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mini1013
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master_cate_el8

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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master_cate_el8
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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: HDTOP USB3.0 to HDMI 4K 영상 캡처보드15cm/HT-3C009/입력 4K 60Hz/녹화 1080P 60Hz/딜레이
    없는 실시간 녹화/알루미늄 하우징/금도금 커넥터  디피시스템
- text: 넥시 CAP02 USB HDMI 캡쳐보드 젠더타입  주식회사 디앤에스티
- text: 블랙매직 DeckLink 8K Pro 덱링크 8k pro  디지탈A/V세상
- text: 브리츠 BZ-SP600X 화이트 커브드 게이밍 사운드바  (주)에이치앤인터내셔널
- text: AVerMedia Live Gamer 4K 2.1 GC575  초이스컴퓨터 주식회사
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: 0.8028770510227017
      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                                                                                                                                                                                                               |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3     | <ul><li>'Britz 브리츠인터내셔널 BA-UMK120 다크실버 주식회사 꿈누리'</li><li>'Britz Accessories BA-R9 SoundBar 스피커 [화이트]  (주)조이젠'</li><li>'크리에이티브 PEBBLE V2  (주)아이티블루'</li></ul>                                                           |
| 2     | <ul><li>'GN-2000S 구즈넥 마이크 콘덴서 (회의, 강연, 설교, 스피치, 교회, 법원, 방송)  사운드스토리'</li><li>'컴스 MT195 회의실용 콘덴서 마이크  아이코다(주)'</li><li>'고독스 EM68 RGB 카디오이드 USB 콘덴서 마이크 스탠드 / 납품 세금계산서 가능  주식회사 모즈인터내셔날'</li></ul>                       |
| 8     | <ul><li>'레이저코리아 Razer Kiyo X 키요 X 웹캠 YT  주식회사 옐로우트리'</li><li>'앱코 APC930 QHD 웹캠 (블랙)  주식회사 동행하기'</li><li>'[병행,벌크]로지텍 C922 Pro Stream 웹캠  더블유에이취제이(WHJ)'</li></ul>                                                       |
| 5     | <ul><li>'포커스라이트 스칼렛2i2 3세대 FocusriScarlett 2i2 3rd Gen  와이지스토어(주) (YG store Co., Ltd)'</li><li>'Focusrite 포커스라이트 Scarlett 18i8 3세대 오디오 인터페이스  씨엠뮤직(CM music)'</li><li>'크리에이티브 Creative 사운드 블라스터 X5  (주)아토닉스'</li></ul> |
| 4     | <ul><li>'CORSAIR VOID RGB ELITE WIRELESS (화이트, 정품)  주식회사 꿈누리'</li><li>'TFG CH240 컬러풀 7.1Ch 게이밍헤드셋 (초경량 / 노이즈캔슬링 / 로스트아크) 블랙 (주)한성'</li><li>'로지텍 PRO X 2 LIGHTSPEED (핑크)  주식회사 조이쿨'</li></ul>                           |
| 7     | <ul><li>'HD60X  주식회사 글렌트리'</li><li>'블랙매직 Blackmagic Design ATEM Mini Pro 아템미니프로  어썸팩토리(awesome factory)'</li><li>'AVerMedia ER330 EzRecorder PVR(독립형 녹화장치)  (주)스트림텍'</li></ul>                                         |
| 0     | <ul><li>'이지넷유비쿼터스 NEXT-4516HDP 16채널 비디오 발룬 수신기  에이치엠에스'</li><li>'하이크비젼 DS-7604NI-K1/4P 4채널 IP POE NVR  CCTV테크'</li><li>'[HIKVISION 공식 수입원] 하이크비전 DS-7608NI-I2/8P UHD 4K IP카메라 네트워크 녹화기  (주)씨넥스존'</li></ul>             |
| 6     | <ul><li>'스카이디지탈 DT-800 HDTV 안테나  (주)컴퓨존'</li><li>'(스카이디지탈) DT-800 HDTV 안테나 /안테나  엠지솔루션'</li><li>'무료 스카이디지탈 SKY DT-800 HDTV 지상파 안테나  주식회사에프엘인텍'</li></ul>                                                               |
| 1     | <ul><li>'서진네트웍스 유니콘 AV-M9 UHD4K 안드로이드 셋탑박스 디빅스미디어플레이어 광고용디스플레이  (주)컴퓨존'</li><li>'유니콘 AV-M7 2세대 디빅스플레이어 UHD 4K지원 미디어플레이어  더원'</li><li>'서진네트웍스 UNICORN AV-M9 정품 멀티미디어 플레이어/영샵  영 샵'</li></ul>                            |
| 9     | <ul><li>'옴니트로닉 MSP-Q1 2채널 휴대용 마이크스피커 핸드+핸드마이크 에이스전자'</li><li>'[공식] 에버미디어 AS311 Speakerphon 휴대용 스피커폰 AI 소음감지 USB전원  주식회사 이선디지탈'</li><li>'브리츠 BE-MC100 야외설치 아웃도어 방수 스피커  (주)담다몰'</li></ul>                               |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8029 |

## 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_el8")
# Run inference
preds = model("넥시 CAP02 USB HDMI 캡쳐보드 젠더타입  주식회사 디앤에스티")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.3503 | 26  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 49                    |
| 1     | 25                    |
| 2     | 50                    |
| 3     | 50                    |
| 4     | 50                    |
| 5     | 50                    |
| 6     | 15                    |
| 7     | 50                    |
| 8     | 50                    |
| 9     | 5                     |

### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0161  | 1    | 0.496         | -               |
| 0.8065  | 50   | 0.2401        | -               |
| 1.6129  | 100  | 0.0385        | -               |
| 2.4194  | 150  | 0.025         | -               |
| 3.2258  | 200  | 0.0181        | -               |
| 4.0323  | 250  | 0.0004        | -               |
| 4.8387  | 300  | 0.0002        | -               |
| 5.6452  | 350  | 0.0001        | -               |
| 6.4516  | 400  | 0.0002        | -               |
| 7.2581  | 450  | 0.0001        | -               |
| 8.0645  | 500  | 0.0001        | -               |
| 8.8710  | 550  | 0.0001        | -               |
| 9.6774  | 600  | 0.0001        | -               |
| 10.4839 | 650  | 0.0001        | -               |
| 11.2903 | 700  | 0.0001        | -               |
| 12.0968 | 750  | 0.0           | -               |
| 12.9032 | 800  | 0.0           | -               |
| 13.7097 | 850  | 0.0           | -               |
| 14.5161 | 900  | 0.0           | -               |
| 15.3226 | 950  | 0.0           | -               |
| 16.1290 | 1000 | 0.0           | -               |
| 16.9355 | 1050 | 0.0           | -               |
| 17.7419 | 1100 | 0.0           | -               |
| 18.5484 | 1150 | 0.0           | -               |
| 19.3548 | 1200 | 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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