https://github.com/cllspy/learnnlp
NLP with Python
https://github.com/cllspy/learnnlp
Last synced: 12 months ago
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NLP with Python
- Host: GitHub
- URL: https://github.com/cllspy/learnnlp
- Owner: CllsPy
- Created: 2024-11-27T15:28:48.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-08T00:46:55.000Z (over 1 year ago)
- Last Synced: 2025-02-07T19:15:51.682Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 61.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Natural Language Processing (NLP) Learning Repository
Welcome to this repository dedicated to learning and exploring Natural Language Processing (NLP). This repo serves as a collection of resources, projects, and experiments designed to help you understand and apply NLP concepts, algorithms, and techniques. Whether you're a beginner or looking to expand your knowledge, this repo provides the foundation and tools to guide you through the process of learning NLP.
## Book
## Problem Set
- chapter 03
- en
- [doc](https://github.com/CllsPy/Learn-NLP/tree/main/ch03/problem-set-0/doc)
## Introduction
Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) focused on the interaction between computers and human language. The goal of NLP is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful.
This repository contains various resources to help you learn NLP, including:
- Key algorithms and techniques
- Implementations of common NLP tasks
- Exploratory data analysis
- Preprocessing and feature extraction methods
- Evaluation metrics
## Key Concepts
The following are key concepts you will explore in this repository:
- **Text Preprocessing**: Techniques to clean and format text data, such as tokenization, stop-word removal, and stemming/lemmatization.
- **Word Embeddings**: Methods like Word2Vec, GloVe, and FastText for representing words in continuous vector spaces.
- **Language Models**: Understanding and implementing models like n-grams, RNNs, LSTMs, and transformers.
- **Named Entity Recognition (NER)**: Identifying and classifying entities (e.g., person, location, date) in text.
- **Sentiment Analysis**: Analyzing the sentiment of a given text, whether it’s positive, negative, or neutral.
- **Text Classification**: Assigning predefined labels to text (e.g., spam detection, topic categorization).
- **Sequence-to-Sequence Models**: Building models for machine translation, text summarization, and more.
## Projects and Examples
This repository includes a variety of hands-on projects and code examples. Some of the key projects include:
1. **Text Classification**: Implementing a model to classify text into categories like spam or non-spam.
2. **Sentiment Analysis**: Analyzing movie reviews or social media posts to determine their sentiment.
3. **Named Entity Recognition**: Extracting entities from text using both rule-based and machine learning-based approaches.
4. **Language Modeling**: Building an RNN-based or Transformer-based language model from scratch.
5. **Machine Translation**: Implementing a simple translation model using sequence-to-sequence techniques.
Check the `projects/` directory for all project-specific files and notebooks.
## Installation
To get started with this repository, clone it to your local machine and install the necessary dependencies.
1. Clone the repository:
```bash
git clone https://github.com/yourusername/nlp-learning.git
cd nlp-learning
```
2. Install the dependencies:
```bash
pip install -r requirements.txt
```
**Dependencies** may include libraries such as:
- `nltk` for basic NLP tasks
- `spacy` for advanced NLP operations
- `transformers` for transformer-based models
- `torch` and `tensorflow` for deep learning models
- `scikit-learn` for machine learning models
## Usage
Once the repository is cloned and dependencies are installed, you can start exploring the projects and examples. For example, to run a sentiment analysis example:
1. Navigate to the project folder:
```bash
cd projects/sentiment-analysis
```
2. Run the Jupyter Notebook or Python script to begin:
```bash
jupyter notebook sentiment_analysis.ipynb
```
Alternatively, you can run scripts directly from the command line:
```bash
python sentiment_analysis.py
```
Refer to the individual project README files for more specific instructions on each project.
## Contributing
We welcome contributions! If you'd like to contribute to this repository, please follow these steps:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature-name`).
3. Make your changes.
4. Commit your changes (`git commit -am 'Add new feature'`).
5. Push to your branch (`git push origin feature-name`).
6. Open a Pull Request.
Please ensure that your code follows the existing style and includes tests where applicable.
## Resources
Here are some valuable resources to help you on your NLP learning journey:
- [Stanford NLP Course](https://web.stanford.edu/class/cs224n/)
- [Deep Learning for NLP with PyTorch](https://pytorch.org/tutorials/beginner/nlp.html)
- [NLTK Documentation](https://www.nltk.org/)
- [SpaCy Documentation](https://spacy.io/)
- [The Illustrated Transformer](http://jalammar.github.io/illustrated-transformer/)
- [Machine Learning Mastery](https://machinelearningmastery.com/)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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Happy learning, and feel free to reach out with any questions or suggestions!