README.md

---
library_name: setfit
metrics:
- f1
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: To make introductions between Camelot's Chairman and the Cabinet Secretary.
    We discussed the operation of the UK National Lottery and how to maximise returns
    to National Lottery Good Causes as well as our plans to celebrate the 25th birthday
    of The National Lottery.
- text: Discussion on crime
- text: To discuss Northern Powerhouse Rail and HS2
- text: To discuss food security
- text: Electricity market
inference: True
model-index:
- name: SetFit
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: f1
      value: 0.92
      name: F1
    - type: accuracy
      value: 0.9658119658119658
      name: Accuracy
---

# SetFit

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 4 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)

## Evaluation

### Metrics
| Label   | F1   | Accuracy |
|:--------|:-----|:---------|
| **all** | 0.92 | 0.9658   |

## 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("twright8/setfit-oversample-labels-lobbying")
# Run inference
preds = model("Electricity market")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 26.1406 | 153 |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (4, 9)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (1.0797496673911536e-05, 3.457046714445997e-05)
- head_learning_rate: 0.0004470582121407239
- loss: CoSENTLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: True
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch   | Step    | Training Loss | Validation Loss |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0040  | 1       | 19.1843       | -               |
| 0.2024  | 50      | 11.3434       | -               |
| 0.4049  | 100     | 9.3116        | -               |
| 0.6073  | 150     | 2.7233        | -               |
| 0.8097  | 200     | 1.5662        | -               |
| **1.0** | **247** | **-**         | **14.3603**     |
| 1.0121  | 250     | 0.0159        | -               |
| 1.2146  | 300     | 0.0135        | -               |
| 1.4170  | 350     | 0.0003        | -               |
| 1.6194  | 400     | 0.0002        | -               |
| 1.8219  | 450     | 0.0007        | -               |
| 2.0     | 494     | -             | 16.8205         |
| 2.0243  | 500     | 0.0023        | -               |
| 2.2267  | 550     | 0.0004        | -               |
| 2.4291  | 600     | 0.0001        | -               |
| 2.6316  | 650     | 0.0           | -               |
| 2.8340  | 700     | 0.0003        | -               |
| 3.0     | 741     | -             | 15.2312         |
| 3.0364  | 750     | 0.0           | -               |
| 3.2389  | 800     | 3.1257        | -               |
| 3.4413  | 850     | 0.0001        | -               |
| 3.6437  | 900     | 0.0002        | -               |
| 3.8462  | 950     | 0.0139        | -               |
| 4.0     | 988     | -             | 14.4995         |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu118
- Datasets: 2.20.0
- Tokenizers: 0.15.2

## 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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