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master_cate_sl30

Text Classification
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master_cate_sl30 / README.md
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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 요넥스 테니스공 홀더 메탈 볼클립 볼걸이 테니스용품 스포츠/레저>테니스>기타테니스용품
- text: 스트링 스타팅 클램프 알루미늄 합금 익스텐션 코드 테니스 배드민턴 전문 액세서리 1m 스포츠/레저>테니스>기타테니스용품
- text: 60 개 롤 스풀 10m 탄성 신축성 스트링 스레드 헤어 익스텐션 스레드 와이 스포츠/레저>테니스>스트링
- text: 알로 MATCH POINT 여성 테니스 스커트 스포츠/레저>테니스>테니스의류
- text: 디아도라 AIR TEX 테니스 볼 그래픽 반팔 티셔츠 GREEN D4221TRS14GNL 스포츠/레저>테니스>테니스의류
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:** 8 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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0 | <ul><li>'윌슨 테니스 진동 방지 2개들이 PROFEEL 프로필 WRZ537700 스포츠/레저>테니스>기타테니스용품'</li><li>'국제 단추엘보 대 2단 1개입 엘보링 테니스라켓 댐프너 스포츠/레저>테니스>기타테니스용품'</li><li>'테니스 라켓거치대 배드민턴채 진열대 수납 보관대 스포츠/레저>테니스>기타테니스용품'</li></ul> |
| 5.0 | <ul><li>'윌슨 클래시 100 투어 테니스라켓 WR005711 스포츠/레저>테니스>테니스라켓'</li><li>'요넥스 아스트렐 100 테니스라켓 YY1209RT030 스포츠/레저>테니스>테니스라켓'</li><li>'낫소 옵티멈 투어 테니스라켓 스포츠/레저>테니스>테니스라켓'</li></ul> |
| 2.0 | <ul><li>'슈퍼스트링 갓클래스 신의클래스 122 127 12M 스포츠/레저>테니스>스트링'</li><li>'낫소 다이너마이트 테니스 스트링 200M 스포츠/레저>테니스>스트링'</li><li>'LUXILON 럭실론 테니스 스트링 거트 롤 알루파워 러프 1 25 200m WRZ9902 스포츠/레저>테니스>스트링'</li></ul> |
| 4.0 | <ul><li>'낫소스포츠 낫소 통볼 T-507C 스포츠/레저>테니스>테니스공'</li><li>'신신상사 스타스포츠 매치포인트 시합구 TB172 스포츠/레저>테니스>테니스공'</li><li>'프록시마 매치 포인트 테니스공 스포츠/레저>테니스>테니스공'</li></ul> |
| 0.0 | <ul><li>'투나 GENUINE 가죽그립 1 교체용 쿠션그립 리플레이스먼트 테니스 원그립 스포츠/레저>테니스>그립'</li><li>'낫소 테니스 오버그립 30개입 NSOG-30 스포츠/레저>테니스>그립'</li><li>'감마 GAMMA Tennis Overgrip Ideal for Tennis Pickleball Squash Badminton and Racquetball Durable and 스포츠/레저>테니스>그립'</li></ul> |
| 3.0 | <ul><li>'윌슨 WILSON 테니스 쉴드 슬링백 라켓 가방 스포츠/레저>테니스>테니스가방'</li><li>'부천정스포츠 라코스테 테니스가방 락팩 L23 스포츠 대용량 가방 스포츠/레저>테니스>테니스가방'</li><li>'윌슨 더플백 스포츠/레저>테니스>테니스가방'</li></ul> |
| 7.0 | <ul><li>'아식스 젤리솔루션 9 올코트 1041A330 600 스포츠/레저>테니스>테니스화'</li><li>'아식스 COURT SLIDE 3 CLAY OC코트 슬라이드 OC 여성 테니스화 옴니 클레이용 신발 1042A230 220824ASTS 스포츠/레저>테니스>테니스화'</li><li>'아식스 코트 FF3 올코트 테니스화 여성 1042A220 400 스포츠/레저>테니스>테니스화'</li></ul> |
| 6.0 | <ul><li>'윌슨 여성 윈 풀온 플리츠 테니스숏 반바지 클래식네이비 스포츠/레저>테니스>테니스의류'</li><li>'디아도라 테니스 라이프 그래픽 반팔티셔츠 VIOLET 스포츠/레저>테니스>테니스의류'</li><li>'라코스테 스포츠 치마바지 테니스 베이직 플리츠 스커트 7WJ JF0990-54G 스포츠/레저>테니스>테니스의류'</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_sl30")
