README.md

---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- precision
- recall
- f1
widget:
- text: GMB Gambia
- text: ' end flyout 2 '
- text: 'Books

    '
- text: Persistent
- text: Session
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.87325
      name: Accuracy
    - type: precision
      value: 0.8566450970632156
      name: Precision
    - type: recall
      value: 0.8871134020618556
      name: Recall
    - type: f1
      value: 0.8716130665991391
      name: F1
---

# SetFit

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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:** [Unknown](https://huggingface.co/unknown) -->
- **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:** 2 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                                                                                                                                                                                                                     |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| True  | <ul><li>'-. Pepsi-Colacold beats any cola cold! '</li><li>"Use “Jemes! et : L lemen peeple wen't Lemon. “i720 ait? "</li><li>'Ifit happens once, it could happen again. soptacaceee tates | WOE ¥ 1800 774 5025. '</li></ul> |
| False | <ul><li>'ps-script'</li><li>'Make your bidder browser agnostic to access high-performing cookie alternative supply'</li><li>'International Students & Scholars'</li></ul>                                                    |

## Evaluation

### Metrics
| Label   | Accuracy | Precision | Recall | F1     |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.8732   | 0.8566    | 0.8871 | 0.8716 |

## 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("setfit_model_id")
# Run inference
preds = model("Books
")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max  |
|:-------------|:----|:-------|:-----|
| Word count   | 1   | 8.4845 | 1060 |

| Label | Training Sample Count |
|:------|:----------------------|
| False | 7940                  |
| True  | 8060                  |

### Framework Versions
- Python: 3.11.0
- SetFit: 1.0.3
- Sentence Transformers: 2.3.0
- Transformers: 4.37.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1

## 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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