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https://github.com/vidhi1290/text-classification-with-transformers-roberta-and-xlnet-model
This project explores advanced NLP techniques using RoBERTa and XLNet models to classify human emotions from textual data. By leveraging robust training and optimization methods, it aims to enhance emotion detection accuracy in applications like social media analysis and customer support.
https://github.com/vidhi1290/text-classification-with-transformers-roberta-and-xlnet-model
autoencoder bert ktrain matplotlib natural-language-processing nlp numpy pandas python roberta seaborn sklearn tensorflow transformers xlnet
Last synced: 13 days ago
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This project explores advanced NLP techniques using RoBERTa and XLNet models to classify human emotions from textual data. By leveraging robust training and optimization methods, it aims to enhance emotion detection accuracy in applications like social media analysis and customer support.
- Host: GitHub
- URL: https://github.com/vidhi1290/text-classification-with-transformers-roberta-and-xlnet-model
- Owner: Vidhi1290
- Created: 2024-07-23T13:00:00.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2024-07-24T13:33:21.000Z (4 months ago)
- Last Synced: 2024-10-10T08:20:36.996Z (about 1 month ago)
- Topics: autoencoder, bert, ktrain, matplotlib, natural-language-processing, nlp, numpy, pandas, python, roberta, seaborn, sklearn, tensorflow, transformers, xlnet
- Language: Jupyter Notebook
- Homepage:
- Size: 660 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📚 Transformers in NLP: RoBERTa and XLNet
## 🎯 Business Objective
In Part 1 of our Transformer series - Multi-Class Text Classification with Deep Learning using BERT, we explored the evolution of NLP models, from simpler models like Bag of Words (BOW) and TF-IDF to advanced Transformer architectures like BERT.
In Part 2, we dive into two novel architectures that enhance BERT's performance through innovative training and optimization techniques:
- **RoBERTa**: A Robustly Optimized BERT Pretraining Approach
- **XLNet**: Generalized Autoregressive Pretraining for Language Understanding
We'll analyze these models, explore their training methods, and use them to classify human emotions from text data.
## 📄 Data Description
We use the **Emotion** dataset from the Hugging Face library, which consists of English Twitter messages labeled with six basic emotions: anger, fear, joy, love, sadness, and surprise.
### Dataset Breakdown:
- **Train**: 16,000 rows
- **Validation**: 2,000 rows
- **Test**: 2,000 rows
### Labels:
- 0: sadness
- 1: joy
- 2: love
- 3: anger
- 4: fear
- 5: surprise
## 🛠️ Tech Stack
- **Language**: Python
- **Libraries**: `datasets`, `numpy`, `pandas`, `matplotlib`, `seaborn`, `ktrain`, `transformers`, `tensorflow`, `sklearn`
- **Environment**: Jupyter Notebook, Google Colab Pro (Recommended)
## 🚀 Approach
1. **Install Libraries**: Ensure all necessary libraries are installed.
2. **Load Dataset**: Load and explore the Emotion dataset.
3. **Data Preprocessing**: Convert datasets to DataFrame and create additional features.
4. **Data Visualization**: Use histograms to visualize data distribution.
5. **Model Training**:
- **RoBERTa**:
- Create and configure the model.
- Preprocess data, compile the model, and find optimal learning rates.
- Fine-tune the model and evaluate its performance.
- Save and test the model.
- **XLNet**:
- Similar steps as RoBERTa with additional understanding of Autoregressive and Autoencoder models.
6. **Performance Evaluation**: Evaluate both models on test data and compare their metrics.
## 📂 Project Structure
### Modular Code
- **src**: Contains modularized code for the entire project.
- **Engine.py**: Main script to run the project.
- **ML_Pipeline**: Folder with functions for data processing and model training.
- **output**: Contains trained models for easy loading and reuse.
- **lib**: Contains Jupyter notebooks and reference materials.
## 📝 Project Takeaways
1. Understand business problems in NLP.
2. Explore Transformer architectures and self-attention mechanisms.
3. Gain insights into RoBERTa and XLNet models.
4. Learn data preprocessing and visualization techniques.
5. Develop and fine-tune Transformer models.
6. Compare and evaluate model performances.
## 📦 Setup Instructions
### Prerequisites
- Ensure `git` is installed on your machine.
### Installation
1. **Clone the repo**
```sh
git clone https://github.com/Vidhi1290/Text-Classification-with-Transformers-RoBERTa-and-XLNet-Model.git
```
2. **Navigate to the project directory**
```sh
cd Text-Classification-with-Transformers-RoBERTa-and-XLNet-Model
```
3. **Install dependencies**
```sh
pip install -r modular_code/requirements.txt
```
4. **Run the project**
```sh
python modular_code/src/Engine.py
```
## Follow me on:
Follow me on:
- **[LinkedIn](https://www.linkedin.com/in/vidhi-waghela-434663198/)**
- **[Kaggle](https://www.kaggle.com/vidhikishorwaghela)**
- **[Medium](https://medium.com/@datasciencemeetscybersecurity)**
- **[GitHub](https://github.com/Vidhi1290)**
Feel free to reach out for any questions or collaboration opportunities!