ULMfit: Universal Language Model Fine-Tuning for Text Classification

This repository contains an implementation of the ULMfit (Universal Language Model Fine-Tuning) approach. ULMfit is a powerful transfer learning method for natural language processing tasks, designed to enable efficient fine-tuning of language models for text classification or similar tasks.

Overview

The notebook provides:

• A detailed introduction to transfer learning in NLP.
• Step-by-step instructions to fine-tune a pretrained language model.
• Methods for evaluating and visualizing model performance.

Features

• Language Model Pretraining: Leverage a pretrained language model to reduce training time and data requirements.
• Fine-tuning: Adapt the model to your specific dataset.
• Classification: Train a classifier on top of the fine-tuned language model for downstream tasks.