Understanding BART: A Breakdown of the BART Model in Natural Language Processing
nagababu molleti
Research intern @IIT(BHU),IITD,AIISC(UofSC) | ex-Gen AI Intern @ DIGIOTAI Solutions | ex-SDE intern @IIITH-RCTS| LLM | Generative Ai | Prompt engineering | Deep learning | NLP | Machine learning| R&D | Multimodality |AI
Introduction: Natural Language Processing (NLP) has witnessed significant advancements in recent years, and one of the notable models contributing to this progress is BART (Bidirectional and Auto-Regressive Transformers). BART, developed by Facebook AI, is a state-of-the-art model that excels in various NLP tasks. In this article, we will delve into the high-level concepts of the BART model, exploring its architecture, training methodology, and applications.
BART Overview: BART, an acronym for Bidirectional and Auto-Regressive Transformer, is a denoising autoencoder developed by Lewis et al. in 2019. It operates as a pre-trained sequence-to-sequence method, utilizing masked language modeling for Natural Language Generation and Translation. The model's architecture combines elements of both BERT and GPT models, making it a powerful and versatile tool for various NLP tasks.
from transformers import AutoModel, AutoTokenizer
BART = AutoModel.from_pretrained("facebook/bart-large")
print(BART)
BartModel(
(shared): Embedding(50265, 1024, padding_idx=1)
(encoder): BartEncoder(
(embed_tokens): Embedding(50265, 1024, padding_idx=1)
(embed_positions): BartLearnedPositionalEmbedding(1026, 1024)
(layers): ModuleList(
(0-11): 12 x BartEncoderLayer(
(self_attn): BartAttention(
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(activation_fn): GELUActivation()
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
)
(layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(decoder): BartDecoder(
(embed_tokens): Embedding(50265, 1024, padding_idx=1)
(embed_positions): BartLearnedPositionalEmbedding(1026, 1024)
(layers): ModuleList(
(0-11): 12 x BartDecoderLayer(
(self_attn): BartAttention(
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(activation_fn): GELUActivation()
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(encoder_attn): BartAttention(
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
)
(layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
)
Architecture: BART uses a standard Transformer-based neural machine translation architecture, featuring a bidirectional encoder similar to BERT and a left-to-right decoder akin to GPT. This combination allows BART to effectively capture contextual information from both directions, enhancing its understanding of language nuances during training.
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Pre-training: To achieve its robust capabilities, BART undergoes pre-training through a two-step process. Firstly, text is corrupted using an arbitrary noising function. Subsequently, the model learns to reconstruct the original text. This unsupervised pre-training strategy enables BART to capture general language patterns and representations, laying the foundation for its success in various NLP tasks.
Auto-regressive Training: In addition to its bidirectional capabilities, BART introduces the concept of auto-regressive training. During this phase, the model generates output tokens one at a time, conditioning each prediction on the previously generated tokens. This auto-regressive approach ensures that BART learns to generate coherent and contextually relevant sequences, a crucial aspect for tasks like text generation and summarization.
Additional Insights: BART boasts approximately 140 million parameters, surpassing both BERT (110 million parameters) and GPT-1 (117 million parameters). Despite its higher parameter count, BART outperforms these models significantly. This superiority can be attributed to BART's unique combination of bidirectional capabilities and auto-regressive training, showcasing its prowess in capturing complex language structures.
Applications: BART's versatility shines through in various NLP tasks, including text summarization, text generation, machine translation, and document classification. Its adaptability allows for fine-tuning over small supervised datasets, enabling the creation of domain-specific models for specialized tasks.
Conclusion: In conclusion, the BART model represents a significant milestone in the field of NLP, combining bidirectional capabilities with auto-regressive training to create a versatile and powerful architecture. As researchers continue to refine and extend transformer-based models, BART stands as a testament to the ongoing evolution of state-of-the-art NLP techniques.
Facebook Hugging Face ChatGPT #bart #bert #gpt #llm #nlp #deeplearning #transformers #encoder #decoder #research #generativeai #ai #machinelearning #neuralnetworks
AI Engineer
9 个月BART showed a few key concepts, 1. The importance of masking as a noising technique 2. Use of the full transformer architecture(encoder-decoder) They showed that using the encoder-decoder architecture does not reduce the capability at discriminative tasks( something which encoder-only models had excelled before BART).But also they showed that, at purely generative tasks or tasks where output is loosely constrained by input(like in the ELI5 dataset) BART is slightly behind stand-alone decoder models like GPT which are generative models. 3. They also showed that not only the pretaining objective but the model architecture is also important(By the testing done with the permuted language model) 4. They also confirmed the already known theory that bi-directional models are better at discriminative tasks and left-to-right models are better at generative tasks. I think BART excels in tasks like summarization and translation according to the theory. The bi-directional encoder has the capability to grasp the full meaning of the input text and the left-to-right decoder then can use representations from the encoder to generate meaningful outputs. This has been also shown in their results in the qualitative analysis section of the paper.