---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Aircraft position displayed on screen is erratic.
- text: Frequent signal loss during communication with ATC.
- text: Radar detects a non-existent aircraft nearby.
- text: Unexpected loss of altitude while in autopilot mode.
- text: Cabin lighting and screens are controlled unexpectedly.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
datasets:
- Petitepoupoune/Cyberattacks_aviation
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: Petitepoupoune/Cyberattacks_aviation
      type: Petitepoupoune/Cyberattacks_aviation
      split: test
    metrics:
    - type: accuracy
      value: 0.6666666666666666
      name: Accuracy
---

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

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [Petitepoupoune/Cyberattacks_aviation](https://huggingface.co/datasets/Petitepoupoune/Cyberattacks_aviation) dataset 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 [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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 10 classes
- **Training Dataset:** [Petitepoupoune/Cyberattacks_aviation](https://huggingface.co/datasets/Petitepoupoune/Cyberattacks_aviation)
<!-- - **Language:** 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                                                                                                                                                                                                                     |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'The radar display suddenly shows multiple ghost aircraft.'</li><li>'Engine parameters display fluctuates, but engine runs fine.'</li><li>'Pilot receives incorrect weather data from ground station.'</li></ul>     |
| 1     | <ul><li>'Navigation coordinates keep shifting without any inputs.'</li><li>'Navigation system reports inconsistent coordinates.'</li></ul>                                                                                   |
| 2     | <ul><li>'Unable to establish secure communication with the ground.'</li><li>'Pilot headset communication filled with static noises.'</li><li>'GPS fails to lock onto satellites during flight.'</li></ul>                    |
| 3     | <ul><li>'Unexpected engine alert appeared without apparent malfunction.'</li><li>'Air Traffic Control reports conflicting position data.'</li><li>'Ground proximity warnings trigger in normal flight conditions.'</li></ul> |
| 4     | <ul><li>'Passenger internet shows anomalies, potentially exposing data.'</li><li>'Unusual network activity detected in cockpit systems.'</li><li>'Passengers report unauthorized access to personal devices.'</li></ul>      |
| 5     | <ul><li>'Pilots unable to update flight plan due to system freeze.'</li><li>'Cabin displays turn off intermittently without reason.'</li><li>'Unusual delay in system response when adjusting controls.'</li></ul>           |
| 6     | <ul><li>'Incorrect altitude data reported by onboard instruments.'</li><li>'Cockpit alarm indicates incorrect fuel levels.'</li><li>'Unexpected power fluctuation in avionics systems.'</li></ul>                            |
| 7     | <ul><li>'Aircraft is directed off course by autopilot without input.'</li><li>'Sudden and unexplained decrease in engine thrust.'</li><li>'Aircraft enters unexpected descent despite normal controls.'</li></ul>            |
| 8     | <ul><li>'In-flight entertainment malfunctions and reboots frequently.'</li><li>'Unexpected system update initiated during flight.'</li><li>'Sudden reboot of all electronic systems mid-flight.'</li></ul>                   |
| 9     | <ul><li>'Minor turbulence encountered during flight.'</li><li>'Pilot reports fatigue after long flight hours.'</li><li>'Passenger complains about seatbelt malfunction.'</li></ul>                                           |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.6667   |

## 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("Petitepoupoune/SetFit_Cyberaviation")
# Run inference
preds = model("Radar detects a non-existent aircraft nearby.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 5   | 6.7857 | 10  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 4                     |
| 1     | 2                     |
| 2     | 6                     |
| 3     | 3                     |
| 4     | 4                     |
| 5     | 6                     |
| 6     | 3                     |
| 7     | 4                     |
| 8     | 4                     |
| 9     | 6                     |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0095 | 1    | 0.2581        | -               |
| 0.4762 | 50   | 0.1219        | -               |
| 0.9524 | 100  | 0.0351        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.19.1

## 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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