In this video, we will be providing a beginner's guide to fine-tuning BERT, one of the most powerful natural language processing (NLP) models available. BERT, which stands for "Bidirectional Encoder Representations from Transformers," has been trained on a massive amount of text data and can be fine-tuned for a variety of NLP tasks, such as text classification, machine translation, and named entity recognition. We'll be walking through the process of fine-tuning BERT using the Hugging Face library, and provide examples and code snippets to help you get started with your own fine-tuning projects. This video is perfect for anyone who is new to NLP and wants to learn more about BERT and how to use it in their own projects. So, if you are a beginner in the field of NLP and want to learn more about BERT, this video is for you!

Source Code:
Kaggle Notebook: https://bit.ly/3GTQklf

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Timestamps:
00:00 Introduction
00:33 Loading BERT from HuggingFace 🤗
01:52 Loading Tokenizer from HuggingFace 🤗
03:42 Output of BERT 👀 (Understanding Encoder Representations)
05:50 Loading the Dataset
07:22 Building the Model (BERT for Classification)
09:18 Fine-Tuning/Training BERT
09:47 Evaluating BERT (92% Yeyy!!)
10:05 So what are you fine-tuning next? 👀
10:22 Outro. See you soon! 👋

Tags:
#BERT #FineTuning #NLP #MachineLearning #BeginnersGuide #DeepLearning #NaturalLanguageProcessing #Tutorial #howto #BERTModel #TextClassification #LanguageModeling #TransferLearning #NeuralNetworks.

Keywords:
BERT, Fine-Tuning, NLP, Machine Learning, Beginners Guide, Deep Learning, Natural Language Processing, BERT Model, Text Classification, Pre-trained Models, Language Modeling, Transfer Learning, Neural Networks, Fine-Tune BERT, Fine-Tuning BERT

Thank you,
Pritish Mishra