metadata

license: mit
pipeline_tag: text-generation
datasets:
  - EdinburghNLP/xsum
language:
  - en
base_model:
  - facebook/bart-large-xsum

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: