File size: 11,691 Bytes
e576dce |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 |
---
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.*
--> |