t5-news-title-creation

T5-Small for News Title Creation

This is a T5-Small model fine-tuned for generating concise and informative news titles from article summaries. It is useful for news agencies, content creators, and media professionals to generate titles efficiently.

Model Details

Model Type: Sequence-to-Sequence Transformer
Base Model: t5-small
Maximum Sequence Length: 128 tokens (input and output)
Output: News titles based on input summaries
Task: Title Creation

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

pip install -U transformers torch datasets

Load the Model and Run Inference

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 Title: {headline}")

Training Details

Training Dataset

Dataset Name: News Titles Dataset
Size: 30,000 rows
Columns: article_summary (input), title (output)

Approximate Statistics

article_summary:
Type: string
Min length: ~20 tokens
Mean length: ~50-60 tokens (estimated)
Max length: ~128 tokens
title:
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