Hugging Face's logo

mini1013
/
master_cate_top_bt13_3

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
master_cate_top_bt13_3 / README.md
mini1013's picture
mini1013
Push model using huggingface_hub.
15f6138 verified
preview code
|
raw
history blame contribute delete
20.1 kB
---
base_model: klue/roberta-base
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '[시세이도프로페셔널/손상모발용] 서브리믹 아쿠아 인텐시브 샴푸 (리필) 450ml DepartmentSsg > 명품화장품 > 향수/바디/헤어
> 헤어케어 > 샴푸/트리트먼트 DepartmentSsg > 명품화장품 > 향수/바디/헤어 > 헤어케어 > 샴푸/트리트먼트'
- text: 제이숲 컬러제이 오로라 보색 샴푸 핑크 380ml C02 오로라 보색 샴푸 (핑크) (#M)화장품/미용>헤어케어>샴푸 AD > traverse
> Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 보색샴푸
- text: '정샘물 살롱집 단백질 리차징 샴푸 1,000ml [ : 트리트먼트] 리차징 샴푸 1000ml+트리트먼트 (#M)화장품/미용>헤어케어>샴푸
AD > Naverstore > 화장품/미용 > 시트러스 > 샴푸'
- text: '[유니크앤몰] 미쟝센 에이징 케어 파워베리 샴푸 1000ml 1000ml × 3개 (#M)쿠팡 홈>생활용품>헤어/바디/세안>샴푸/린스>샴푸>일반샴푸
Coupang > 뷰티 > 헤어 > 샴푸 > 일반샴푸'
- text: 히말라야 허브나드 두피 쿨링 샴푸 900ml x2개 (#M)11st>헤어케어>샴푸>기능성 11st > 뷰티 > 헤어케어 > 샴푸 >
기능성
inference: true
model-index:
- name: SetFit with klue/roberta-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8699763593380615
name: Accuracy
---
# SetFit with klue/roberta-base
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) 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:** [klue/roberta-base](https://huggingface.co/klue/roberta-base)
- **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 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3 | <ul><li>'라보에이치 탈모증상완화 두피강화 샴푸 페어앤프리지아 400ml × 2개 (#M)쿠팡 홈>생활용품>헤어/바디/세안>샴푸/린스>샴푸>기능성샴푸 Coupang > 뷰티 > 헤어 > 샴푸 > 기능성샴푸'</li><li>'[라보에이치] 탈모증상완화 샴푸 댄드러프클리닉 일반건성비듬 400ml (#M)11st>헤어케어>탈모/두피관리제>헤어토닉 11st > 뷰티 > 헤어케어 > 탈모/두피관리제 > 헤어토닉'</li><li>'탈모완화 기능성 헤모나후코이단샴푸 460ml 300ml 헤모나샴푸 460ml (#M)11st>헤어케어>샴푸>기능성 11st > 뷰티 > 헤어케어 > 샴푸 > 기능성'</li></ul> |
| 0 | <ul><li>'코랩 드라이 샴푸 200ml2개 50ml2개 물없이감는 사춘기 초등학생 청소년 올리브영 유니콘 (#M)홈>화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸'</li><li>'1+1 코랩 올리브영 드라이샴푸 프레쉬 200ml 오리지널_유니콘 (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 드라이샴푸'</li><li>'바티스트 드라이샴푸 11종 중 택1 04_스위티 200ml 11st>헤어케어>샴푸>일반;11st > 뷰티 > 헤어케어 > 샴푸 11st > 뷰티 > 헤어케어 > 샴푸 > 일반'</li></ul> |
