README.md

metadata

license: apache-2.0
library_name: transformers

Enhanced Model Features

Adaptability: Adjusts to diverse contexts and user needs, ensuring relevant and precise interactions.

Contextual Intelligence: Provides contextually aware responses, improving engagement and interaction quality.

Advanced Algorithms: Employs cutting-edge algorithms for sophisticated and intelligent responses.

User Experience: Designed with a focus on seamless interaction, offering an intuitive and refined user experience.

Enhanced Model Features

Adaptability: Adjusts to diverse contexts and user needs, ensuring relevant and precise interactions.

Contextual Intelligence: Provides contextually aware responses, improving engagement and interaction quality.

Advanced Algorithms: Employs cutting-edge algorithms for sophisticated and intelligent responses.

User Experience: Designed with a focus on seamless interaction, offering an intuitive and refined user experience.

from transformers import BartTokenizer, BartForConditionalGeneration
from datasets import load_dataset


	
		
	
	
		Load pre-trained BART model for summarization
	

tokenizer = BartTokenizer.from_pretrained('ayjays132/EnhancerModel')
model = BartForConditionalGeneration.from_pretrained('ayjays132/EnhancerModel')

	
		
	
	
		Load dataset
	

dataset = load_dataset("cnn_dailymail", "3.0.0")

	
		
	
	
		Function to generate summary
	

def summarize(text):
    inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
    summary_ids = model.generate(inputs['input_ids'], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)

	
		
	
	
		Debugging: Print the type and content of the first example
	

print("Type of dataset['test']:", type(dataset['test']))
print("Type of the first element in dataset['test']:", type(dataset['test'][0]))
print("Content of the first element in dataset['test']:", dataset['test'][0])

	
		
	
	
		Test the model on a few examples
	

for example in dataset['test'][:5]:
    try:
        # If the example is a string, then it's likely that 'dataset['test']' is not loaded as expected
        if isinstance(example, str):
            print(f"Article: {example}\n")
            print(f"Summary: {summarize(example)}\n")
        else:
            # Access the 'article' field if the example is a dictionary
            article = example.get('article', None)
            if article:
                print(f"Article: {article}\n")
                print(f"Summary: {summarize(article)}\n")
            else:
                print("No 'article' field found in this example.")
    except Exception as e:
        print(f"Error processing example: {e}")