# Run inference
preds = model("알로 MATCH POINT 여성 테니스 스커트 스포츠/레저>테니스>테니스의류")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 8.2241 | 18 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 70 |
| 4.0 | 50 |
| 5.0 | 70 |
| 6.0 | 70 |
| 7.0 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0094 | 1 | 0.4693 | - |
| 0.4717 | 50 | 0.4966 | - |
| 0.9434 | 100 | 0.2749 | - |
| 1.4151 | 150 | 0.0397 | - |
| 1.8868 | 200 | 0.0179 | - |
| 2.3585 | 250 | 0.0076 | - |
| 2.8302 | 300 | 0.0 | - |
| 3.3019 | 350 | 0.0 | - |
| 3.7736 | 400 | 0.0 | - |
| 4.2453 | 450 | 0.0 | - |
| 4.7170 | 500 | 0.0 | - |
| 5.1887 | 550 | 0.0 | - |
| 5.6604 | 600 | 0.0 | - |
| 6.1321 | 650 | 0.0 | - |
| 6.6038 | 700 | 0.0 | - |
| 7.0755 | 750 | 0.0 | - |
| 7.5472 | 800 | 0.0 | - |
| 8.0189 | 850 | 0.0 | - |
| 8.4906 | 900 | 0.0 | - |
| 8.9623 | 950 | 0.0 | - |
| 9.4340 | 1000 | 0.0 | - |
| 9.9057 | 1050 | 0.0 | - |
| 10.3774 | 1100 | 0.0 | - |
| 10.8491 | 1150 | 0.0 | - |
| 11.3208 | 1200 | 0.0 | - |
| 11.7925 | 1250 | 0.0 | - |
| 12.2642 | 1300 | 0.0 | - |
| 12.7358 | 1350 | 0.0 | - |
| 13.2075 | 1400 | 0.0 | - |
| 13.6792 | 1450 | 0.0 | - |
| 14.1509 | 1500 | 0.0 | - |
| 14.6226 | 1550 | 0.0 | - |
| 15.0943 | 1600 | 0.0 | - |
| 15.5660 | 1650 | 0.0 | - |
| 16.0377 | 1700 | 0.0 | - |
| 16.5094 | 1750 | 0.0 | - |
| 16.9811 | 1800 | 0.0 | - |
| 17.4528 | 1850 | 0.0 | - |
| 17.9245 | 1900 | 0.0 | - |
| 18.3962 | 1950 | 0.0 | - |
| 18.8679 | 2000 | 0.0 | - |
| 19.3396 | 2050 | 0.0 | - |
| 19.8113 | 2100 | 0.0 | - |
| 20.2830 | 2150 | 0.0 | - |
| 20.7547 | 2200 | 0.0 | - |
| 21.2264 | 2250 | 0.0 | - |
| 21.6981 | 2300 | 0.0 | - |
| 22.1698 | 2350 | 0.0 | - |
| 22.6415 | 2400 | 0.0 | - |
| 23.1132 | 2450 | 0.0 | - |
| 23.5849 | 2500 | 0.0 | - |
| 24.0566 | 2550 | 0.0 | - |
| 24.5283 | 2600 | 0.0 | - |
| 25.0 | 2650 | 0.0 | - |
| 25.4717 | 2700 | 0.0 | - |
| 25.9434 | 2750 | 0.0 | - |
| 26.4151 | 2800 | 0.0 | - |
| 26.8868 | 2850 | 0.0 | - |
| 27.3585 | 2900 | 0.0 | - |
| 27.8302 | 2950 | 0.0 | - |
| 28.3019 | 3000 | 0.0 | - |
| 28.7736 | 3050 | 0.0 | - |
| 29.2453 | 3100 | 0.0 | - |
| 29.7170 | 3150 | 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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