---
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: 0.12786885245901639
      name: Accuracy
---

# 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) -->
<!-- - **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                                                                                                                                                                                                                                                                                                                                                                           |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4     | <ul><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><li>"He finds someone's records in a box. Someone"</li><li>'With a nod, the man hands it over to the defeated boy. Someone'</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>'We see a man dunk the ball twice. We'</li><li>'Several people use different methods to perform trick shots. They continue performing impressive shots'</li></ul>                                                                             |
| 6     | <ul><li>'A young child is moving back and fourth on a swing while laughing and smiling to the camera. The child'</li><li>'The son of Poseidon holds the water at bay on either side of himself. Someone'</li><li>'The guy pours product in a container and uses a brush to put the liquid on the surface of a metal object. The guy'</li></ul>                                     |
| 8     | <ul><li>'A woman smiles at the camera. The woman'</li><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></ul>                                                                                                                                                                                    |
| 2     | <ul><li>'As our view retracts through the star map a holographic line sets out from the gunner chair and targets hologram of the planet earth. She'</li><li>'Together, they wander a few steps without taking their eyes off of him. Now in the car as someone drives, someone'</li><li>'People stand by the wall, laughing. He'</li></ul>                                         |
| 0     | <ul><li>'Someone steps outside and opens an umbrella. Someone halts,'</li><li>'He shows a water bottle he has along with a brush, and uses the brush to remove snow from the dash window of a car and the water to remove any excess snow left on the windshield. Once finished, he'</li><li>'She opens a small metal box on a desk and pushes a button inside. Someone'</li></ul> |
| 5     | <ul><li>'Now in the eating quarters, someone faces a husky, larged - nosed cook. The cook'</li><li>'She forces a smile, then watches him place his hand on her hand. He caresses her cheek, and she'</li><li>'Someone stirs the cookie dough in a bowl. The dough'</li></ul>                                                                                                       |
| 3     | <ul><li>'A kid in blue shorts is vacuuming the floor. A kid in a red shirt'</li><li>'The official extends a red flag. As Master someone'</li><li>'The girls flips, then runs, flips and dismounts. The cloud'</li></ul>                                                                                                                                                            |
| 7     | <ul><li>'She flinches, but quickly composes herself and moves on. The crowd of onlookers'</li><li>'He eyes someone with a furrowed brow, then springs up and hurries after her. Someone and someone'</li><li>'Now, someone stands below an overcast sky. Strands of his greasy black hair'</li></ul>                                                                               |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.1279   |

## 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-6")
# 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   | 14.3148 | 40  |

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

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0074 | 1    | 0.303         | -               |
| 0.3704 | 50   | 0.1185        | -               |
| 0.7407 | 100  | 0.0656        | -               |
| 1.1111 | 150  | 0.0179        | -               |
| 1.4815 | 200  | 0.0109        | -               |
| 1.8519 | 250  | 0.0076        | -               |

### 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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