File size: 9,855 Bytes
d41fd9e |
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 |
---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 모던탑21 모던 클래식 800 3단 장식장 가구/인테리어>거실가구>장식장
- text: 스코나 마넌트 아쿠아텍스 패브릭 1인 리클라이너 소파 가구/인테리어>거실가구>소파>리클라이너소파
- text: 가구느낌 베스트책상 1000x400 접이식 간이 테이블 가구/인테리어>거실가구>테이블>접이식테이블
- text: 자코모 러버블 컴팩트 4인 스위브 소파 + 스툴 가구/인테리어>거실가구>소파>패브릭소파
- text: 미드센추리테이블 유리좌탁 거실소파테이블 1000 가구/인테리어>거실가구>테이블>거실테이블
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain
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: accuracy
value: 1.0
name: Accuracy
---
# 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:** 4 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 |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0 | <ul><li>'에싸 라보엠Ⅲ 4인 오픈코너형 기능성 카시미라 패브릭 소파 가구/인테리어>거실가구>소파>패브릭소파'</li><li>'보루네오 플레타 3인용 천연소가죽 소파 가구/인테리어>거실가구>소파>가죽소파'</li><li>'동서가구 프라임 소나무원목 내추럴 황토 카우치 흙소파 DF638379 가구/인테리어>거실가구>소파>흙/돌소파'</li></ul> |
| 2.0 | <ul><li>'체스 유리 진열장 가구/인테리어>거실가구>장식장'</li><li>'디자인벤처스 로맨틱 1800 유리장 가구/인테리어>거실가구>장식장'</li><li>'퍼니처스마트 로랜드 유리 장식장 가구/인테리어>거실가구>장식장'</li></ul> |
| 0.0 | <ul><li>'나무뜰 켄트 서랍형 거실장 1200 티비다이 MRF013 가구/인테리어>거실가구>TV거실장'</li><li>'리바트 셀리나 1800 거실장 가구/인테리어>거실가구>TV거실장'</li><li>'슈퍼홈 리처 티비다이 낮은 거실장 2000 가구/인테리어>거실가구>TV거실장'</li></ul> |
| 3.0 | <ul><li>'테이블 거실 커피 탁자 북유럽 좌식 인테리어 티 모던 카페 라운드 가구/인테리어>거실가구>테이블>거실테이블'</li><li>'미드센추리 테라조 협탁 사이드 테이블 거실 소파 장식장 선반형 가구/인테리어>거실가구>테이블>사이드테이블'</li><li>'원목좌식테이블 방석 세트 원형 차 홈 카페 거실 가구/인테리어>거실가구>테이블>거실테이블'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 1.0 |
## 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_fi1")
# Run inference
preds = model("모던탑21 모던 클래식 800 3단 장식장 가구/인테리어>거실가구>장식장")
```
<!--
### 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 | 8.1714 | 18 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 70 |
### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0182 | 1 | 0.4862 | - |
| 0.9091 | 50 | 0.4961 | - |
| 1.8182 | 100 | 0.4367 | - |
| 2.7273 | 150 | 0.0317 | - |
| 3.6364 | 200 | 0.0 | - |
| 4.5455 | 250 | 0.0 | - |
| 5.4545 | 300 | 0.0 | - |
| 6.3636 | 350 | 0.0 | - |
| 7.2727 | 400 | 0.0 | - |
| 8.1818 | 450 | 0.0 | - |
| 9.0909 | 500 | 0.0 | - |
| 10.0 | 550 | 0.0 | - |
| 10.9091 | 600 | 0.0 | - |
| 11.8182 | 650 | 0.0 | - |
| 12.7273 | 700 | 0.0 | - |
| 13.6364 | 750 | 0.0 | - |
| 14.5455 | 800 | 0.0 | - |
| 15.4545 | 850 | 0.0 | - |
| 16.3636 | 900 | 0.0 | - |
| 17.2727 | 950 | 0.0 | - |
| 18.1818 | 1000 | 0.0 | - |
| 19.0909 | 1050 | 0.0 | - |
| 20.0 | 1100 | 0.0 | - |
| 20.9091 | 1150 | 0.0 | - |
| 21.8182 | 1200 | 0.0 | - |
| 22.7273 | 1250 | 0.0 | - |
| 23.6364 | 1300 | 0.0 | - |
| 24.5455 | 1350 | 0.0 | - |
| 25.4545 | 1400 | 0.0 | - |
| 26.3636 | 1450 | 0.0 | - |
| 27.2727 | 1500 | 0.0 | - |
| 28.1818 | 1550 | 0.0 | - |
| 29.0909 | 1600 | 0.0 | - |
| 30.0 | 1650 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- Datasets: 3.2.0
- Tokenizers: 0.19.1
## 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.*
--> |