Bert short story
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The document discusses BERT, a new linguistic model that uses transformers to represent text. BERT performs very well on natural language understanding tasks and there are different methodologies for adapting BERT to specific tasks, including further pre-training, retraining strategies, and multitasking learning. The basic BERT model has 12 transformer blocks and 512 token input, and outputs vector representations. Research is ongoing to improve subject-specific text classification using BERT.
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- 1. Applications of BERT in NLP and Understanding Samer Baslan CMPE-258: Spring 2021
- 2. The Emergence of BERT & Previous methods
- 3. Basic Concepts about the new BERT linguistic model ● Today, most advanced text models use transformers to teach how to represent text. ● Ease of use - one output layer to existing neural architecture to obtain state-of- art accuracy in several NLP tasks ● 2 categories of NLP tasks: ○ Holistic ○ Tokenized ● Masked Language Models ● 2 stages of BERT model training ● Performed very well on GLUE, SQuAD, and SWAG (natural language understanding tasks)
- 4. BERT Retraining Methodology for Text Problems 2 groups of methodologies: ● The use of pretrained models (transfer learning) ● Multitasking Learning When adapting BERT to specific word processing tasks, a special retraining technique is required. 3 types of techniques: 1. Further pre-training 2. Retraining strategies 3. Multitasking Learning “Catastrophic Forgetting” Basic BERT model: ● An encoder with 12 transformer blocks, 12 attention areas, and a textual representation dimension of 768. ● 512 tokens input, and outputs its vector representation ● SEP, CLS tokens
- 5. improving the subject-specific classification of texts using BERT Traditional text embedding models represent tokens as an embedding Problems: ambiguity, subject- specificity General Universal Text Model, pre- trained on a large corpus of general purpose texts Research is ongoing, potential as a universal text model has not yet been revealed
- 6. BERTScore
- 7. Conclusion BERT producing state of the art results in NLP and driving the industry forward
- 8. Thanks! Samer Baslan CMPE-258: Deep Learning Spring 2021, Vijay Eranti