**Practical Prompt Engineering: Context-Aware Story Generati

Practical Prompt Engineering: Context-Aware Story Generation with BART

Story generation is a fascinating application of Natural Language Processing (NLP) that involves creating engaging narratives based on provided context or prompts. In this post, we'll explore a practical approach to context-aware story generation using BART (Bidirectional and Auto-Regressive Transformers), a state-of-the-art language model developed by Meta AI.

Code Snippet: BART Story Generation

python
from transformers import BartTokenizer, BartForConditionalGeneration
import torch

# Load pre-trained BART model and tokenizer
tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')

# Define a function to generate a story based on context
def generate_story(context, max_length=200):
    inputs = tokenizer.encode_plus(
        context,
        add_special_tokens=True,
        max_length=max_length,
        r...

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