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master_cate_sl5

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master_cate_sl5 / README.md
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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 스프링 구조 고급형 농구링 농구골망 간편한운동 스포츠/레저>농구>농구대
- text: 판 점수 배구 농구 전자 스포츠/레저>농구>기타농구용품
- text: 낫소 농구공 믹스 매치 BMM 장기간 공기 보존 스포츠/레저>농구>농구공
- text: 스타스포츠 농구 트레이닝 포지션 마커 세트 OFKNN1O2 스포츠/레저>농구>농구대
- text: 소닉블라스트폭스40 휘슬와치 손목스톱워치세트폭스40 6906-0700 스포츠/레저>농구>기타농구용품
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:** 6 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>'NYS 365 긴팔 티셔츠 빅로고 농구유니폼 농구의류 슈팅셔츠 롱슬리브 상의 스포츠/레저>농구>농구의류'</li><li>'나이키 남성 맥스90 농구 티셔츠 FV8395-345 스포츠/레저>농구>농구의류'</li><li>'농구져지 나시 농구 반티 메쉬 시카고불스 농구복 유니폼 민소매 헬스 짐웨어 트레이닝 티셔츠 스포츠/레저>농구>농구의류'</li></ul> |
| 0.0 | <ul><li>'타요 이지훅 농구대 세트 스포츠/레저>농구>기타농구용품'</li><li>'먼지제거 더스터 슈 체육관신발 몰텐 농구장 보드판 AW5EA0E1 스포츠/레저>농구>기타농구용품'</li><li>'Kuangmi 카우아미 농구 6호 7호 스트리트볼 KMbb18 흰색 6호 스포츠/레저>농구>기타농구용품'</li></ul> |
| 3.0 | <ul><li>'접이식 농구 게임 슈팅 골대 슛팅 연습 게임기 스포츠 스포츠/레저>농구>농구대'</li><li>'농구대 벽걸이 야외 연습 백보드 농구골대 체육관 스포츠/레저>농구>농구대'</li><li>'농구네트 이동식 거치대 트레이닝 패스 연습 기구 스포츠/레저>농구>농구대'</li></ul> |
| 2.0 | <ul><li>'농구 축구 풋살 공3개입 3볼백 스타 볼가방 중등부 스포츠/레저>농구>농구공가방'</li><li>'엄브로 백팩 이지 18L 에어팟 파우치 구성 풋살 블루 UP123CBP11 114856 스포츠/레저>농구>농구공가방'</li><li>'미카사 공가방 3개입 AC-BG230W 스포츠/레저>농구>농구공가방'</li></ul> |
| 1.0 | <ul><li>'NBA NCAA 윌슨 농구공 한정판 DRV ENDURE PU 7호 스포츠/레저>농구>농구공'</li><li>'클래식 점보 농구공 스포츠/레저>농구>농구공'</li><li>'몰텐 농구공 7호 KBL 공인구 BG4000 스포츠/레저>농구>농구공'</li></ul> |
| 5.0 | <ul><li>'조던 레거시 312 로우 파이어 Jordan Legacy Low Fire 547656 스포츠/레저>농구>농구화'</li><li>'JORDAN 조던 11 레트로 로우 시멘트 조단 11 Retro Low Cement 스포츠/레저>농구>농구화'</li><li>'아식스 젤 후프 V15 스탠다드 농구화 1063A063 100 스포츠/레저>농구>농구화'</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_sl5")
# Run inference
preds = model("판 점수 배구 농구 전자 스포츠/레저>농구>기타농구용품")
```
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## 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.*
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### Recommendations
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 9.1981 | 23 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 70 |
| 4.0 | 70 |
| 5.0 | 69 |
### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0122 | 1 | 0.5273 | - |
| 0.6098 | 50 | 0.4932 | - |
| 1.2195 | 100 | 0.2677 | - |
| 1.8293 | 150 | 0.0673 | - |
| 2.4390 | 200 | 0.0159 | - |
| 3.0488 | 250 | 0.0002 | - |
| 3.6585 | 300 | 0.0001 | - |
| 4.2683 | 350 | 0.0001 | - |
| 4.8780 | 400 | 0.0 | - |
| 5.4878 | 450 | 0.0 | - |
| 6.0976 | 500 | 0.0 | - |
| 6.7073 | 550 | 0.0 | - |
| 7.3171 | 600 | 0.0 | - |
| 7.9268 | 650 | 0.0 | - |
| 8.5366 | 700 | 0.0 | - |
| 9.1463 | 750 | 0.0 | - |
| 9.7561 | 800 | 0.0 | - |
| 10.3659 | 850 | 0.0 | - |
| 10.9756 | 900 | 0.0001 | - |
| 11.5854 | 950 | 0.0 | - |
| 12.1951 | 1000 | 0.0 | - |
| 12.8049 | 1050 | 0.0 | - |
| 13.4146 | 1100 | 0.0 | - |
| 14.0244 | 1150 | 0.0 | - |
| 14.6341 | 1200 | 0.0 | - |
| 15.2439 | 1250 | 0.0 | - |
| 15.8537 | 1300 | 0.0001 | - |
| 16.4634 | 1350 | 0.0 | - |
| 17.0732 | 1400 | 0.0 | - |
| 17.6829 | 1450 | 0.0 | - |
| 18.2927 | 1500 | 0.0 | - |
| 18.9024 | 1550 | 0.0 | - |
| 19.5122 | 1600 | 0.0 | - |
| 20.1220 | 1650 | 0.0 | - |
| 20.7317 | 1700 | 0.0 | - |
| 21.3415 | 1750 | 0.0 | - |
| 21.9512 | 1800 | 0.0 | - |
| 22.5610 | 1850 | 0.0 | - |
| 23.1707 | 1900 | 0.0 | - |
| 23.7805 | 1950 | 0.0 | - |
| 24.3902 | 2000 | 0.0 | - |
| 25.0 | 2050 | 0.0 | - |
| 25.6098 | 2100 | 0.0 | - |
| 26.2195 | 2150 | 0.0 | - |
| 26.8293 | 2200 | 0.0 | - |
| 27.4390 | 2250 | 0.0 | - |
| 28.0488 | 2300 | 0.0 | - |
| 28.6585 | 2350 | 0.0 | - |
| 29.2683 | 2400 | 0.0 | - |
| 29.8780 | 2450 | 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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