| 2 | <ul><li>'엘라스틴 실크리페어 퍼펙트 샤이닝 샴푸 1200ml x2개 ssg > 뷰티 > 미용기기/소품 > 바디관리기기;ssg > 뷰티 > 헤어/바디 > 헤어케어 > 샴푸;ssg > 뷰티 > 헤어/바디 > 헤어케어 ssg > 뷰티 > 헤어/바디 > 세정/입욕용품 > 바디워시'</li><li>'오가니스트 티트리 비듬 세정 샴푸 500ml x3개 MinSellAmount (#M)바디/헤어>헤어케어>샴푸/린스 Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 샴푸/린스'</li><li>'오가니스트 체리블라썸 샴푸 500ml x 3개 LotteOn > 뷰티 > 헤어케어 > 샴푸 > 샴푸 LotteOn > 뷰티 > 헤어케어 > 샴푸 > 드라이샴푸'</li></ul> |
| 1 | <ul><li>'자연이랑 약산성 샴푸바2+린스바1 특별가/제로웨이스트 올인원 고체샴푸/고체린스 청대오일샴푸바2+린스바1_선택안함 (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 샴푸바'</li><li>'케세리 샴푸바 2개입 100g+100g 비건 퍼퓸 약산성 올인원 비누 [모발영양]딥 너리싱 샴푸바 200g (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 시트러스 > 샴푸'</li><li>'어성초자소엽녹차 샴푸바 로즈마리 자연 샴푸바 1개 (#M)화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 샴푸바'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8700 |
## 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_3")
# Run inference
preds = model("히말라야 허브나드 두피 쿨링 샴푸 900ml x2개 (#M)11st>헤어케어>샴푸>기능성 11st > 뷰티 > 헤어케어 > 샴푸 > 기능성")
```
<!--
### 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 | 24.02 | 122 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 50 |
| 2 | 50 |
| 3 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0032 | 1 | 0.4979 | - |
| 0.1597 | 50 | 0.4236 | - |
| 0.3195 | 100 | 0.3685 | - |
| 0.4792 | 150 | 0.2863 | - |
| 0.6390 | 200 | 0.1407 | - |
| 0.7987 | 250 | 0.0265 | - |
| 0.9585 | 300 | 0.0059 | - |
| 1.1182 | 350 | 0.0046 | - |
| 1.2780 | 400 | 0.0003 | - |
| 1.4377 | 450 | 0.0001 | - |
| 1.5974 | 500 | 0.0002 | - |
| 1.7572 | 550 | 0.0001 | - |
| 1.9169 | 600 | 0.0001 | - |
| 2.0767 | 650 | 0.0001 | - |
| 2.2364 | 700 | 0.0001 | - |
| 2.3962 | 750 | 0.0 | - |
| 2.5559 | 800 | 0.0 | - |
| 2.7157 | 850 | 0.0 | - |
| 2.8754 | 900 | 0.0 | - |
| 3.0351 | 950 | 0.0 | - |
| 3.1949 | 1000 | 0.0 | - |
| 3.3546 | 1050 | 0.0 | - |
| 3.5144 | 1100 | 0.0 | - |
| 3.6741 | 1150 | 0.0 | - |
| 3.8339 | 1200 | 0.0 | - |
| 3.9936 | 1250 | 0.0 | - |
| 4.1534 | 1300 | 0.0 | - |
| 4.3131 | 1350 | 0.0 | - |
| 4.4728 | 1400 | 0.0 | - |
| 4.6326 | 1450 | 0.0 | - |
| 4.7923 | 1500 | 0.0 | - |
| 4.9521 | 1550 | 0.0 | - |
| 5.1118 | 1600 | 0.0 | - |
| 5.2716 | 1650 | 0.0 | - |
| 5.4313 | 1700 | 0.0 | - |
| 5.5911 | 1750 | 0.0 | - |
