clickbait-bart-dailymail

Model Card for bwlw3127/clickbait-bart-dailymail

This model fine-tunes facebook/bart-base to generate clickbait-style headlines from DailyMail articles.
It takes an article as input and produces a short, attention-grabbing headline.

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Model Details

Model Description

This is a sequence-to-sequence model trained with the 🤗 Transformers library. It was adapted from facebook/bart-base and fine-tuned on scraped DailyMail article/headline pairs.
The goal is to demonstrate how large language models can be steered toward stylistic objectives such as “clickbait” headline generation.

• Developed by: @bwlw3127
• Model type: Seq2Seq (Encoder–Decoder) — BART
• Language(s): English
• License: MIT (code); base model follows facebook/bart-base license
• Finetuned from: facebook/bart-base

Model Sources

• Repository (code): GitHub – weiw3127/clickbait
• Model (weights): Hugging Face Hub

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Uses

Direct Use

• Generate sensational or catchy headlines from article text.
• Educational demo for seq2seq fine-tuning with Hugging Face.

Downstream Use

• Adaptation to other headline-generation tasks (news summarization, marketing, etc.) by re-finetuning on different headline corpora.

Out-of-Scope Use

• Producing factually reliable news summaries.
• Sensitive domains where misleading/sensational output may cause harm.

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Bias, Risks, and Limitations

• The model reflects biases in DailyMail content and clickbait style.
• Headlines may exaggerate, distort, or misrepresent facts.
• It should not be used in production systems where factual accuracy is critical.

Recommendations

• Use only in controlled or educational contexts.
• Always review outputs manually before publishing.

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How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "bwlw3127/clickbait-bart-dailymail"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

article = "Scientists have discovered a new exoplanet twice the size of Earth..."
inputs = tokenizer("generate clickbait headline: " + article,
                   return_tensors="pt", truncation=True, max_length=512)

outputs = model.generate(**inputs, max_length=24, num_beams=4, early_stopping=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))