---
library_name: transformers
tags:
- clickbait
- headline-generation
- summarization
- seq2seq
license: mit
language: en
---

# Model Card for `bwlw3127/clickbait-bart-dailymail`

This model fine-tunes [`facebook/bart-base`](https://huggingface.co/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](https://huggingface.co/facebook/bart-base)  
- **Finetuned from:** `facebook/bart-base`

### Model Sources

- **Repository (code):** [GitHub – weiw3127/clickbait](https://github.com/weiw3127/clickbait)  
- **Model (weights):** [Hugging Face Hub](https://huggingface.co/bwlw3127/clickbait-bart-dailymail)

---

## 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

```python
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))