BERT Fine Tuning Step by Step Hugging Face Classification Project Explained

In this video, we learn how to fine tune a BERT model on a real world dataset using Hugging Face Transformers. This is a complete end to end implementation for text classification using BERT. Here is the GitHub repo link https://github.com/switch2ai You can download all the code, scripts, and documents from the above GitHub repository. We use a real world complaint dataset and perform classification into multiple categories like Loan, Card, Services, Credit Report, and Others. Dataset Preparation We start by loading the dataset and selecting only relevant columns such as Product and Consumer complaint narrative. We clean the data by Converting text to lowercase Removing special characters Removing masked values Handling Imbalance The dataset is highly imbalanced, so we apply undersampling by selecting equal number of samples per class. This helps the model learn balanced patterns across all categories. Label Encoding We convert text labels into numeric values Loan → 0 Card → 1 Services → 2 Credit Report → 3 Others → 4 Hugging Face Dataset Format We convert pandas dataframe into Hugging Face dataset format Important requirement Target column must be named as labels Then we split data into train and test sets Model and Tokenizer We use pretrained BERT model checkpoint bert base cased Tokenizer Converts text into input ids and attention masks Model AutoModelForSequenceClassification Configured with number of labels equal to 5 Tokenization We tokenize text using Padding to fixed length Truncation for long sequences Max length set to 500 BERT supports up to 512 tokens Training Setup We define training arguments Number of epochs Output directory Model saving strategy Evaluation Metrics Accuracy Precision Recall F1 Score We use these metrics to evaluate model performance during training Trainer API We use Hugging Face Trainer to simplify training It handles Training loop Evaluation Metrics calculation Model Training We train the mo