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

This is a SetFit model trained on the Petitepoupoune/Cyberattacks_aviation dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression 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 with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
0
  • 'The radar display suddenly shows multiple ghost aircraft.'
  • 'Engine parameters display fluctuates, but engine runs fine.'
  • 'Pilot receives incorrect weather data from ground station.'
1
  • 'Navigation coordinates keep shifting without any inputs.'
  • 'Navigation system reports inconsistent coordinates.'
2
  • 'Unable to establish secure communication with the ground.'
  • 'Pilot headset communication filled with static noises.'
  • 'GPS fails to lock onto satellites during flight.'
3
  • 'Unexpected engine alert appeared without apparent malfunction.'
  • 'Air Traffic Control reports conflicting position data.'
  • 'Ground proximity warnings trigger in normal flight conditions.'
4
  • 'Passenger internet shows anomalies, potentially exposing data.'
  • 'Unusual network activity detected in cockpit systems.'
  • 'Passengers report unauthorized access to personal devices.'
5
  • 'Pilots unable to update flight plan due to system freeze.'
  • 'Cabin displays turn off intermittently without reason.'
  • 'Unusual delay in system response when adjusting controls.'
6
  • 'Incorrect altitude data reported by onboard instruments.'
  • 'Cockpit alarm indicates incorrect fuel levels.'
  • 'Unexpected power fluctuation in avionics systems.'
7
  • 'Aircraft is directed off course by autopilot without input.'
  • 'Sudden and unexplained decrease in engine thrust.'
  • 'Aircraft enters unexpected descent despite normal controls.'
8
  • 'In-flight entertainment malfunctions and reboots frequently.'
  • 'Unexpected system update initiated during flight.'
  • 'Sudden reboot of all electronic systems mid-flight.'
9
  • 'Minor turbulence encountered during flight.'
  • 'Pilot reports fatigue after long flight hours.'
  • 'Passenger complains about seatbelt malfunction.'

Evaluation

Metrics

Label Accuracy
all 0.6667

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

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.")

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

@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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