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t5-small for headline generation

This model is a t5-small fine-tuned for headline generation using the JulesBelveze/tldr_news dataset.

Using this model

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
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Dataset used to train JulesBelveze/t5-small-headline-generator

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