A GPTNeoForSequenceClassification head fine-tuned on the Jigsaw Toxic Comment Classification corpus. It outputs a continuous P(toxic) ∈ [0, 1] for a piece of English text (softmax probability of the toxic class, label index 1).

The model is used as an external violation scorer (a CDD-style surrogate) in research on guided discrete diffusion for controllable text generation: at evaluation time it scores decoded samples, and it also provides the soft labels used to train the per-timestep guidance classifiers.

Intended use

Primary: reward / violation scoring for controllable generation experiments.
Input: a raw English string. Output: softmax(logits)[1] = P(toxic).
Not intended as a production content-moderation system.

python

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

repo = "TheSky0908/gpt-neo-1.3B-toxicity-surrogate"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSequenceClassification.from_pretrained(repo).eval()

text = "I hope you have a wonderful day."
enc = tok(text, return_tensors="pt", truncation=True, max_length=256)
with torch.no_grad():
    p_toxic = torch.softmax(model(**enc).logits, dim=-1)[0, 1].item()
print(f"P(toxic) = {p_toxic:.4f}")

Training data

Arsive/toxicity_classification_jigsaw. The six Jigsaw sub-labels (toxic, severe_toxic, obscene, threat, insult, identity_hate) are consolidated into a single binary target: label 1 if any sub-label is positive, else 0.

Training procedure

Base model: EleutherAI/gpt-neo-1.3B, 2-way sequence-classification head.
Objective: cross-entropy on the binarized toxic label.
Optimizer: AdamW, lr 1e-5, weight decay 0.01, linear schedule with 6% warmup, grad-clip 1.0.
Batch size: 16 · Max length: 256 tokens · Epochs: 3 (~4.9k steps).
Precision: bf16 autocast, fp32 weights · gradient checkpointing enabled.
Best checkpoint selected by validation F1 (early stopping on the full val set).

Evaluation (Jigsaw validation split)

Metric	Value
F1	0.9885
Accuracy	0.9886
ROC-AUC	0.9986
Precision	0.9855
Recall	0.9916

Limitations & bias

Trained only on Jigsaw English comments, so it inherits that corpus's annotation biases (e.g. sensitivity to identity terms) and does not transfer to other languages or domains. High held-out F1 reflects in-distribution Jigsaw data, not real-world moderation performance. Use as a research surrogate signal, not as a safety-critical filter.