| # 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](https://huggingface.co/distilbert-base-uncased) | |
| - **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 | |
| ```python | |
| 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) | |