Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/sukhmancs/textwizards

This repository houses a collection of my natural language processing (NLP) projects, showcasing a variety of applications and experiments in the field. From sentiment analysis to language modeling, explore the power of NLP through my code. Feel free to use, modify, and contribute!
https://github.com/sukhmancs/textwizards

char-rnn chat-bot-nltk classification data-visualization encoder-decoder-attention gpt gradient-descent math-question-solver n-gram-language-models naive-bayes-classifier nlp patient-survival-analysis regression-algorithms sentiment-analysis smallgpt spell-checker titanic-dataset translation trax word-embeddings

Last synced: about 12 hours ago
JSON representation

This repository houses a collection of my natural language processing (NLP) projects, showcasing a variety of applications and experiments in the field. From sentiment analysis to language modeling, explore the power of NLP through my code. Feel free to use, modify, and contribute!

Awesome Lists containing this project

README

        







LinkedIn Badge


Reddit Badge


Twitter Badge



🚀 NLP Projects Repository


This repository houses a collection of my natural language processing (NLP) projects, showcasing a variety of applications and experiments in the field. From sentiment analysis to language modeling, explore the power of NLP through my code. Feel free to use, modify, and contribute!

### 🔍 **Key Features:**
- Sentiment Analysis
- Named Entity Recognition
- Text Classification
- Language Modeling
- Encoder-Decoder Architecture with Attention
- Transformers
- Recurrent Neural Network
- Fine-tuning using HuggingFace
- Word Embeddings
- Machine Translation
- 🤖 Intelligent Conversational Chatbot

### 🌐 **Technologies Used:**
- Python
- TensorFlow
- PyTorch
- NLTK
- Trax
- Pandas
- Numpy

### 👨‍💻 **How to Use:**

To run any of the projects in Google Colab, follow these simple steps:

1. **Open in Colab:**
- Click on the "Open in Colab" at the top of any notebook.

2. **Set Up the Environment:**
- If required, follow the instructions within the Colab notebook to set up any necessary environment or dependencies.

3. **Run the Code:**
- Execute the code cells in the notebook one by one to observe the results or modify them as needed.

4. **Explore and Learn:**
- Feel free to experiment with the code, modify parameters, and gain hands-on experience with natural language processing.

**Note:** Make sure to check the license information before using or modifying the code.

Happy coding! 🚀

### 🤝 **Contributions:**

Contributions, bug reports, and feature requests are welcome! Feel free to fork and submit pull requests.

### 📚 **Resources:**

#### Books

- [Deep Learning with Pytorch Step-by-Step](https://pytorchstepbystep.com/) by Daniel Voigt Godoy.
- A comprehensive guide to understanding and implementing NLP techniques in real-world applications. Covers key concepts, algorithms, and practical examples.

### 📋 **License:**

This repository is licensed under the MIT License - see the [MIT License](./LICENSE) file for details.