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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: LG전자 24V50N-GR35K 정윤아
- text: '[윈도우11 홈] 이그닉 비와이 프로 27Y 2535 (5년 A/S) 게이밍 일체형 PC NVMe 1TB_16GB RAM 이그닉 주식회사'
- text: Dell 옵티플렉스 7020MFF i3-14100T 사무용 업무용 마이크로 폼펙터 초소형 PC 키보드 마우스 포함 주식회사 아이딜컴퍼니
- text: i5 13400F RX6600 본체 게이밍 PC 컴퓨터 G346A 1.G20-블랙_기본선택 애즈락 B610M D5 리메이드 컴퓨터
- text: 삼성전자 데스크탑 DM500TEA-A58A 컴퓨터 인텔i5-12세대 윈도우11홈 강의 재택근무 사무용 주식회사 에스씨엔씨
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: 0.8841463414634146
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:** 3 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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2 | <ul><li>'몬스타기어 7500F 4070 SUPER 32G 500GB 조립PC AMD 7500F 4070SUPER 32G 500GB 몬스타 주식회사'</li><li>'사무용 주식 인텔 i3 12100F/GT710/SSD 250G/8G 조립컴퓨터 컴퓨터본체 데스크탑 컴퓨터 조립PC 기본사양(추가구성에서 사양변경 가능) (주)아싸컴'</li><li>'장우컴 가정용 PC (13100F/8G/GT1030/256G) i40207 (주)장우컴퍼니'</li></ul> |
| 0 | <ul><li>'T) DELL 옵티플렉스 7010SFF-UB02KR (NVMe 512G 교체 장착) 윈11프로 DSP설치 으뜸'</li><li>'이그닉 비와이 프로 27Y 4535 OS 미포함 NVMe 512G + 16GB RAM (5년 A／S) 빌리어네어에프'</li><li>'10만원 쿠폰💖 삼성 DM500TFA-A78A 데스크탑 인텔 13세대 i7 [기본제품] (주)컴퓨존'</li></ul> |
| 1 | <ul><li>'레노버 씽크스테이션 P620 라이젠 스레드리퍼 프로 5945WX RAM16GB SSD256GB NVMe HDD1TB NOVGA Win11 Pro (주)디지탈노뜨'</li><li>'[Dell] Precision 3460 SFF i7-13700 8GB 1TB [추가구성 필요] (주)다인엔시스'</li><li>'HP DL20 GEN10 E-2224 / 32G / HDD 1T x2 RAID1 / 서버2019 / AS3년 상품권 주식회사 제로원씨앤씨'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.8841 |
## 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_el0")
# Run inference
preds = model("LG전자 24V50N-GR35K 정윤아")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 4 | 11.6691 | 21 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 36 |
| 2 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0455 | 1 | 0.4961 | - |
| 2.2727 | 50 | 0.005 | - |
| 4.5455 | 100 | 0.0001 | - |
| 6.8182 | 150 | 0.0001 | - |
| 9.0909 | 200 | 0.0 | - |
| 11.3636 | 250 | 0.0 | - |
| 13.6364 | 300 | 0.0 | - |
| 15.9091 | 350 | 0.0 | - |
| 18.1818 | 400 | 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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