| 5.7508 | 1800 | 0.0 | - |
| 5.9105 | 1850 | 0.0 | - |
| 6.0703 | 1900 | 0.0 | - |
| 6.2300 | 1950 | 0.0 | - |
| 6.3898 | 2000 | 0.0 | - |
| 6.5495 | 2050 | 0.0 | - |
| 6.7093 | 2100 | 0.0 | - |
| 6.8690 | 2150 | 0.0 | - |
| 7.0288 | 2200 | 0.0007 | - |
| 7.1885 | 2250 | 0.0027 | - |
| 7.3482 | 2300 | 0.0008 | - |
| 7.5080 | 2350 | 0.0 | - |
| 7.6677 | 2400 | 0.0 | - |
| 7.8275 | 2450 | 0.0 | - |
| 7.9872 | 2500 | 0.0 | - |
| 8.1470 | 2550 | 0.0 | - |
| 8.3067 | 2600 | 0.0 | - |
| 8.4665 | 2650 | 0.0 | - |
| 8.6262 | 2700 | 0.0 | - |
| 8.7859 | 2750 | 0.0 | - |
| 8.9457 | 2800 | 0.0 | - |
| 9.1054 | 2850 | 0.0 | - |
| 9.2652 | 2900 | 0.0 | - |
| 9.4249 | 2950 | 0.0 | - |
| 9.5847 | 3000 | 0.0 | - |
| 9.7444 | 3050 | 0.0 | - |
| 9.9042 | 3100 | 0.0 | - |
| 10.0639 | 3150 | 0.0 | - |
| 10.2236 | 3200 | 0.0 | - |
| 10.3834 | 3250 | 0.0 | - |
| 10.5431 | 3300 | 0.0 | - |
| 10.7029 | 3350 | 0.0 | - |
| 10.8626 | 3400 | 0.0 | - |
| 11.0224 | 3450 | 0.0 | - |
| 11.1821 | 3500 | 0.0 | - |
| 11.3419 | 3550 | 0.0 | - |
| 11.5016 | 3600 | 0.0 | - |
| 11.6613 | 3650 | 0.0 | - |
| 11.8211 | 3700 | 0.0 | - |
| 11.9808 | 3750 | 0.0 | - |
| 12.1406 | 3800 | 0.0 | - |
| 12.3003 | 3850 | 0.0 | - |
| 12.4601 | 3900 | 0.0 | - |
| 12.6198 | 3950 | 0.0 | - |
| 12.7796 | 4000 | 0.0 | - |
| 12.9393 | 4050 | 0.0 | - |
| 13.0990 | 4100 | 0.0 | - |
| 13.2588 | 4150 | 0.0 | - |
| 13.4185 | 4200 | 0.0 | - |
| 13.5783 | 4250 | 0.0 | - |
| 13.7380 | 4300 | 0.0 | - |
| 13.8978 | 4350 | 0.0 | - |
| 14.0575 | 4400 | 0.0 | - |
| 14.2173 | 4450 | 0.0 | - |
| 14.3770 | 4500 | 0.0 | - |
| 14.5367 | 4550 | 0.0 | - |
| 14.6965 | 4600 | 0.0 | - |
| 14.8562 | 4650 | 0.0 | - |
| 15.0160 | 4700 | 0.0 | - |
| 15.1757 | 4750 | 0.0 | - |
| 15.3355 | 4800 | 0.0 | - |
| 15.4952 | 4850 | 0.0 | - |
| 15.6550 | 4900 | 0.0 | - |
| 15.8147 | 4950 | 0.0 | - |
| 15.9744 | 5000 | 0.0 | - |
| 16.1342 | 5050 | 0.0 | - |
| 16.2939 | 5100 | 0.0 | - |
| 16.4537 | 5150 | 0.0 | - |
| 16.6134 | 5200 | 0.0 | - |
| 16.7732 | 5250 | 0.0 | - |
| 16.9329 | 5300 | 0.0 | - |
| 17.0927 | 5350 | 0.0 | - |
| 17.2524 | 5400 | 0.0 | - |
| 17.4121 | 5450 | 0.0 | - |
| 17.5719 | 5500 | 0.0 | - |
| 17.7316 | 5550 | 0.0 | - |
| 17.8914 | 5600 | 0.0 | - |
