README.md

# Model Card: T5 Email Response Generator
- **Model Details**
- **Model Name:** T5 Email Response Generator
- **Model Version:** 1.0
- **Base Model:** t5-base (Hugging Face Transformers)
- **Task:** Text generation for email response automation

# Model Description
This model is a fine-tuned version of the T5 (Text-to-Text Transfer Transformer) t5-base model, designed to generate concise and contextually appropriate email responses. It was trained on a custom dataset (email.csv) containing input prompts and corresponding email responses. The model supports both FP32 and FP16 precision, with the latter optimized for reduced memory usage on GPUs.

# Intended Use
- **Primary Use Case:** Automating email response generation for common queries (e.g., scheduling, confirmations, updates).
- **Target Users:** Individuals or organizations looking to streamline email communication.
- **Out of Scope:** Generating long-form content, handling highly sensitive or complex email threads requiring human judgment.
- 
# Model Architecture
-b**Base Model:** T5 (t5-base)
- **Parameters:** ~220M
- **Layers:** 12 encoder layers, 12 decoder layers
- **Hidden Size:** 768
- **Precision:** Available in FP32 (full precision) and FP16 (mixed precision)
# Training Details
- **Dataset**
- **Source:** Custom dataset (email.csv)
- **Format:** CSV with columns input (prompt) and output (response)
# Preprocessing:
- Added prefix "generate response: " to all inputs.
- Filtered out examples with None values, lengths > 100 characters, or containing "dataset" in the input.
- **Split:** 90% training, 10% validation.
# Training Procedure
- **Framework:** Hugging Face Transformers
- **Hardware:** GPU (e.g., NVIDIA with 12 GB memory)
# Training Arguments:
- **Epochs:** 30
- **Batch Size:** 4 (effective 8 with gradient accumulation)
- **Learning Rate:** 3e-4
- **Warmup Steps:** 10
- **Weight Decay:** 0.01
- **Optimizer:** AdamW
- **Mixed Precision:** FP16 enabled
- **Evaluation:** Performed at the end of each epoch, using validation loss as the metric for the best model.
# Tokenization
- **Tokenizer:** T5Tokenizer from t5-base
- **Max Length:** 128 tokens (input and output)
- **Padding:** Applied with max_length
- **Truncation:** Enabled for longer sequences
# Performance
- **Metrics:** Validation loss (best model selected based on lowest loss)
# Sample Outputs:
- **Input:** "Can you send me the report?"
- **Output:** I’ll send the report over this afternoon!
- **Input:** Write a follow-up email for our last discussion.
- **Output:** I’ll send a follow-up for you shortly.
- **Limitations:** Performance depends on the quality and diversity of email.csv. May struggle with prompts outside the training distribution.

# Installation

- pip install transformers datasets torch pandas accelerate -q
# Loading the Model
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load FP16 model
model = T5ForConditionalGeneration.from_pretrained("./t5_email_finetuned_fp16").to(device)
tokenizer = T5Tokenizer.from_pretrained("./t5_email_finetuned_fp16")

# Generate a response
def generate_response(prompt, max_length=128):
    input_text = f"generate response: {prompt}"
    inputs = tokenizer(input_text, max_length=128, truncation=True, padding="max_length", return_tensors="pt").to(device)
    outputs = model.generate(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], max_length=max_length, num_beams=4, early_stopping=True)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(generate_response("Can you send me the report?"))
```