DistilBERT Fine-Tuned for Sequence Classification

Model Overview

This is a fine-tuned version of the DistilBERT model designed for sequence classification tasks. It is inspired by the r/AmItheAsshole subreddit, where it has been trained on textual data to assess and classify user-submitted stories.

Base Model: DistilBERT
Fine-Tuned For: Sequence classification (e.g., sentiment analysis, AITA-type categorization)
Dataset: https://huggingface.co/datasets/MattBoraske/Reddit-AITA-2018-to-2022
Task: Sequence classification with predefined labels.

Model Details

Architecture: Transformer-based model (DistilBERT)
Input Format: Text sequences
Output Format: Classification labels with confidence scores
Labels:
- LABEL_0: The Asshole
- LABEL_1: Not the Asshole

Intended Use

This model is intended to provide insights and assessments for user-submitted textual scenarios. It works well for binary classification tasks.

Example Usage

from transformers import pipeline

classifier = pipeline(
    "text-classification", 
    model="your-username/your-model-name"
)

text = "I did not invite my friend for my wedding. AITA ?"
result = classifier(text)
print(result)