File size: 9,407 Bytes
b8f8716 |
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 |
---
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: '[바다원] 깨끗한 돌김자반볶음 오리지널 40g x 5봉 (주)씨제이이엔엠'
- text: 쭈꾸미사령부 매운맛 300g 3개 불타는 매운맛 원츄쟈챠
- text: 냉동 새우 튀김 300g 6미 10미 대용량 업소용 빵가루 왕새우튀김 코코넛쉬림프 360g (30미) 주식회사 더꽃게
- text: 잇투헤븐 팔당 불 오징어 매운 오징어 볶음 400g 쭈꾸미도사 쭈꾸미볶음 01.팔당불오징어400g 1팩 (주)잇투헤븐
- text: CJ 명가김 파래김 4g 16입 트릴리어네어스
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: 0.8689361702127659
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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2.0 | <ul><li>'훈제연어(통) 약1.1kg 냉동연어 필렛 슬라이스 칠레산 HACCP 국내가공 화이트베어 화이트베어 훈제연어슬라이스 ±1.3kg 주식회사 셀피'</li><li>'안동간고등어 80g 10팩(5마리) 동의합니다_80g 10팩(5마리) 델리아마켓'</li><li>'제주 국내산 손질 고등어 2KG 한팩150g이상 11-12팩 3KG(16-19팩) 효명가'</li></ul> |
| 1.0 | <ul><li>'동원F&B 양반 김치맛 김부각 50g 1개 동원F&B 양반 김치맛 김부각 50g 1개 다팔아스토어'</li><li>'오뚜기 옛날 자른미역 50G 대성상사'</li><li>'환길산업 섬마을 해초샐러드 냉동 해초무침 2kg 제루통상'</li></ul> |
| 0.0 | <ul><li>'Fish Tree 국물용멸치 1.3kg 케이원'</li><li>'Fish Tree 국물용 볶음용 멸치 1.3kg 1kg 뼈건강 깊은맛 육수 대멸치 좋은식감 국물용 멸치 1.3kg 유라너스'</li><li>'Fish Tree 국물용 멸치 1.3kg 이숍'</li></ul> |
| 3.0 | <ul><li>'랭킹수산 장어구이 혼합 140gx20팩(데리야끼10매콤10) -인증 제이원무역'</li><li>'올반 대왕 오징어튀김 400g 나라유통'</li><li>'바다愛한끼 이원일 연평도 꽃게 해물탕 760g 소스포함 2팩 (주)티알엔'</li></ul> |
| 5.0 | <ul><li>'날치알 동림 담홍 레드 800G [800G][동림]날치알(골드)(팩) 주식회사 명품씨푸드'</li><li>'날치알 동림 담홍 레드 800G [800G][동림]날치알(레드)(팩) 주식회사 명품씨푸드'</li><li>'날치알 동림 담홍 레드 800G [800gG[코아]날치알[골드] 주식회사 명품씨푸드'</li></ul> |
| 4.0 | <ul><li>'명인오가네 연어장 250g 명인오가네몰'</li><li>'[나브연] 수제 간장 연어장 750g 덜짜게 주희종'</li><li>'[나브연] 수제 간장 연어장 500g 보통 주희종'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.8689 |
## 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_fd11")
# Run inference
preds = model("CJ 명가김 파래김 4g 16입 트릴리어네어스")
```
<!--
### 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 | 9.1164 | 23 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 50 |
| 1.0 | 50 |
| 2.0 | 50 |
| 3.0 | 50 |
| 4.0 | 50 |
| 5.0 | 25 |
### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0233 | 1 | 0.4609 | - |
| 1.1628 | 50 | 0.2116 | - |
| 2.3256 | 100 | 0.0876 | - |
| 3.4884 | 150 | 0.0442 | - |
| 4.6512 | 200 | 0.0254 | - |
| 5.8140 | 250 | 0.0133 | - |
| 6.9767 | 300 | 0.0252 | - |
| 8.1395 | 350 | 0.0176 | - |
| 9.3023 | 400 | 0.0116 | - |
| 10.4651 | 450 | 0.004 | - |
| 11.6279 | 500 | 0.0231 | - |
| 12.7907 | 550 | 0.0023 | - |
| 13.9535 | 600 | 0.0017 | - |
| 15.1163 | 650 | 0.0002 | - |
| 16.2791 | 700 | 0.0001 | - |
| 17.4419 | 750 | 0.0001 | - |
| 18.6047 | 800 | 0.0001 | - |
| 19.7674 | 850 | 0.0001 | - |
### 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.*
--> |