setfit safetensors roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:mini1013/master_domain base_model:finetune:mini1013/master_domain model-index region:us
---
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")
```

<!--
### 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   | 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->