| 18.0511 | 5650 | 0.0 | - |
| 18.2109 | 5700 | 0.0 | - |
| 18.3706 | 5750 | 0.0 | - |
| 18.5304 | 5800 | 0.0 | - |
| 18.6901 | 5850 | 0.0 | - |
| 18.8498 | 5900 | 0.0 | - |
| 19.0096 | 5950 | 0.0 | - |
| 19.1693 | 6000 | 0.0 | - |
| 19.3291 | 6050 | 0.0 | - |
| 19.4888 | 6100 | 0.0 | - |
| 19.6486 | 6150 | 0.0 | - |
| 19.8083 | 6200 | 0.0 | - |
| 19.9681 | 6250 | 0.0 | - |
| 20.1278 | 6300 | 0.0 | - |
| 20.2875 | 6350 | 0.0 | - |
| 20.4473 | 6400 | 0.0 | - |
| 20.6070 | 6450 | 0.0 | - |
| 20.7668 | 6500 | 0.0 | - |
| 20.9265 | 6550 | 0.0 | - |
| 21.0863 | 6600 | 0.0 | - |
| 21.2460 | 6650 | 0.0 | - |
| 21.4058 | 6700 | 0.0 | - |
| 21.5655 | 6750 | 0.0 | - |
| 21.7252 | 6800 | 0.0 | - |
| 21.8850 | 6850 | 0.0 | - |
| 22.0447 | 6900 | 0.0 | - |
| 22.2045 | 6950 | 0.0 | - |
| 22.3642 | 7000 | 0.0 | - |
| 22.5240 | 7050 | 0.0 | - |
| 22.6837 | 7100 | 0.0 | - |
| 22.8435 | 7150 | 0.0 | - |
| 23.0032 | 7200 | 0.0 | - |
| 23.1629 | 7250 | 0.0 | - |
| 23.3227 | 7300 | 0.0 | - |
| 23.4824 | 7350 | 0.0 | - |
| 23.6422 | 7400 | 0.0 | - |
| 23.8019 | 7450 | 0.0 | - |
| 23.9617 | 7500 | 0.0 | - |
| 24.1214 | 7550 | 0.0 | - |
| 24.2812 | 7600 | 0.0 | - |
| 24.4409 | 7650 | 0.0 | - |
| 24.6006 | 7700 | 0.0 | - |
| 24.7604 | 7750 | 0.0 | - |
| 24.9201 | 7800 | 0.0 | - |
| 25.0799 | 7850 | 0.0 | - |
| 25.2396 | 7900 | 0.0 | - |
| 25.3994 | 7950 | 0.0 | - |
| 25.5591 | 8000 | 0.0 | - |
| 25.7188 | 8050 | 0.0 | - |
| 25.8786 | 8100 | 0.0 | - |
| 26.0383 | 8150 | 0.0 | - |
| 26.1981 | 8200 | 0.0 | - |
| 26.3578 | 8250 | 0.0 | - |
| 26.5176 | 8300 | 0.0 | - |
| 26.6773 | 8350 | 0.0 | - |
| 26.8371 | 8400 | 0.0 | - |
| 26.9968 | 8450 | 0.0 | - |
| 27.1565 | 8500 | 0.0 | - |
| 27.3163 | 8550 | 0.0 | - |
| 27.4760 | 8600 | 0.0 | - |
| 27.6358 | 8650 | 0.0 | - |
| 27.7955 | 8700 | 0.0 | - |
| 27.9553 | 8750 | 0.0 | - |
| 28.1150 | 8800 | 0.0 | - |
| 28.2748 | 8850 | 0.0 | - |
| 28.4345 | 8900 | 0.0 | - |
| 28.5942 | 8950 | 0.0 | - |
| 28.7540 | 9000 | 0.0 | - |
| 28.9137 | 9050 | 0.0 | - |
| 29.0735 | 9100 | 0.0 | - |
| 29.2332 | 9150 | 0.0 | - |
| 29.3930 | 9200 | 0.0 | - |
| 29.5527 | 9250 | 0.0 | - |
| 29.7125 | 9300 | 0.0 | - |
| 29.8722 | 9350 | 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.*
-->