
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. 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 body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 9 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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### 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                                                                                                                                                                                                                                                                    |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 8     | <ul><li>'Later she meets someone at the bar. He'</li><li>'He heads to them and sits. The bus'</li><li>'Someone leaps to his feet and punches the agent in the face. Seemingly unaffected, the agent'</li></ul>                                                              |
| 2     | <ul><li>'A man sits behind a desk. Two people'</li><li>'A man is seen standing at the bottom of a hole while a man records him. Two men'</li><li>'Someone questions his female colleague who shrugs. Through a window, we'</li></ul>                                        |
| 0     | <ul><li>'A woman bends down and puts something on a scale. She then'</li><li>'He pulls down the blind. He'</li><li>'Someone flings his hands forward. The someone fires, but the water'</li></ul>                                                                           |
| 6     | <ul><li>'People are sitting down on chairs. They'</li><li>'They look up at stained glass skylights. The Americans'</li><li>'The lady and the man dance around each other in a circle. The people'</li></ul>                                                                 |
| 1     | <ul><li>'An older gentleman kisses her. As he leads her off, someone'</li><li>'The first girl comes back and does it effortlessly as the second girl still struggles. For the last round, the girl'</li><li>'As she leaves, the bartender smiles. Now the blonde'</li></ul> |
| 3     | <ul><li>'Someone lowers his demoralized gaze. Someone'</li><li>'Someone goes into his bedroom. Someone'</li><li>'As someone leaves, someone spots him on the monitor. Someone'</li></ul>                                                                                    |
| 7     | <ul><li>'Four inches of Plexiglas separate the two and they talk on monitored phones. Someone'</li><li>'The American and Russian commanders each watch them returning. As someone'</li><li>'A group of walkers walk along the sidewalk near the lake. A man'</li></ul>      |
| 4     | <ul><li>'The secretary flexes the foot of her crossed - leg as she eyes someone. The woman'</li><li>'A man in a white striped shirt is smiling. A woman'</li><li>'He grabs her hair and pulls her head back. She'</li></ul>                                                 |
| 5     | <ul><li>'He heads out of the plaza. Someone'</li><li>"As he starts back, he sees someone's scared look just before he slams the door shut. Someone"</li><li>'He nods at her beaming. Someone'</li></ul>                                                                     |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.1654   |

## 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("HelgeKn/Swag-multi-class-20")
# Run inference
preds = model("He sneers and winds up with his fist. Someone")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 5   | 12.1056 | 33  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 20                    |
| 1     | 20                    |
| 2     | 20                    |
| 3     | 20                    |
| 4     | 20                    |
| 5     | 20                    |
| 6     | 20                    |
| 7     | 20                    |
| 8     | 20                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0022 | 1    | 0.3747        | -               |
| 0.1111 | 50   | 0.2052        | -               |
| 0.2222 | 100  | 0.1878        | -               |
| 0.3333 | 150  | 0.1126        | -               |
| 0.4444 | 200  | 0.1862        | -               |
| 0.5556 | 250  | 0.1385        | -               |
| 0.6667 | 300  | 0.0154        | -               |
| 0.7778 | 350  | 0.0735        | -               |
| 0.8889 | 400  | 0.0313        | -               |
| 1.0    | 450  | 0.0189        | -               |
| 1.1111 | 500  | 0.0138        | -               |
| 1.2222 | 550  | 0.0046        | -               |
| 1.3333 | 600  | 0.0043        | -               |
| 1.4444 | 650  | 0.0021        | -               |
| 1.5556 | 700  | 0.0033        | -               |
| 1.6667 | 750  | 0.001         | -               |
| 1.7778 | 800  | 0.0026        | -               |
| 1.8889 | 850  | 0.0022        | -               |
| 2.0    | 900  | 0.0014        | -               |

### Framework Versions
- Python: 3.9.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.0
- PyTorch: 2.1.1+cpu
- Datasets: 2.15.0
- Tokenizers: 0.15.0

## 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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{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "widget": [{"text": "She picks up a wine glass and takes a drink. She"}, {"text": "Someone smiles as she looks out her window. Their car"}, {"text": "Someone turns and her jaw drops at the site of the other woman. Moving in slow motion, someone"}, {"text": "He sneers and winds up with his fist. Someone"}, {"text": "He smooths it back with his hand. Finally, appearing confident and relaxed and with the old familiar glint in his eyes, someone"}], "pipeline_tag": "text-classification", "inference": true, "base_model": "sentence-transformers/paraphrase-mpnet-base-v2", "model-index": [{"name": "SetFit with sentence-transformers/paraphrase-mpnet-base-v2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.16538461538461538, "name": "Accuracy"}]}]}]}
setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 model-index region:us