readme: ---
  license: mit
  language:
  - en
  tags:
  - summarization
  - headline-generation
  - text-generation
  datasets:
  - JulesBelveze/tldr_news
  metrics:
  - rouge1
  - rouge2
  - rougeL
  - rougeLsum
  
  ---
  
  # t5-small for headline generation
  
  This model is a [t5-small](https://huggingface.co/t5-small) fine-tuned for headline generation using
  the [JulesBelveze/tldr_news](https://huggingface.co/datasets/JulesBelveze/tldr_news) dataset.
  
  ## Using this model
  ```python
  import re
  from transformers import AutoTokenizer, T5ForConditionalGeneration
  
  WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
  
  article_text = """US FCC commissioner Brendan Carr has asked Apple and Google to remove TikTok from their app stores. The video app is owned by Chinese company ByteDance. Carr claims that TikTok functions as a surveillance tool that harvests extensive amounts of personal and sensitive data from US citizens. TikTok says its data access approval process is overseen by a US-based security team and that data is only accessed on an as-needed basis under strict controls."""
  model_name = "JulesBelveze/t5-small-headline-generator"
  
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  model = T5ForConditionalGeneration.from_pretrained(model_name)
  
  input_ids = tokenizer(
      [WHITESPACE_HANDLER(article_text)],
      return_tensors="pt",
      padding="max_length",
      truncation=True,
      max_length=384
  )["input_ids"]
  
  output_ids = model.generate(
      input_ids=input_ids,
      max_length=84,
      no_repeat_ngram_size=2,
      num_beams=4
  )[0]
  
  summary = tokenizer.decode(
      output_ids,
      skip_special_tokens=True,
      clean_up_tokenization_spaces=False
  )
  print(summary)
  ```
  
  ## Evaluation
  
  | Metric     | Score   |
  |------------|---------|
  | ROUGE 1    | 44.2379 |
  | ROUGE 2    | 17.4961 |
  | ROUGE L    | 41.1119 |
  | ROUGE Lsum | 41.1256 |