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Introduction

Notebook setup

Data exploration

Data preprocessing - tokenization, padding & attention mask

Choosing maximum sequence length

Create PyTorch dataset

Splitting the data into train, validation, and test sets

Creating data loaders

Description:

Dive into a comprehensive tutorial on text preprocessing for sentiment analysis using BERT, Hugging Face, PyTorch, and Python. Explore data preprocessing techniques, including tokenization with BertTokenizer, adding special tokens, padding sequences to fixed lengths, and creating attention masks. Learn to set up a notebook, explore data, choose optimal sequence lengths, create PyTorch datasets, split data into train/validation/test sets, and set up data loaders. Gain practical insights into natural language processing and machine learning workflows for sentiment analysis tasks.

Sentiment Analysis with BERT Using Huggingface, PyTorch and Python Tutorial

Venelin Valkov

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#Computer Science #Artificial Intelligence #Natural Language Processing (NLP) #Sentiment Analysis #Deep Learning #PyTorch #LLM (Large Language Model) #BERT

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