Text Classification with BERT in PyTorch

       Text classification is a fundamental task in NLP that involves categorizing text into predefined categories or labels. With the advent of deep learning and transformer-based models like BERT (Bidirectional Encoder Representations from Transformers), text classification has witnessed significant advancements in accuracy and performance. 

    In this tutorial, we will explore how to perform text classification using BERT in PyTorch, covering data preparation, model training, and prediction. The tutorial covers:

  1. Preparing data for text classification
  2. Overview of BERT
  3. Tokeniziation and encoding
  4. Loading the pre-trained BERT model
  5. Training (fine-tuning) the model
  6. Making predictions on new sentences
  7. Conclusion 
  8. Source code listing

     Let's get started.

Sentiment Analysis with BERT in PyTorch

      Sentiment analysis involves determining the sentiment (positive, negative, or neutral) expressed in a piece of text, making it a valuable tool for understanding user opinions, customer feedback, and social media sentiment. In this tutorial, we'll explore how to perform sentiment analysis using BERT (Bidirectional Encoder Representations from Transformers), one of the most powerful models in NLP.

  1. Loading the Pre-trained BERT Model
  2. Performing Sentiment Analysis
  3. Conclusion 
  4. Source code listing

     Let's get started.

Understanding Transformers and How To Use BERT

     The introduction of the transformer architecture in the 2017 paper "Attention Is All You Need" by Vaswani et al. brought transformative changes to natural language processing (NLP). Transformers have become a cornerstone in many cutting-edge machine learning models, including large language models like ChatGPT, BERT, and LLAMA. These models have demonstrated remarkable performance in various NLP tasks, marking a significant advancement in the field. In this blog post, we'll explore the core concepts behind transformers and learn how to use them. The tutorial covers:

  1. Introduction to transformers
  2. Key Components of Transformers
  3. Practical Usage of Transformers
  4. Conclusion

     Let's get started.

Text Classification Example with SpaCy and Scikit-Learn

     Text classification is a fundamental task in natural language processing (NLP) that involves categorizing text into predefined categories or labels. In this blog post, we will explore how to perform text classification using the SpaCy library for text preprocessing and the Scikit-Learn library for building a machine learning classifier. The tutorial covers:

  1. Preparing data
  2. Feature extraction with TF-IDF
  3. Building a text classifier
  4. Evaluating the model and prediction
  5. Conclusion

     Let's get started.

Understanding Document Ranking in NLP

    Document ranking is used in information retrieval, helping users in finding the most relevant content based on their queries. In this blog post, we'll explore the fundamentals of document ranking and implement a simple yet effective example using scikit-learn.

  The tutorial covers:

  1. Understanding document ranking
  2. Methods for document ranking
  3. Document ranking example with scikit-learn
  4. Conclusion

     Let's get started.

Understanding Syntactic Trees with Spacy

     Understanding the structure of language is a fundamental step in the realm of Natural Language Processing (NLP). Syntactic trees play a pivotal role in unraveling the intricate web of grammatical relationships within sentences. In this blog post, we'll delve into the concept of syntactic trees, exploring their significance and providing a hands-on example using SpaCy.

  The tutorial covers:

  1. The concept of Syntactic Trees
  2. The importance of Syntactic Trees in NLP
  3. Generating Syntactic Tree with SpaCy
  4. Conclusion

     Let's get started.