File size: 13,921 Bytes
8e47fbc 1bd1bdd 008115d 1bd1bdd 8e47fbc 1bd1bdd 8e47fbc 1bd1bdd 8e47fbc 1bd1bdd 8e47fbc 1bd1bdd 8e47fbc 1bd1bdd 8e47fbc 1bd1bdd 8e47fbc 1bd1bdd 8e47fbc 1bd1bdd 8e47fbc 1bd1bdd 8e47fbc 1bd1bdd 8e47fbc 1bd1bdd 8e47fbc 1bd1bdd 8e47fbc 008115d 8e47fbc 008115d 8e47fbc 008115d 8e47fbc 1bd1bdd 8e47fbc |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 |
---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 헤어샵 전용 바이오메드 엘피피 트리트먼트 LPP 실크 트리트먼트1000ml 사은품 증정 (#M)쿠팡 홈>뷰티>헤어>트리트먼트/팩>일반
트리트먼트 Coupang > 뷰티 > 헤어 > 트리트먼트/팩 > 일반 트리트먼트
- text: 미쟝센 퍼펙트 세럼 트리트먼트 330ml × 1개 (#M)쿠팡 홈>뷰티>헤어>트리트먼트/팩/앰플>일반 트리트먼트 Coupang > 뷰티
> 헤어 > 트리트먼트/팩/앰플 > 일반 트리트먼트
- text: 한소희Pick 로레알파리 토탈리페어5 트리트먼트 헤어팩 400ml 50ml 헤어팩280ml LotteOn > 뷰티 > 헤어/바디 >
헤어케어 > 트리트먼트/헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩
- text: 밀크바오밥 오리지널 샴푸 화이트솝 1L(옵션선택1) 11 트리트먼트 화이트솝 1000ml (#M)헤어케어>샴푸>샴푸바 AD > traverse
> 11st > 뷰티 > 헤어케어 > 샴푸 > 샴푸바
- text: 로레알 토탈리페어5 헤어팩 280ml + 170ml (#M)쿠팡 홈>생활용품>헤어/바디/세안>트리트먼트/팩/앰플>헤어팩/헤어마스크
Coupang > 뷰티 > 헤어 > 트리트먼트/팩/앰플 > 헤어팩/헤어마스크
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: accuracy
value: 0.8786919831223629
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:** 2 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 | <ul><li>'[웰라] 염색모전용 SP 컬러 세이브 마스크 400ml (#M)화장품/미용>헤어케어>헤어팩 LO > window_fashion_town > Naverstore > FashionTown > 뷰티 > CATEGORY > 헤어케어 > 트리트먼트/팩 > 헤어팩'</li><li>'아모스 01 퓨어스마트 샴푸 팩 비듬케어 사춘기샴푸 퓨어 스마트 팩 300ml-비듬두피팩 (#M)홈>화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸'</li><li>'미쟝센 데미지 케어 로즈프로틴 헤어팩 150ml × 1개 (#M)쿠팡 홈>생활용품>헤어/바디/세안>트리트먼트/팩/앰플>헤어팩/헤어마스크 Coupang > 뷰티 > 헤어 > 트리트먼트/팩/앰플 > 헤어팩/헤어마스크'</li></ul> |
| 0 | <ul><li>'스무드 인퓨전 너리싱 스타일링 크림 250ml LotteOn > 뷰티 > 명품화장품 > 헤어케어 LotteOn > 뷰티 > 헤어케어 > 헤어에센스'</li><li>'체리블라썸/아르간오일 트리트먼트 280ml x2개 02)모로코아르간 트리트먼트 2개 LotteOn > 뷰티 > 헤어케어 > 트리트먼트 LotteOn > 뷰티 > 헤어케어 > 트리트먼트'</li><li>'[LG생활건강] 비욘드 프로페셔널 디펜스 트리트먼트 500ml LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 린스 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 린스'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8787 |
## 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_top_bt13_9_test_flat")
# Run inference
preds = model("미쟝센 퍼펙트 세럼 트리트먼트 330ml × 1개 (#M)쿠팡 홈>뷰티>헤어>트리트먼트/팩/앰플>일반 트리트먼트 Coupang > 뷰티 > 헤어 > 트리트먼트/팩/앰플 > 일반 트리트먼트")
```
<!--
### 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 | 11 | 21.07 | 49 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 50 |
### Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 100
- 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0064 | 1 | 0.4262 | - |
| 0.3185 | 50 | 0.4176 | - |
| 0.6369 | 100 | 0.314 | - |
| 0.9554 | 150 | 0.0953 | - |
