Create README.md

README.md

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+# 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?"))
+```