Create README.md

README.md

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+# 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. 🚀
+