File size: 8,561 Bytes

a61c578

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Pyramex Goliath 보안경 프레임 렌즈 스포츠/레저>스쿼시>기타스쿼시용품
- text: 베이퍼 130 라님 엘 윌리 스포츠/레저>스쿼시>스쿼시라켓
- text: HEAD 스파크 팀 스쿼시 팩 라켓 안경 공 2개 파란색 스포츠/레저>스쿼시>기타스쿼시용품
- text: 헤드 HEAD Spark Team Pack 2024 스포츠/레저>스쿼시>스쿼시라켓
- text: 던롭 DunLop 스쿼시볼 경기용 낱개 1개입 스포츠/레저>스쿼시>기타스쿼시용품
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:** 3 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                                                                                                                                                                                                                                                   |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0.0   | <ul><li>'헤드 HEAD 남성용 그리드 2 0 로우 라켓볼스쿼시 실내 코트 슈즈 자국이 정품보장 스포츠/레저>스쿼시>기타스쿼시용품'</li><li>'테크니화이버 초록줄 릴 200m TF 스쿼시스트링 20회작업분 TF-305 1 스포츠/레저>스쿼시>기타스쿼시용품'</li><li>'MOTUZP 단일 도트 스쿼시 공 고무 고탄력 라켓 초보자 경쟁 훈련을위한 훈련 연습을위한 single dot 스포츠/레저>스쿼시>기타스쿼시용품'</li></ul> |
| 2.0   | <ul><li>'테크니화이버 Carboflex 125 X탑 언스트렁 스쿼시 라켓 138966103 스포츠/레저>스쿼시>스쿼시라켓'</li><li>'Gearbox GB3K 170Q 라켓볼 라켓 3 58 그립 스포츠/레저>스쿼시>스쿼시라켓'</li><li>'Tecnifibre 스쿼시 Carboflex 125S 라켓 SynGut 스트링 스포츠/레저>스쿼시>스쿼시라켓'</li></ul>                                      |
| 1.0   | <ul><li>'던롭 PRO 스쿼시볼 스포츠/레저>스쿼시>스쿼시공'</li><li>'브니엘 토너먼트 스쿼시볼 스포츠/레저>스쿼시>스쿼시공'</li><li>'던롭 Pro 스쿼시볼 (유리 코트 전용구) 스포츠/레저>스쿼시>스쿼시공'</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_sl18")
# Run inference
preds = model("베이퍼 130 라님 엘 윌리 스포츠/레저>스쿼시>스쿼시라켓")
```

<!--
### 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   | 4   | 9.4626 | 18  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 7                     |
| 2.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0345  | 1    | 0.4863        | -               |
| 1.7241  | 50   | 0.2641        | -               |
| 3.4483  | 100  | 0.018         | -               |
| 5.1724  | 150  | 0.0           | -               |
| 6.8966  | 200  | 0.0           | -               |
| 8.6207  | 250  | 0.0           | -               |
| 10.3448 | 300  | 0.0           | -               |
| 12.0690 | 350  | 0.0           | -               |
| 13.7931 | 400  | 0.0           | -               |
| 15.5172 | 450  | 0.0           | -               |
| 17.2414 | 500  | 0.0           | -               |
| 18.9655 | 550  | 0.0           | -               |
| 20.6897 | 600  | 0.0           | -               |
| 22.4138 | 650  | 0.0           | -               |
| 24.1379 | 700  | 0.0           | -               |
| 25.8621 | 750  | 0.0           | -               |
| 27.5862 | 800  | 0.0           | -               |
| 29.3103 | 850  | 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.*
-->