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.
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}")