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| license: mit |
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| ## license: mit |
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| # VerseFormer |
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| VerseFormer is a GPT-style transformer language model implemented in PyTorch and trained on Shakespeare's literary works. |
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| The project was developed to explore transformer architectures, self-attention mechanisms, token embeddings, and autoregressive text generation through hands-on implementation and experimentation. |
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| ## Overview |
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| VerseFormer learns patterns in Shakespearean text and generates text by predicting the next token in a sequence. |
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| ## Features |
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| * GPT-style transformer architecture |
| * Implemented using PyTorch |
| * Autoregressive next-token prediction |
| * Shakespeare-trained language model |
| * Text generation capability |
| * Custom training pipeline |
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| ## Architecture |
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| The model includes: |
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| * Token Embeddings |
| * Positional Embeddings |
| * Multi-Head Self-Attention |
| * Feed-Forward Networks |
| * Layer Normalization |
| * Residual Connections |
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| ## Training Data |
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| The model was trained on Shakespeare's plays, sonnets, and poems to learn language structure, context, and literary style. |
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| ## Technologies Used |
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| * Python |
| * PyTorch |
| * NumPy |
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| ## Learning Outcomes |
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| This project helped develop practical understanding of: |
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| * Transformer architectures |
| * Attention mechanisms |
| * Language modeling |
| * Deep learning workflows |
| * Model training and experimentation |
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| ## Example |
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| Input: |
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| To be, or not to be |
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| Possible Output: |
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| To be, or not to be, that is the question whether... |
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| *Generated output may vary depending on training configuration.* |
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| ## Author |
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| **Anurag Verma** |
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| * GitHub: https://github.com/anuragverma81 |
| * LinkedIn: https://www.linkedin.com/in/anurag-verma-8943162b5 |
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| ## License |
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| MIT License |
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