Update README.md

README.md

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 # Training Procedure
 **Framework:** Hugging Face Transformers
 **Hyperparameters:**
-Epochs: 1 (subset training; full training planned for 3 epochs)
-Effective Batch Size: 32 (8 per device, 4 gradient accumulation steps)
-Learning Rate: 2e-5
-Optimizer: AdamW (default in Trainer)
-Mixed Precision: FP16 (fp16=True)
-Training Time: ~2.3 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
-bash
-
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+- 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
+```python
 pip install transformers torch datasets
-Inference Example
-python
+```
+# Inference Example
 
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+```python
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 import torch
 
@@ -88,22 +78,4 @@ with torch.no_grad():
     output_ids = model.generate(input_ids, max_length=256, num_beams=4, early_stopping=True, no_repeat_ngram_size=2)
 directions = tokenizer.decode(output_ids[0], skip_special_tokens=True)
 print(directions)
-Saved Model
-Location: ./t5_recipe_finetuned_fp16
-Size: ~425 MB (FP16 weights)
-Limitations and Biases
-Data Quality: Some RecipeNLG entries have mismatched ingredients and directions, potentially leading to nonsensical outputs.
-Scope: Trained only on English recipes; may not handle non-English inputs or exotic cuisines well.
-Bias: Reflects biases in RecipeNLG (e.g., Western cuisine dominance).
-Quantization: FP16 may introduce minor numerical differences vs. FP32, though mitigated by FP16 training.
-Ethical Considerations
-Use: Should not be used to replace professional culinary expertise without validation.
-Safety: Generated directions aren’t guaranteed to be safe or accurate (e.g., cooking times, temperatures).
-Contact
-Author: [Your Name/Group Name]
-Support: [Your Email/GitHub, if applicable]
-Citation
-If you use this model, please cite:
-
-RecipeNLG dataset: [Add citation if available]
-T5: Raffel et al., "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" (2020)
+```