
# 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
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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                                                                                                                                                                                                                                                                                                                                           |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6     | <ul><li>'The girl does 2 perfect flips. The girls'</li><li>'Emerging in open water, he does a breaststroke toward the murky. He'</li><li>'A young child is moving back and fourth on a swing while laughing and smiling to the camera. The child'</li></ul>                                                                                        |
| 4     | <ul><li>'He turns away and covers his face with one hand. Someone'</li><li>'With a nod, the man hands it over to the defeated boy. Someone'</li><li>"On the shop floor, his little helper helps himself to an expensive handbag from a display cabinet, then some women's designer shoes, all of which are detailed on a list. He"</li></ul>       |
| 1     | <ul><li>'A lot of people are sitting on terraces in a big field and people is walking in the entrance of a big stadium. men'</li><li>'The water gets rough as the past through some rocks. Several people'</li><li>'We see a man dunk the ball twice. We'</li></ul>                                                                                |
| 5     | <ul><li>'A man wearing a safari hat leads a group of camels with riders in a single file. The camera'</li><li>'Someone stirs the cookie dough in a bowl. The dough'</li><li>'A logo for a sports even is shown. There'</li></ul>                                                                                                                   |
| 8     | <ul><li>'A girl is shown several times running on a track. She'</li><li>'Someone peers out from the cabin. As she emerges, someone'</li><li>'Someone and someone swap looks. She'</li></ul>                                                                                                                                                        |
| 2     | <ul><li>'In slow motion, both the Russians and Americans celebrate. Someone'</li><li>'Through a window, we watch someone raise his teacup to his companions. At home, someone'</li><li>'People stand by the wall, laughing. He'</li></ul>                                                                                                          |
| 7     | <ul><li>'Someone turns at the sound of the distant horns. 6000 horsemen, lead by people,'</li><li>"The man plays the video in reverse to look as if he's putting shaving cream on with the razor. The men then"</li><li>'He eyes someone with a furrowed brow, then springs up and hurries after her. Someone and someone'</li></ul>               |
| 0     | <ul><li>'She opens a small metal box on a desk and pushes a button inside. Someone'</li><li>'Someone starts to say something then thinks better of it, and remains silent. Someone'</li><li>"Someone changes into a Spanish policeman's outfit and heads down an outside staircase with the packed up rifle. As someone leaves, someone"</li></ul> |
| 3     | <ul><li>'He is shown playing a game with a virtual sumo wrestler. The shorter man'</li><li>'The girls flips, then runs, flips and dismounts. The cloud'</li><li>'The official extends a red flag. As Master someone'</li></ul>                                                                                                                     |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.1148   |

## 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-4")
# Run inference
preds = model("He approaches the object and reads a plaque on its side. Someone")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 6   | 13.25  | 31  |

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

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0111 | 1    | 0.2285        | -               |
| 0.5556 | 50   | 0.0567        | -               |
| 1.1111 | 100  | 0.0083        | -               |
| 1.6667 | 150  | 0.0084        | -               |

### 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": "Someone comes out of the shack and shoves one of the kids to the ground. He"}, {"text": "He approaches the object and reads a plaque on its side. Someone"}, {"text": "Later at someone's family farm, someone sees the lights on in the hangar. Someone"}, {"text": "Someone stands looking over some of the old photographs as someone goes through the mess on the desk. Someone"}, {"text": "Snow blows around a city of towering crystalline structures. A warrior"}], "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": 0.11475409836065574, "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