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master_cate_sl11

Text Classification
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master_cate_sl11 / README.md
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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 스타스포츠 비치 발리볼 소프트 4호 CB814 스포츠/레저>배구>배구공
- text: 안전요원의자 1 9m 수영장 풀장 심판대 안전바 의자 구조 요원 스포츠/레저>배구>기타배구용품
- text: 미즈노 웨이브 라이트닝 Z7 배구화 V1GA220041 스포츠/레저>배구>배구화
- text: 배구 지주대 이동식 맨홀형 체육 강당 맨홀식 거치대 스포츠/레저>배구>기타배구용품
- text: 미즈노 남성 여성 배구복 배구 유니폼 긴팔티 긴팔 티셔츠 N-XT V2MAA510 스포츠/레저>배구>배구의류
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:** 5 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4.0 | <ul><li>'아식스 배구화 미들컷 브이 스위프트 V-SWIFT FF MT21053A018 100 스포츠/레저>배구>배구화'</li><li>'아식스 스카이 엘리트 FF MT 인도어화 21051A065103 스포츠/레저>배구>배구화'</li><li>'미즈노 아식스 배구화 젤 로켓10 남여공용 1073A047-100 스포츠/레저>배구>배구화'</li></ul> |
| 0.0 | <ul><li>'의자 농구 코트 야외 휴식 좌석 학교 놀이터 관람석 의자 경기장 15177N30166 스포츠/레저>배구>기타배구용품'</li><li>'스포츠 심판대 심판 감시대 족구 의자 심판석 안전 분리형 시합 스포츠/레저>배구>기타배구용품'</li><li>'닛타쿠 홀츠 시벤 탁구라켓 ST 19812 NE-6112 스포츠/레저>배구>기타배구용품'</li></ul> |
| 3.0 | <ul><li>'밀리언웨이브 24 단체 배구 전사 유니폼 - 옵티머스 스포츠/레저>배구>배구의류'</li><li>'스타스포츠 스타 전사 유니폼 상하의세트 Model S124 족구 배구 축구 등 활용가능 원하는로고 팀명 이름 번호 추가 스포츠/레저>배구>배구의류'</li><li>'뱃저바스켓 Alleson Athletic - 반소매 배구 MA 108603 829VSJW 스포츠/레저>배구>배구의류'</li></ul> |
| 1.0 | <ul><li>'미카사 배구공 4호 초등학생용 V400W 스포츠/레저>배구>배구공'</li><li>'스타스포츠 폼 발리볼 피구볼 CB834 스포츠/레저>배구>배구공'</li><li>'스타스포츠 S 배구공 그랜드챔피언 2 4호 5호 생활체육 시합용 VB224-34S VB225-34S 스포츠/레저>배구>배구공'</li></ul> |
| 2.0 | <ul><li>'스타스포츠 배구네트 6인제 VN320H 스포츠/레저>배구>배구네트'</li><li>'스타 스타스포츠 배구 네트 9인제 (경기용) VN371H 스포츠/레저>배구>배구네트'</li><li>'스타 스타스포츠 배구 네트 9인제 (경기용) VN380 스포츠/레저>배구>배구네트'</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_sl11")
# Run inference
preds = model("스타스포츠 비치 발리볼 소프트 4호 CB814 스포츠/레저>배구>배구공")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 4 | 8.8833 | 18 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 20 |
| 3.0 | 70 |
| 4.0 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0169 | 1 | 0.461 | - |
| 0.8475 | 50 | 0.478 | - |
| 1.6949 | 100 | 0.1412 | - |
| 2.5424 | 150 | 0.0009 | - |
| 3.3898 | 200 | 0.0 | - |
| 4.2373 | 250 | 0.0 | - |
| 5.0847 | 300 | 0.0 | - |
| 5.9322 | 350 | 0.0001 | - |
| 6.7797 | 400 | 0.0 | - |
| 7.6271 | 450 | 0.0 | - |
| 8.4746 | 500 | 0.0 | - |
| 9.3220 | 550 | 0.0 | - |
| 10.1695 | 600 | 0.0 | - |
| 11.0169 | 650 | 0.0 | - |
| 11.8644 | 700 | 0.0 | - |
| 12.7119 | 750 | 0.0 | - |
| 13.5593 | 800 | 0.0 | - |
| 14.4068 | 850 | 0.0 | - |
| 15.2542 | 900 | 0.0 | - |
| 16.1017 | 950 | 0.0 | - |
| 16.9492 | 1000 | 0.0 | - |
| 17.7966 | 1050 | 0.0 | - |
| 18.6441 | 1100 | 0.0 | - |
| 19.4915 | 1150 | 0.0 | - |
| 20.3390 | 1200 | 0.0 | - |
| 21.1864 | 1250 | 0.0 | - |
| 22.0339 | 1300 | 0.0 | - |
| 22.8814 | 1350 | 0.0 | - |
| 23.7288 | 1400 | 0.0 | - |
| 24.5763 | 1450 | 0.0 | - |
| 25.4237 | 1500 | 0.0 | - |
| 26.2712 | 1550 | 0.0 | - |
| 27.1186 | 1600 | 0.0 | - |
| 27.9661 | 1650 | 0.0 | - |
| 28.8136 | 1700 | 0.0 | - |
| 29.6610 | 1750 | 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}
}
```
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