Introduction
BART (Bidirectional and Auto-Regressive Transformers) provides a versatile framework for NLP tasks:
- Combines bidirectional encoding (BERT) with auto-regressive decoding (GPT)
- Pre-trained by corrupting text with noise and learning to reconstruct it
- Excels at text generation, comprehension, and transformation tasks
- Developed by Facebook AI in 2019 for sequence-to-sequence applications
Instructions
1. Choose Interaction Method
- Hugging Face: Import transformers library
- PyTorch: Load pre-trained weights
- API: Access via Hugging Face Inference API
2. Implementation Steps
- Install dependencies with
pip install transformers torch - Import model and tokenizer classes
- Initialize with pre-trained weights
- Process text through tokenizer and model pipeline
3. Key Parameters
- num_beams: Controls beam search (higher = better quality)
- max_length: Limit output token count
- length_penalty: Favor shorter/longer sequences
4. Refinement & Best Practices
- Fine-tune on domain-specific data when possible
- Use early stopping for efficient generation
- Implement post-processing for better outputs
Capabilities
Text Summarization
- • News article condensation
- • Document abstractive summaries
- • Meeting notes generation
Translation
- • Multi-language support
- • Context-aware translations
- • Domain adaptation
Text Generation
- • Question answering
- • Content completion
- • Dialogue systems
Text Reconstruction
- • Error correction
- • Denoising documents
- • Content restoration
Examples
# Text Summarization with BART
from transformers import BartTokenizer, BartForConditionalGeneration
# Load pre-trained model and tokenizer
tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
# Prepare input text
article_text = "Your long article text goes here..."
inputs = tokenizer([article_text], max_length=1024, return_tensors='pt')
# Generate summary
summary_ids = model.generate(inputs['input_ids'], num_beams=4,
max_length=150, early_stopping=True)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary)
# Fine-tuning BART for a custom task
from transformers import BartTokenizer, BartForConditionalGeneration
from transformers import Trainer, TrainingArguments
# Load model and tokenizer
model = BartForConditionalGeneration.from_pretrained('facebook/bart-base')
tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
# Set up training arguments
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=3,
per_device_train_batch_size=4,
save_steps=10_000,
save_total_limit=2,
)
# Initialize trainer and start fine-tuning
trainer = Trainer(
model=model,
args=training_args,
train_dataset=your_dataset,
data_collator=your_data_collator,
)
trainer.train()
Table of Contents
Key Features
- • Bidirectional Encoding: Complete context understanding
- • Noise-Based Pre-training: Robust to input variations
- • Multiple Sizes: base, large, large-cnn variants
- • Task Flexibility: Single architecture for many tasks
- • Fine-tuning Friendly: Adapts well to specific domains