simocorbo/toxicthesis-vicunauc-distillseq-classification-2

DISTILLSEQ - VICUNAUC - Classification (2 classes)

Toxicity prediction model trained on the VICUNAUC dataset.

Property	Value
Model	DISTILLSEQ
Task	Classification (2 classes)
Dataset	vicunaUC
Framework	PyTorch / PyTorch Lightning

Model Information

See the ToxicThesis repository for model class documentation.

Usage

from huggingface_hub import hf_hub_download
import torch

checkpoint_path = hf_hub_download(
    repo_id="simocorbo/toxicthesis-vicunauc-distillseq-classification-2",
    filename="checkpoints/best.pt"
)

checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=False)
print("Checkpoint keys:", checkpoint.keys())

# See ToxicThesis repository for model implementation
# git clone https://github.com/simo-corbo/ToxicThesis

Score Interpretation

Output	Range	Meaning
probability	[0, 1]	Probability of being toxic (class 1).
class	0 or 1	0 = non-toxic, 1 = toxic.

Decision boundary: Class 1 if probability >= 0.5.

Files

File	Description
checkpoints/best.pt	Model checkpoint (best validation loss)
hparams.yaml	Hyperparameters used for training
train.csv	Training metrics per epoch
val.csv	Validation metrics per epoch
vocab_stanza_hybrid.pkl	Vocabulary (for tree-based models)

Installation

# Clone ToxicThesis for full model implementations
git clone https://github.com/simo-corbo/ToxicThesis
cd ToxicThesis
pip install -r requirements.txt

# Or install dependencies directly
pip install torch transformers huggingface_hub fasttext-wheel stanza

Citation

@software{toxicthesis2025,
  title={ToxicThesis},
  author={Corbo, Simone},
  year={2025},
  url={https://github.com/simo-corbo/ToxicThesis}
}