README.md

---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- ag_news
metrics:
- accuracy
widget:
- text: FSU-Miami Postponed Hurricane Frances forces the postponement of Monday's
    college football season opener between Florida State and Miami.
- text: Lenovo to buy IBM PC arm IBM said late Tuesday that it will sell its personal
    computer division, transferring an iconic brand to a Chinese rival that also will
    absorb about 2,000 local workers.
- text: 'NBA Roundup: Sonics fly high again in Philly PHILADELPHIA - Wide open or
    contested, the Seattle SuperSonics hit three-pointers from all over the court.
    Ray Allen scored a season-high 37 points, Rashard Lewis had 21 and Vladimir Radmanovic
    added 20, leading '
- text: Democrats Come to Observe Convention (AP) AP - The Democrats have come to
    town to prick rhetorical balloons at the Republican National Convention.
- text: 'US women into final The United States edged past world champions Germany
    in a dramatic 2-1 victory to seal their place in the women #39;s football final.'
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-MiniLM-L3-v2
---

# SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [ag_news](https://huggingface.co/datasets/ag_news) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) 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:** [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [ag_news](https://huggingface.co/datasets/ag_news)
<!-- - **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)

## 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("vincent1337/test_student_model")
# Run inference
preds = model("FSU-Miami Postponed Hurricane Frances forces the postponement of Monday's college football season opener between Florida State and Miami.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 18  | 36.04  | 51  |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 16)
- max_steps: 50
- sampling_strategy: oversampling
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0196 | 1    | 0.8923        | -               |
| 0.9804 | 50   | 0.0968        | -               |
| 0.0196 | 1    | 0.0852        | -               |
| 0.9804 | 50   | 0.0048        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- 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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