bart large

Introduction

BART (Bidirectional and Auto-Regressive Transformers) is a large-scale model pre-trained for natural language tasks such as generation, translation, and comprehension. It was introduced in the paper "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension" by Lewis et al. This model is particularly effective when fine-tuned for specific tasks like summarization and translation.

Architecture

BART utilizes a transformer encoder-decoder architecture. The encoder is bidirectional, similar to BERT, and the decoder is autoregressive like GPT. BART is pre-trained by corrupting text using a noising function and then training the model to reconstruct the original text.

Training

BART is pre-trained in two main steps:

1. Corrupting Text: Text is corrupted with a noising function.
2. Reconstruction: The model learns to reconstruct the original text from the corrupted version. Fine-tuning on specific supervised datasets enhances its capability for various NLP tasks.

Guide: Running Locally

To run BART locally, follow these steps:

1. Install Transformers Library:

   pip install transformers
   

2. Load the Model and Tokenizer:

   from transformers import BartTokenizer, BartModel
   
   tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
   model = BartModel.from_pretrained('facebook/bart-large')
   

3. Prepare Inputs:

   inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
   

4. Obtain Outputs:

   outputs = model(**inputs)
   last_hidden_states = outputs.last_hidden_state
   

Cloud GPUs

For more efficient execution, especially on large datasets or complex tasks, consider using cloud GPU services such as AWS EC2, Google Cloud, or Azure.

License

BART is released under the Apache 2.0 License, which allows for both commercial and non-commercial use, modification, and distribution.