README.md

# T5-Small for News Headline Generation
This is a T5-Small model fine-tuned for generating concise and informative news topics from content summaries. It is useful for news agencies, content creators, and media professionals to generate headlines efficiently.

# Model Details
**Model Type:** Sequence-to-Sequence Transformer  
**Base Model:** t5-small  
**Maximum Sequence Length:** 128 tokens (input and output)  
**Output:** News headlines based on input summaries  
**Task:** Text Summarization (Headline Generation)

# Model Sources
**Documentation:** T5 Model Documentation  
**Repository:** Hugging Face Model Hub  
**Hugging Face Model:** Available on Hugging Face

# Full Model Architecture
```
T5ForConditionalGeneration(
  (shared): Embedding(32128, 512)
  (encoder): T5Stack(
    (embed_tokens): Embedding(32128, 512)
    (block): ModuleList(...)
    (final_layer_norm): LayerNorm((512,), eps=1e-12)
    (dropout): Dropout(p=0.1)
  )
  (decoder): T5Stack(
    (embed_tokens): Embedding(32128, 512)
    (block): ModuleList(...)
    (final_layer_norm): LayerNorm((512,), eps=1e-12)
    (dropout): Dropout(p=0.1)
  )
  (lm_head): Linear(in_features=512, out_features=32128, bias=False)
)
```

# Installation and Setup
```bash
pip install -U transformers torch datasets
```

# Load the Model and Run Inference
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch

# Model Name
model_name = "your_fine_tuned_model_id"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

# Move model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Inference
news_summary = "Ministry of Education has announced a major reform in the national curriculum to enhance digital literacy among students."
inputs = tokenizer(news_summary, 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=20,
    num_beams=5,
    early_stopping=True
)
headline = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"Generated Headline: {headline}")
```

# Training Details
### Training Dataset
**Dataset Name:** News Headlines Dataset  
**Size:** 30,000 rows  
**Columns:** article_summary (input), headline (output)  

# Approximate Statistics
```
article_summary:
Type: string
Min length: ~20 tokens
Mean length: ~50-60 tokens (estimated)
Max length: ~128 tokens
headline:
Type: string
Min length: ~5 tokens
Mean length: ~10-15 tokens
Max length: ~20 tokens
```

# Training Hyperparameters
- **per_device_train_batch_size:** 8
- **per_device_eval_batch_size:** 8
- **gradient_accumulation_steps:** 2 
- **num_train_epochs:** 4
- **learning_rate:** 5e-5
- **fp16:** True

This model is optimized for **content topic generation**, ensuring concise, accurate, and informative outputs. 🚀