bart base cnn

Introduction

The BART-base model fine-tuned on the CNN/DailyMail dataset is a summarization model available on Hugging Face. It utilizes the seq2seq architecture with a bidirectional encoder and a left-to-right decoder. This model is optimized for text generation tasks and achieves high performance in summarization and comprehension tasks.

Architecture

BART uses a standard seq2seq architecture, combining a BERT-like bidirectional encoder with a GPT-like decoder. It is pre-trained with tasks such as sentence shuffling and in-filling, where spans of text are masked. BART achieves state-of-the-art results in various tasks, including summarization and question answering.

Training

The model is fine-tuned on the CNN/DailyMail dataset using Ainize Teachable-NLP. The dataset is designed for summarization tasks, providing a robust training ground for developing a summarization model. The BART model matches RoBERTa's performance on GLUE and SQuAD tasks and excels in abstractive dialogue and summarization tasks.

Guide: Running Locally

To run the BART-base model locally, follow these steps:

1. Install Transformers Library: Ensure you have the transformers library installed in your Python environment.

   pip install transformers
   

2. Load Model and Tokenizer: Use the provided Python code to load the model and tokenizer.

   from transformers import PreTrainedTokenizerFast, BartForConditionalGeneration
   
   tokenizer = PreTrainedTokenizerFast.from_pretrained("ainize/bart-base-cnn")
   model = BartForConditionalGeneration.from_pretrained("ainize/bart-base-cnn")
   

3. Encode and Summarize Input Text: Encode your text and generate a summary.

   input_text = "Your text here"
   input_ids = tokenizer.encode(input_text, return_tensors="pt")
   summary_text_ids = model.generate(input_ids=input_ids, max_length=142, min_length=56, num_beams=4)
   print(tokenizer.decode(summary_text_ids[0], skip_special_tokens=True))
   

4. Cloud GPUs: For large-scale or resource-intensive tasks, consider using cloud GPU services such as AWS EC2, Google Cloud, or Azure for enhanced performance.

License

The BART-base model fine-tuned on CNN/DailyMail is released under the Apache 2.0 License, allowing for both personal and commercial use.