File size: 9,815 Bytes
2230f13 |
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 |
---
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: 길이조절 안경고정 밴드 코받침 패드 운동 캠핑 등산 진브라운 알리몽드
- text: 레이밴 안경테 RB3691VF 2509 남자 여자 동그란안경 아시안핏 시온아이엔티
- text: 밀착 스포츠안경줄 흔들림방지 안경스트랩 비앤비
- text: '[텐바이텐]바체타팩토리 가죽 안경 케이스 08 오렌지_추가 안 함_추가 안 함 신세계몰'
- text: TUMI 투미 카본 티타늄 명품 안경테 메탈 스퀘어 남자 여자 공용 안경 04.TU10-0003-01 LFmall02
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: 0.9104360692836626
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:** 6 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 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 5.0 | <ul><li>'초경량 국산 안경테 베타 울템 카본 티타늄 뿔테안경 551-599_S571-2 브라운투톤 ENA아이웨어'</li><li>'B019 ORIGINAL GLASS CRYSTAL GREEN '</li><li>'니시데카즈오 BROWLINE2 하금테 근적외선 차단렌즈 아이라이크(EYE LIKE)'</li></ul> |
| 1.0 | <ul><li>'레더렛소가죽선글라스파우치휴대용안경케이스 이정민'</li><li>'위에 안경 쓰는 파우치 편광 끼우는 선글라스 3종 세트 선그라스 클립 에끼우는 플립 온 클립선글라스3종세트_일반블랙 홉포엘'</li><li>'휴대용 가죽 선글라스 안경 파우치 케이스 보관함 안 PU안경케이스_그레이 라이프패션'</li></ul> |
| 3.0 | <ul><li>'아이업꽈배기인조가죽안경줄10p세트선글라스줄 유어드림커머스'</li><li>'스트랩 캐주얼디자인줄 스토퍼줄 안경걸이 끈 B 더펭귄샵'</li><li>'천연 크리스탈 안경 선글라스 걸이 줄 원석 비즈 빈티지 에스닉 마스크 스트랩 겸용 블루 3mm 70-75CM nouville'</li></ul> |
| 0.0 | <ul><li>'갤러리아 NIRNIR SUNGLASS 5 COLOR GREEN 갤러리아몰'</li><li>'여자 켓아이 뿔테 선그라스 썬그라스 남자 RORGGE 2111 상품선택_2유광블랙 온달이'</li><li>'뮤즈 서클 뿔테선글라스 코코아 푸치백'</li></ul> |
| 2.0 | <ul><li>'로에드 안경 자국 코패드 코받침 눌림 선글라스 코 통증 방지 패드 교체 스티커 안경코패드 1.8mm（화이트）_2.8mm(화이트) 로에드'</li><li>'[힐포]국산 고급 초극세사 렌즈 안경닦이 김서림방지 클리너 크리너 악기수건 안경천 융s 05. knit 안경닦이30매 15x18cm_블루 모아텍스'</li><li>'자우버 렌즈 케어 클리닝 티슈 200매 메디위'</li></ul> |
| 4.0 | <ul><li>'산리오 안경정리함 안경케이스 세트 6종 안경케이스시나모롤 지에이치글로벌'</li><li>'(이거찜) 프리미엄 가죽 안경집 안경케이스 가죽안경집 스카이 제이케이'</li><li>'스트랩 안경케이스 휴대용 안경파우치 가죽안경보관집 선글라스보관케이스 No.01 스트랩 안경케이스 블랙 여선영'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.9104 |
## 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_ac4")
# Run inference
preds = model("밀착 스포츠안경줄 흔들림방지 안경스트랩 비앤비")
```
<!--
### 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 | 3 | 9.53 | 20 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 50 |
| 1.0 | 50 |
| 2.0 | 50 |
| 3.0 | 50 |
| 4.0 | 50 |
| 5.0 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0213 | 1 | 0.4524 | - |
| 1.0638 | 50 | 0.2583 | - |
| 2.1277 | 100 | 0.0642 | - |
| 3.1915 | 150 | 0.0781 | - |
| 4.2553 | 200 | 0.0806 | - |
| 5.3191 | 250 | 0.0391 | - |
| 6.3830 | 300 | 0.0011 | - |
| 7.4468 | 350 | 0.0003 | - |
| 8.5106 | 400 | 0.0001 | - |
| 9.5745 | 450 | 0.0001 | - |
| 10.6383 | 500 | 0.0 | - |
| 11.7021 | 550 | 0.0 | - |
| 12.7660 | 600 | 0.0 | - |
| 13.8298 | 650 | 0.0 | - |
| 14.8936 | 700 | 0.0 | - |
| 15.9574 | 750 | 0.0 | - |
| 17.0213 | 800 | 0.0 | - |
| 18.0851 | 850 | 0.0 | - |
| 19.1489 | 900 | 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.*
--> |