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.

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

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.

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.

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))

Model Card for bwlw3127/clickbait-bart-dailymail