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master_cate_el1

Text Classification
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sentence-transformers
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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: 넥스트 USB 3.0 2포트 PCI Express 카드 (NEXT-212U3) YNMI-NK0431 윤 미디어
- text: 앱코 NCORE G30 트루포스 (블랙) 미들타워 컴퓨터 케이스 오케이 바이오
- text: APC SMC1500I-2U Smart UPS 900W/1500VA 무정전 전원공급장치 교체배터리 전원백업장치 (DHCNC) 주식회사
대현씨앤씨
- text: 이지넷 카드리더기 NEXT-8603TCU3 블랙 [KF] 주식회사 케이에프컴퍼니
- text: 다크플래쉬 darkFlash DS900 ARGB 강화유리 컴퓨터 PC 케이스 (블랙) 주식회사 아크런 (Akrun Co., Ltd.)
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: 0.9098343017000216
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:** 10 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 |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 9 | <ul><li>'APC BK500EI UPS배터리 무정전전원장치 300W 500VA 다피(dappy)'</li><li>'리안리 SP750 80PLUS GOLD (WHITE) 주식회사 브라보세컨즈'</li><li>'APC Smart UPS C 2000VA Tower 무정전전원장치 - smc2000ic 주식회사 파인인프라'</li></ul> |
| 2 | <ul><li>'3RSYS R200 RGB (블랙) 미들타워 컴온씨앤씨(주)'</li><li>'DAVEN AQUA (블랙) 주식회사 꿈누리'</li><li>'w 대원TMT DW-H1200 허브랙 (H1200×D800×W600/25U/회색) (착불배송) (주)원영씨앤씨'</li></ul> |
| 0 | <ul><li>'인텔 코어i7-13세대 13700K 랩터레이크 정품 에어캡배송 (주)신우밀루유떼'</li><li>'AMD 라이젠5-4세대 5600X (버미어)벌크포장 AS 3년 태성에프앤비(주)'</li><li>'[INTEL] 코어10세대 i7-10700 벌크 병행 쿨러미포함 (코멧레이크) (주)컴퓨존'</li></ul> |
| 4 | <ul><li>'SAPPHIRE 라데온 RX 7900 GRE PURE D6 16GB 주식회사 꿈누리'</li><li>'ASRock 라데온 RX 7900 XTX Phantom Gaming OC D6 24GB 대원씨티에스 주식회사 에스씨엠인포텍'</li><li>'[HY] INNO3D 지포스 GT1030 D5 2GB LP 무소음 (주)제이케이존'</li></ul> |
| 8 | <ul><li>'잘만 ZM-STC10 (2g) 주식회사 피씨사자'</li><li>'3RSYS APB BAR 35 (주)컴퓨존'</li><li>'LP30 ARGB PSU 커버 화이트 주식회사보성닷컴'</li></ul> |
| 6 | <ul><li>'NEXTU NEXT-206NEC EX 에스앤와이'</li><li>'LANstar PCI-E 내부 SATA3 4포트 카드/LS-PCIE-4SATA/PC 내부에 SATA3 4포트 생성/발열 방지용 방열판/LP 브라켓 포함 디피시스템'</li><li>'NEXTU NEXT-405NEC LP 에스앤와이'</li></ul> |
| 3 | <ul><li>'V-Color BLACK DDR5-5200 CL42 STANDARD 벌크 (8GB) (주)가이드컴'</li><li>'TEAMGROUP T-Force DDR5 6000 CL38 Delta RGB 화이트 패키지 32GB(16Gx2) (주)서린씨앤아이'</li><li>'ADATA DDR5-5600 CL46 (16GB)/정품판매점/하이닉스A다이/언락/평생 제한 보증/R 주식회사 에이알씨앤아이'</li></ul> |
| 5 | <ul><li>'ASRock H510M-HDV/M.2 SE 에즈윈 주식회사디케이'</li><li>'DK ASRock B760M PG Riptide D5 에즈윈 주식회사디케이'</li><li>'[ ] GIGABYTE B650 AORUS ELITE AX ICE 제이씨현 뉴비시스템즈'</li></ul> |
| 7 | <ul><li>'아틱 P14 PWM PST 블랙 VALUE 5팩 (주)서린씨앤아이'</li><li>'앱코 타이폰 120X5 CPU 쿨러 알루미늄 방열판 주식회사 지디스엠알오'</li><li>'Thermalright Peerless Assassin 120 SE 서린 태성에프앤비(주)'</li></ul> |
| 1 | <ul><li>'엠비에프 CAT.7 SFTP 금도금 UTP 3중 쉴드 패치코드 기가비트 랜케이블 0.5M (MBF-U705G) 주식회사 아크런 (Akrun Co., Ltd.)'</li><li>'MBF-C5E305R 305M 레드 BOX CAT.5E UTP 랜케이블 컴샷정보'</li><li>'엠비에프 CAT.5e UTP 제작형 랜케이블 박스 MBF-C5E305Y 옐로우 305m (주)아토닉스'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.9098 |
## 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_el1")
# Run inference
preds = model("앱코 NCORE G30 트루포스 (블랙) 미들타워 컴퓨터 케이스 오케이 바이오")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 4 | 9.206 | 18 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
| 4 | 50 |
| 5 | 50 |
| 6 | 50 |
| 7 | 50 |
| 8 | 50 |
| 9 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0127 | 1 | 0.4969 | - |
| 0.6329 | 50 | 0.2753 | - |
| 1.2658 | 100 | 0.0677 | - |
| 1.8987 | 150 | 0.014 | - |
| 2.5316 | 200 | 0.0023 | - |
| 3.1646 | 250 | 0.0001 | - |
| 3.7975 | 300 | 0.0001 | - |
| 4.4304 | 350 | 0.0001 | - |
| 5.0633 | 400 | 0.0001 | - |
| 5.6962 | 450 | 0.0 | - |
| 6.3291 | 500 | 0.0001 | - |
| 6.9620 | 550 | 0.0001 | - |
| 7.5949 | 600 | 0.0 | - |
| 8.2278 | 650 | 0.0 | - |
| 8.8608 | 700 | 0.0 | - |
| 9.4937 | 750 | 0.0 | - |
| 10.1266 | 800 | 0.0 | - |
| 10.7595 | 850 | 0.0 | - |
| 11.3924 | 900 | 0.0 | - |
| 12.0253 | 950 | 0.0 | - |
| 12.6582 | 1000 | 0.0 | - |
| 13.2911 | 1050 | 0.0 | - |
| 13.9241 | 1100 | 0.0 | - |
| 14.5570 | 1150 | 0.0 | - |
| 15.1899 | 1200 | 0.0 | - |
| 15.8228 | 1250 | 0.0 | - |
| 16.4557 | 1300 | 0.0 | - |
| 17.0886 | 1350 | 0.0 | - |
| 17.7215 | 1400 | 0.0 | - |
| 18.3544 | 1450 | 0.0 | - |
| 18.9873 | 1500 | 0.0 | - |
| 19.6203 | 1550 | 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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