from huggingface_hub import hf_hub_download
import torch

checkpoint_path = hf_hub_download(
    repo_id="simocorbo/toxicthesis-deepseek-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

bash

# 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

bibtex

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