bhadresh-ft-enc

Fine-tuned version of bhadresh-savani/distilbert-base-uncased-emotion on a mix of clean and imperceptibly perturbed emotion classification data. This model is designed to improve robustness against character-level adversarial attacks while retaining high accuracy on clean text.

Model Description

• Base model: distilbert-base-uncased-emotion
• Fine-tuning data: vlwk/emotion-perturbed: lean and perturbed emotion classification inputs (perturbation types: homoglyphs, deletions, reorderings, invisible characters), perturbation budget 1 to 5.
• Training epochs: 3
• Batch size: 16
• Learning rate: 2e-5
• Validation split: 10%

Intended Use

This model is intended for robust emotion classification under adversarial character-level noise. It is particularly useful for evaluating or defending against imperceptible text perturbations.

Usage

from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification

tokenizer = DistilBertTokenizerFast.from_pretrained("vlwk/bhadresh-ft-enc")
model = DistilBertForSequenceClassification.from_pretrained("vlwk/bhadresh-ft-enc")

inputs = tokenizer("I'm feeling great today!", return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(-1).item()