NLP Labs

Welcome to the NLP Labs repository! This repository contains a collection of hands-on projects and experiments from my Natural Language Processing (NLP) module. Each lab focuses on a specific aspect of NLP, ranging from text preprocessing and rule-based methods to advanced deep learning techniques like RNNs, LSTMs, and Transformers.

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Labs Overview

1. Scraping and NLP Pipeline for Arabic Web Sources

This lab demonstrates:

• Web scraping techniques for Arabic web sources using libraries like BeautifulSoup and Requests.
• Preprocessing Arabic text, including tokenization, stemming, lemmatization, and stopword removal.
• Building an end-to-end NLP pipeline tailored for Arabic text analysis.

2. Rule-Based NLP, Regex, and Word Embedding

This lab focuses on:

• Creating rule-based NLP systems for text analysis and pattern matching using Regex.
• Extracting meaningful information from structured and semi-structured data.
• Utilizing word embeddings like Word2Vec and GloVe for semantic understanding and vectorization of text.

3. Language Modeling for Regression & Classification

This lab involves:

• Developing language models for predicting numeric scores (regression tasks).
• Implementing classification models for text data, such as spam detection or sentiment analysis.
• Leveraging machine learning algorithms like Logistic Regression, SVMs, or Random Forest with text features.

4. Advanced NLP Techniques with RNN, GRU, LSTM, and Transformers

This comprehensive lab explores advanced NLP techniques:

• Predicting text scores using Recurrent Neural Networks (RNNs), Bidirectional RNNs, GRUs, and LSTMs.
• Fine-tuning and generating text with Transformers, specifically leveraging GPT-2.
• Fine-tuning BERT to predict sentiment and enhance text classification accuracy.

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Key Features

• Comprehensive Approach: Covers foundational NLP techniques, advanced deep learning methods, and practical applications.
• Multilingual Focus: Includes specialized pipelines for Arabic text processing.
• State-of-the-Art Models: Utilizes modern architectures like GPT-2 and BERT for superior NLP performance.

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Tools and Technologies Used

• Libraries: BeautifulSoup, NLTK, spaCy, gensim, Transformers, Keras, TensorFlow, PyTorch
• Languages: Python
• Applications: Text analysis, sentiment prediction, regression, and classification

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How to Use

1. Clone this repository:

   git clone https://github.com/drisskhattabi6/NLP-Labs.git

2. Navigate to the desired lab folder.
3. Follow the README or Jupyter Notebook instructions to explore and execute the code.

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If you have any questions or ideas to share, plz contact me.

Happy Coding! 🚀