https://github.com/drisskhattabi6/nlp-labs
This repository contains a collection of hands-on labs 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.
https://github.com/drisskhattabi6/nlp-labs
arabic-nlp bert chatbot fine-tuning gpt2 machine-learning mongodb neural-networks nlp nlp-pipeline rnn rule-based-nlp text-processing web-scraping word-embeddings
Last synced: about 2 months ago
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This repository contains a collection of hands-on labs 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.
- Host: GitHub
- URL: https://github.com/drisskhattabi6/nlp-labs
- Owner: drisskhattabi6
- Created: 2024-04-08T09:04:53.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-13T12:56:05.000Z (4 months ago)
- Last Synced: 2025-01-26T14:49:02.638Z (3 months ago)
- Topics: arabic-nlp, bert, chatbot, fine-tuning, gpt2, machine-learning, mongodb, neural-networks, nlp, nlp-pipeline, rnn, rule-based-nlp, text-processing, web-scraping, word-embeddings
- Language: Jupyter Notebook
- Homepage:
- Size: 5.49 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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.
---
## 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.
---
## 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.
---
## Tools and Technologies Used
- **Libraries:** `BeautifulSoup`, `NLTK`, `spaCy`, `gensim`, `Transformers`, `Keras`, `TensorFlow`, `PyTorch`
- **Languages:** Python
- **Applications:** Text analysis, sentiment prediction, regression, and classification
---
## How to Use
1. Clone this repository:
```bash
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.
---
If you have any questions or ideas to share, plz contact me.
Happy Coding! 🚀