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simocorbo/toxicthesis-gemini-3.5-flash-tree-classification-5

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TreeLSTM - GEMINI-3.5-FLASH - Classification (5 classes)

Toxicity prediction model trained on the GEMINI-3.5-FLASH dataset.

Property	Value
Model	TreeLSTM
Task	Classification (5 classes)
Dataset	gemini-3.5-flash
Framework	PyTorch / PyTorch Lightning

Class: `TreeLSTMModel`

python

from src.models.lstm import TreeLSTMModel

model = TreeLSTMModel(
    vocab: dict,                  # Token-to-index mapping
    hidden_dim: int = 256,        # Hidden dimension
    num_classes: int = 1,         # 1=regression, 2+=classification
    dropout: float = 0.3,
    lr: float = 0.001,
    loss_fn: str = 'auto'
)

Methods

Method	Description
`forward(batch)`	Process batch of tree graphs. Returns logits.
`compute_loss(logits, targets)`	Computes loss based on num_classes.
`load_from_checkpoint(path)`	Load model from checkpoint.

Tree Input Format

The model expects constituency trees converted to a PyTorch Geometric batch with:

—x: Node features (token embeddings)
—edge_index: Parent-child edges
—batch: Batch assignment for each node

Usage with ToxicThesis (Recommended)

python

# 1. Clone ToxicThesis repository
# git clone https://github.com/simo-corbo/ToxicThesis
# cd ToxicThesis && pip install -r requirements.txt

from huggingface_hub import snapshot_download
import torch
import pickle

# 2. Download model files
model_dir = snapshot_download(
    repo_id="simocorbo/toxicthesis-gemini-3.5-flash-tree-classification-5",
    allow_patterns=["checkpoints/*", "*.pkl"]
)

# 3. Load vocabulary
with open(f"{model_dir}/vocab_stanza_hybrid.pkl", 'rb') as f:
    vocab = pickle.load(f)

# 4. Import and load model from ToxicThesis
from src.models.lstm import TreeLSTMModel

checkpoint = torch.load(f"{model_dir}/checkpoints/best.pt", map_location='cpu')
hparams = checkpoint.get('hyper_parameters', {})

model = TreeLSTMModel(
    vocab=vocab,
    hidden_dim=hparams.get('hidden_dim', 256),
    num_classes=hparams.get('num_classes', 5)
)
model.load_state_dict(checkpoint.get('state_dict', checkpoint), strict=False)
model.eval()

# 5. For inference, use the preprocessing pipeline to convert text to tree format
from src.preprocessing.tree_utils import text_to_tree_batch

# Parse and convert text to tree batch
tree_batch = text_to_tree_batch("Your text here", vocab)

with torch.no_grad():
    logits = model(tree_batch)
    if model.num_classes == 1:
        score = torch.sigmoid(logits).item()
        print(f"Score: {score}")
    else:
        probs = torch.softmax(logits, dim=-1)
        print(f"Probabilities: {probs}")

Note on Standalone Usage

TreeLSTM requires constituency parsing and tree-to-graph conversion. For standalone usage, you would need to implement the tree preprocessing pipeline. We recommend using ToxicThesis directly.

Score Interpretation

Output	Range	Meaning
`probabilities`	List[float]	Probability distribution over 5 classes.
`class`	0 to 4	Predicted class (argmax of probabilities).

Classes: 5 toxicity levels, where higher class index = more toxic.

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}
}