Model Card: T5-Base Fine-Tuned for Recipe Direction Generation (FP16)

• Model Name: t5-base-recipe-finetuned-fp16
• Model Type: Sequence-to-Sequence Transformer
• Base Model: google/t5-base (220M parameters)
• Quantization: FP16 (half-precision floating-point)
• Task: Generate cooking directions from a list of ingredients

• This model is designed to generate step-by-step cooking directions given a list of ingredients. It’s intended for:
• Recipe creation assistance.
• Educational purposes in culinary AI research.
• Exploration of text-to-text generation in domain-specific tasks.
• Primary Users: Home cooks, recipe developers, AI researchers.

Model Details

• Architecture: T5 (Text-to-Text Transfer Transformer), encoder-decoder Transformer with 12 layers, 768 hidden size, 12 attention heads.
• Input: Text string in the format "generate recipe directions from ingredients: ...".
• Output: Text string containing cooking directions.
• Quantization: Converted to FP16 for reduced memory usage (~425 MB vs. ~850 MB in FP32) and faster inference on GPU.
• Hardware: Fine-tuned and tested on a 12 GB NVIDIA GPU with CUDA.

Training Data

• Dataset: RecipeNLG
• Source: Publicly available recipe dataset (downloaded as CSV)
• Size: 2,231,142 examples (original); subset of 178,491 used for training (10% of train split)

Splits:

• Train: 178,491 examples (subset)
• Validation: 223,114 examples
• Test: 223,115 examples
• Attributes: ingredients (list of ingredients), directions (list of steps)
• Preprocessing: Converted stringified lists to text; input prefixed with "generate recipe directions from ingredients: ".

Training Procedure

• Epochs: 2
• Effective Batch Size: 32 (8 per device, 4 gradient accumulation steps)
• Learning Rate: 2e-5
• Optimizer: AdamW
• Mixed Precision: FP16 (fp16=True)
• Training Time: ~12 hours estimated for subset (1 epoch); full dataset (3 epochs) estimated at ~68 hours per epoch without optimization.
• Compute: Single 12 GB GPU (NVIDIA, CUDA-enabled).

Evaluation

• Metrics: Loss (to be filled post-training)
• Validation Loss: [TBD after training]
• Test Loss: [TBD after evaluation]
• Method: Evaluated using Trainer.evaluate() on validation and test splits.
• Qualitative: Generated directions checked for coherence with input ingredients (e.g., chicken and rice input should yield relevant steps).

Performance

• Results: [TBD; e.g., "Validation Loss: X.XX, Test Loss: Y.YY after 1 epoch on subset"]
• Strengths: Expected to generate plausible directions for common ingredient combinations.

Limitations:

• Limited training on subset may reduce generalization.
• Sporadic data mismatches may affect output quality.
• FP16 quantization might slightly alter precision vs. FP32.

Usage

Installation

Inference Example