Fine-Tune BERT for Multi-Class Sentiment Classification with Twitter Data | Python & Streamlit
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In this video, we’ll walk through fine-tuning BERT for multi-class sentiment classification using Twitter data. The process is broken down step by step to ensure you understand how to handle large-scale sentiment classification efficiently using Hugging Face’s BERT model.
Here’s what you’ll learn:
Introduction to BERT for Classification:
We’ll recap the basics of transformers and learn how to add a classification head to BERT.
Twitter Data Analysis:
We’ll load and analyze a Twitter dataset to set the groundwork for model building.
Data Preparation:
Learn how to prepare your dataset in a format that Hugging Face models can use.
Tokenization & Model Configurations:
We’ll go deep into tokenization and set up model configurations for training.
Model Training & Fine-Tuning:
Set up training arguments, compute metrics, and fine-tune the BERT model for optimal results.
Prediction with Fine-Tuned Model:
Once fine-tuning is complete, we’ll show how to use the model for prediction tasks.
Streamlit App for Predictions:
Finally, we’ll build a Streamlit application to deploy your fine-tuned model and make real-time predictions.
This video is packed with useful information to help you get started with fine-tuning BERT for sentiment analysis and deploying it using a Streamlit app!
#MachineLearning #BERT #SentimentAnalysis #Streamlit #NLP #TwitterData #FineTuningBERT
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