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--- |
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language: en |
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tags: |
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- text-generation |
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- keras |
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- tensorflow |
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- lstm |
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- educational |
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license: other |
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datasets: |
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- epicurious-recipes-kaggle |
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--- |
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# LSTM Recipe Next-Token Generator |
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## Model Summary |
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This model is a next-token language model based on a Long Short-Term Memory (LSTM) network trained on the Epicurious Recipes dataset. It was developed as part of an academic assignment for **MSAI 630 – Generative AI with LLMs** and demonstrates classical sequence modeling for text generation prior to transformer-based architectures. |
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The model predicts the next word token given a sequence of previous tokens and can be used iteratively to generate long-form recipe-style text. |
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## Architecture |
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- Embedding size: 100 |
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- LSTM hidden units: 128 |
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- Vocabulary size: 10,000 |
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- Maximum sequence length: 200 tokens |
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- Output: Softmax over vocabulary (next-token prediction) |
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Framework: TensorFlow / Keras |
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## Training Details |
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- Dataset: Epicurious Recipes (Kaggle) |
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- Objective: Next-token prediction using sparse categorical cross-entropy |
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- Optimizer: Adam |
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- Epochs: 25 |
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- Batch size: 32 |
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- Random seed: 42 |
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Text preprocessing includes: |
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- Lowercasing |
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- Explicit punctuation token separation |
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- Integer tokenization via `TextVectorization` |
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The original dataset is **not redistributed** in this repository. |
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## Usage |
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This model is intended for educational and experimental purposes. |
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Typical usage: |
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1. Tokenize an input prompt using the same vocabulary |
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2. Feed the token sequence to the model |
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3. Sample the next token from the output distribution |
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4. Append the token and repeat |
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Temperature sampling can be applied to control randomness during generation. |
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## Limitations |
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- LSTM-based models have limited long-range coherence |
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- Outputs may become repetitive over long generations |
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- Not suitable for real-world recipe advice or food safety guidance |
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- Trained on a static dataset with no factual grounding |
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## Intended Use |
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- Educational demonstrations of sequence modeling |
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- Classical NLP comparison against transformer models |
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- Coursework and portfolio showcase |
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## Author |
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Zane Graper |
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