Build an Email Spam Detector | DistilBERT Fine-Tuning Tutorial in Google Colab (Step-by-Step) скачать в хорошем качестве

Build an Email Spam Detector | DistilBERT Fine-Tuning Tutorial in Google Colab (Step-by-Step)

In this tutorial, we build a complete Email Spam Detection system using DistilBERT and the Hugging Face Transformers library. This video covers the entire workflow including dataset loading, preprocessing, tokenization, model fine-tuning, evaluation, and testing with sample emails. You will learn: • How to load and clean a spam/ham dataset • How to tokenize text using DistilBERT • How to fine-tune a pretrained Transformer model • How to train the model using a GPU in Google Colab • How to calculate accuracy, precision, recall, and F1-score • How to save and load your trained model • How to test new email messages for spam detection This project is suitable for students, beginners in AI/ML, and anyone who wants to learn text classification using Python and Google Colab. Technologies Used: Python, Pandas, Scikit-Learn, Hugging Face Transformers, DistilBERT, PyTorch, Google Colab. Download the full source code here: https://smsram.dedyn.io/dashboard/sou... If you like the tutorial, consider subscribing for more machine learning and programming videos. YouTube Tags: #emailspamdetector #spamclassifier #distilbert #huggingface #transformers #nltutorial #machinelearning #textclassification #pythonproject #colabtutorial #nlp #spamham #machinelearningproject #studentsproject #pytorch #mltutorial #smsram #mehersivaram #aitutorial #finetuning