul><li>'코스트코 맥코믹 몬트리얼 스테이크 시즈닝 822g 1개 주식회사베이비또'</li><li>'샘표 연두 요리에센스 순 860ml 달달구리'</li><li>'해통령 육수한알 진한맛 25입 100g 트레이더스 스마일유통'</li></ul> |
| 7.0 | <ul><li>'CJ제일제당 백설 허브맛 솔트 매콤한맛 50g 허브솔트매콤한맛 화진유통'</li><li>'백설 허브맛솔트시즈닝 매콤한맛 50g 주식회사 팩앤폴스'</li><li>'[백설]오천년의 신비 명품 천일염 (가는 입자) 250g (영등포점) 주식회사 에스에스지닷컴'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.9504 |
## 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_fd18")
# Run inference
preds = model("마이노멀 알룰로스 485g 메인루트")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 9.1646 | 29 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 50 |
| 1.0 | 50 |
| 2.0 | 50 |
| 3.0 | 50 |
| 4.0 | 21 |
| 5.0 | 50 |
| 6.0 | 50 |
| 7.0 | 50 |
| 8.0 | 50 |
| 9.0 | 50 |
| 10.0 | 50 |
| 11.0 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0111 | 1 | 0.4135 | - |
| 0.5556 | 50 | 0.3821 | - |
| 1.1111 | 100 | 0.0967 | - |
| 1.6667 | 150 | 0.0493 | - |
| 2.2222 | 200 | 0.0399 | - |
| 2.7778 | 250 | 0.0149 | - |
| 3.3333 | 300 | 0.0107 | - |
| 3.8889 | 350 | 0.01 | - |
| 4.4444 | 400 | 0.0116 | - |
| 5.0 | 450 | 0.0078 | - |
| 5.5556 | 500 | 0.0001 | - |
| 6.1111 | 550 | 0.0001 | - |
| 6.6667 | 600 | 0.0001 | - |
| 7.2222 | 650 | 0.0001 | - |
| 7.7778 | 700 | 0.0001 | - |
| 8.3333 | 750 | 0.0001 | - |
| 8.8889 | 800 | 0.0001 | - |
| 9.4444 | 850 | 0.0001 | - |
| 10.0 | 900 | 0.0001 | - |
| 10.5556 | 950 | 0.0 | - |
| 11.1111 | 1000 | 0.0 | - |
| 11.6667 | 1050 | 0.0 | - |
| 12.2222 | 1100 | 0.0 | - |
| 12.7778 | 1150 | 0.0 | - |
| 13.3333 | 1200 | 0.0 | - |
| 13.8889 | 1250 | 0.0 | - |
| 14.4444 | 1300 | 0.0 | - |
| 15.0 | 1350 | 0.0 | - |
| 15.5556 | 1400 | 0.0 | - |
| 16.1111 | 1450 | 0.0 | - |
| 16.6667 | 1500 | 0.0 | - |
| 17.2222 | 1550 | 0.0 | - |
| 17.7778 | 1600 | 0.0 | - |
| 18.3333 | 1650 | 0.0 | - |
| 18.8889 | 1700 | 0.0 | - |
| 19.4444 | 1750 | 0.0 | - |
| 20.0 | 1800 | 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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