{"id":26451433,"url":"https://github.com/venkat-0706/autotext-gpt","last_synced_at":"2025-10-25T18:38:21.400Z","repository":{"id":283105371,"uuid":"950700165","full_name":"venkat-0706/AutoText-GPT","owner":"venkat-0706","description":"A Next-Word Prediction project uses Transformers and GPT-2 for text generation. GPTTokenizer preprocesses input, and the model is fine-tuned. 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The model processes text data, constructs n-grams (bigrams and trigrams), and predicts the most probable next word based on context.\n\n## Features\n- Tokenizes sentences into words\n- Generates bigrams and trigrams\n- Predicts the next word using probability distributions\n- Implements machine learning techniques for text prediction\n\n## Requirements\nEnsure you have the following dependencies installed:\n\n```bash\npip install nltk numpy pandas\n```\n\n## Step-by-Step Process\n\n### 1. Basic Setup\n- Import necessary libraries such as `nltk`, `numpy`, and `pandas`.\n- Load the dataset containing textual data.\n\n### 2. Loading the Dataset\n- The dataset is structured as a list of sentences, where each sentence is a list of words.\n\n### 3. Creating N-grams\n- Generate **unigrams, bigrams, and trigrams** from the dataset.\n  \n  Example:\n  - **Sentence**: \"This is a Data Science Course\"\n  - **Bigrams**: \"This is\", \"is a\", \"a Data\", \"Data Science\", \"Science Course\"\n  - **Trigrams**: \"This is a\", \"is a Data\", \"a Data Science\", \"Data Science Course\"\n\n### 4. Building the Prediction Model\n- Use probability distributions to analyze n-grams and predict the most likely next word.\n\n### 5. Model Evaluation\n- Evaluate the model based on accuracy, perplexity, and fluency.\n\n## How to Run the Project\n1. Clone this repository:\n   ```bash\n   git clone \u003crepository_url\u003e\n   cd Next-Word-Prediction\n   ```\n2. Install dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n3. Run the Jupyter Notebook:\n   ```bash\n   jupyter notebook Next_Word_Prediction.ipynb\n   ```\n\n## Future Enhancements\n- Integrate **Transformers** (e.g., GPT-2) for more advanced predictions.\n- Implement **GPTTokenizer** for better text preprocessing.\n- Improve accuracy using deep learning techniques.\n\n## Contributing\nFeel free to fork this repository and improve the model. Contributions are welcome!\n\n## License\nThis project is licensed under the MIT License.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvenkat-0706%2Fautotext-gpt","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvenkat-0706%2Fautotext-gpt","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvenkat-0706%2Fautotext-gpt/lists"}