| 1.2739 | 200 | 0.0302 | - |
| 1.5924 | 250 | 0.0123 | - |
| 1.9108 | 300 | 0.0005 | - |
| 2.2293 | 350 | 0.0002 | - |
| 2.5478 | 400 | 0.0001 | - |
| 2.8662 | 450 | 0.0001 | - |
| 3.1847 | 500 | 0.0001 | - |
| 3.5032 | 550 | 0.0 | - |
| 3.8217 | 600 | 0.0001 | - |
| 4.1401 | 650 | 0.0 | - |
| 4.4586 | 700 | 0.0 | - |
| 4.7771 | 750 | 0.0 | - |
| 5.0955 | 800 | 0.0001 | - |
| 5.4140 | 850 | 0.0001 | - |
| 5.7325 | 900 | 0.0 | - |
| 6.0510 | 950 | 0.0 | - |
| 6.3694 | 1000 | 0.0 | - |
| 6.6879 | 1050 | 0.0 | - |
| 7.0064 | 1100 | 0.0 | - |
| 7.3248 | 1150 | 0.0 | - |
| 7.6433 | 1200 | 0.0 | - |
| 7.9618 | 1250 | 0.0 | - |
| 8.2803 | 1300 | 0.0 | - |
| 8.5987 | 1350 | 0.0 | - |
| 8.9172 | 1400 | 0.0 | - |
| 9.2357 | 1450 | 0.0 | - |
| 9.5541 | 1500 | 0.0 | - |
| 9.8726 | 1550 | 0.0 | - |
| 10.1911 | 1600 | 0.0 | - |
| 10.5096 | 1650 | 0.0 | - |
| 10.8280 | 1700 | 0.0 | - |
| 11.1465 | 1750 | 0.0 | - |
| 11.4650 | 1800 | 0.0 | - |
| 11.7834 | 1850 | 0.0 | - |
| 12.1019 | 1900 | 0.0 | - |
| 12.4204 | 1950 | 0.0 | - |
| 12.7389 | 2000 | 0.0 | - |
| 13.0573 | 2050 | 0.0 | - |
| 13.3758 | 2100 | 0.0 | - |
| 13.6943 | 2150 | 0.0 | - |
| 14.0127 | 2200 | 0.0 | - |
| 14.3312 | 2250 | 0.0 | - |
| 14.6497 | 2300 | 0.0 | - |
| 14.9682 | 2350 | 0.0 | - |
| 15.2866 | 2400 | 0.0 | - |
| 15.6051 | 2450 | 0.0 | - |
| 15.9236 | 2500 | 0.0 | - |
| 16.2420 | 2550 | 0.0 | - |
| 16.5605 | 2600 | 0.0 | - |
| 16.8790 | 2650 | 0.0 | - |
| 17.1975 | 2700 | 0.0001 | - |
| 17.5159 | 2750 | 0.0001 | - |
| 17.8344 | 2800 | 0.0003 | - |
| 18.1529 | 2850 | 0.0 | - |
| 18.4713 | 2900 | 0.0 | - |
| 18.7898 | 2950 | 0.0 | - |
| 19.1083 | 3000 | 0.0 | - |
| 19.4268 | 3050 | 0.0 | - |
| 19.7452 | 3100 | 0.0001 | - |
| 20.0637 | 3150 | 0.0002 | - |
| 20.3822 | 3200 | 0.0 | - |
| 20.7006 | 3250 | 0.0 | - |
| 21.0191 | 3300 | 0.0 | - |
| 21.3376 | 3350 | 0.0 | - |
| 21.6561 | 3400 | 0.0 | - |
| 21.9745 | 3450 | 0.0 | - |
| 22.2930 | 3500 | 0.0 | - |
| 22.6115 | 3550 | 0.0 | - |
| 22.9299 | 3600 | 0.0 | - |
| 23.2484 | 3650 | 0.0 | - |
| 23.5669 | 3700 | 0.0 | - |
| 23.8854 | 3750 | 0.0 | - |
| 24.2038 | 3800 | 0.0 | - |
| 24.5223 | 3850 | 0.0 | - |
| 24.8408 | 3900 | 0.0 | - |
| 25.1592 | 3950 | 0.0 | - |
| 25.4777 | 4000 | 0.0 | - |
| 25.7962 | 4050 | 0.0 | - |
| 26.1146 | 4100 | 0.0 | - |
| 26.4331 | 4150 | 0.0 | - |
| 26.7516 | 4200 | 0.0 | - |
| 27.0701 | 4250 | 0.0 | - |
| 27.3885 | 4300 | 0.0 | - |
| 27.7070 | 4350 | 0.0 | - |
| 28.0255 | 4400 | 0.0 | - |
| 28.3439 | 4450 | 0.0 | - |
| 28.6624 | 4500 | 0.0 | - |
| 28.9809 | 4550 | 0.0 | - |
| 29.2994 | 4600 | 0.0 | - |
| 29.6178 | 4650 | 0.0 | - |
| 29.9363 | 4700 | 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.*
--> |