Ateeqq

/

news-title-generator

like 5

Here's a breakdown on how to create a news title generator using Hugging Face, Transformers (T5-base specifically), and the Ateeqq/news-title-generator dataset:

1. Libraries and Dataset:

• Install the required libraries:

  pip install transformers datasets
  

• Load the Ateeqq/news-title-generator dataset:

  from datasets import load_dataset
  
  dataset = load_dataset("Ateeqq/news-title-generator")
  

2. Preprocessing (Optional):

• The dataset might require some preprocessing depending on your preference. This could involve:

  • Lowercasing text
  • Removing punctuation
  • Tokenization (splitting text into words)

  Here's an example of basic preprocessing using Transformers tokenizers:

  from transformers import T5Tokenizer
  
  tokenizer = T5Tokenizer.from_pretrained("t5-base")
  
  def preprocess_function(examples):
      return tokenizer(examples["text"], padding="max_length", truncation=True)
  
  dataset = dataset.map(preprocess_function, batched=True)
  

3. Model Loading and Fine-tuning:

• Load the pre-trained T5-base model:

  from transformers import T5ForConditionalGeneration
  
  model = T5ForConditionalGeneration.from_pretrained("t5-base")
  

• Fine-tune the model on the prepared dataset. This involves training the model to specifically generate news titles based on the provided text. This can be a complex process, so be prepared to experiment with different hyperparameters and training configurations. Libraries like accelerate from Hugging Face can simplify this process.

4. Inference:

• Once the model is fine-tuned, you can use it to generate news titles:

  def generate_title(text):
      input_ids = tokenizer(text, return_tensors="pt")
      output = model.generate(**input_ids)
      return tokenizer.decode(output[0], skip_special_tokens=True)
  
  text = "Scientists discover a new planet with potential for life"
  title = generate_title(text)
  print(title)
  

Additional Resources:

• Hugging Face Transformers Library: https://huggingface.co/docs/transformers/en/index
• A news headline generator finetuned on T5-base (GitHub): https://github.com/topics/news-headline-generation (This code offers a basic implementation you can adapt)
• Fine-tuning with Transformers (Hugging Face): https://huggingface.co/docs/transformers/en/index

Note: Fine-tuning a model can be computationally expensive, so consider using a pre-trained model like the one provided in the GitHub repository mentioned above if you don't have the resources for extensive training.