fine-tuned-bart-xsum

Overview

fine-tuned-bart-xsum is a fine-tuned version of the facebook/bart-large-xsum model specifically tailored for narrative text generation from given prompts. This model was trained on the xsum dataset, focusing on generating coherent and contextually appropriate text.

Model Details

• Model Type: facebook/bart-large-xsum
• Training Dataset: XSum (news summary dataset)
• Training Process:
  • Optimized for efficiency with batch processing, mixed precision training, and dynamic padding.
  • Trained over 3 epochs with learning rate adjustments and evaluation every 500 steps.

Usage

import torch

Check if a GPU is available

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

Move the model to the device

model.to(device) input_text = "tell me joke with bbc" input_ids = tokenizer(input_text, return_tensors="pt").input_ids input_ids = input_ids.to(device)

Generate summary

output = model.generate(input_ids, max_length=50, num_beams=4, early_stopping=True)

generated_summary = tokenizer.decode(output[0], skip_special_tokens=True)

print(generated_summary)

To use this model for text generation: