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roberta-base

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RoBERTa-base fine-tuned on the LIAR dataset for binary fake-news detection — 64.5% accuracy, 0.635 macro-F1, beating a TF-IDF + LogReg baseline (0.603). Includes class-weighted training, calibration analysis (ECE 0.042), per-class error breakdown, threshold selection, ONNX export, and a live Spaces demo. Reproducible from one Colab notebook.

  • Updated Oct 22, 2026

This repository contains the code, model configurations, and report for fine-tuning a roberta-base model using Low-Rank Adaptation (LoRA)- a Parameter-Efficient Fine-Tuning (PEFT) method - on the AG News classification task. The goal was to achieve high test accuracy while keeping trainable parameters under 1 million.

  • Updated Apr 22, 2025
  • Jupyter Notebook

Python pipeline for a peer-reviewed JAAER (2025) study on pilot emotions: a recursive scraper collected 1,130,530 posts from Airline Pilot Central Forums, then GPU-accelerated RoBERTa (GoEmotions) scored each on 28 emotions to compare passenger, cargo, and military pilots.

  • Updated Sep 17, 2026
  • Python

This repository contains code for benchmarking ModernBERT, RoBERTa, and OPT-350m on multi-class emotion classification using 8-bit quantization, backbone freezing, and LoRA-based PEFT.

  • Updated Oct 19, 2026
  • Python

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