Search results
4 days ago · BERT is an evolution of self-attention and transformer architecture that's becoming popular for neural network models. BERT is an encoder-only transformer. It's deeply bidirectional, meaning that it uses both left and right contexts in all layers. BERT involves two stages: unsupervised pre-training followed by supervised task-specific fine-tuning.
5 days ago · AI-generated text detection plays an increasingly important role in various fields. In this study, we developed an efficient AI-generated text detection model based on the BERT algorithm, which provides new ideas and methods for solving related problems. In the data preprocessing stage, a series of steps were taken to process the text, including operations such as converting to lowercase, word ...
3 days ago · The introduction of models like OpenAI's ChatGPT solutions marked a major leap, with GPT-3 demonstrating unprecedented capabilities in text generation. Meanwhile, Google's BERT brought a new level ...
2 days ago · What is BERT? Bidirectional Encoder Representation for Transformer (BERT) is an NLP model developed by Google Research in 2018, after its inception it has achieved state-of-the-art accuracy on several NLP tasks.
3 days ago · The details of BERT can be found here: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. We will go through how to setup the data pipeline and how to run the original BERT model. Then we will show step-by-step how to modify the model to leverage DeepSpeed.
2 days ago · Models like BERT, GPT, and T5 each excel in different areas. For instance, BERT is excellent for natural language understanding tasks, while GPT is renowned for text generation, and T5 offers a unified framework for various NLP tasks.
5 days ago · 1. Text Processing and Preprocessing In NLP. Tokenization: Dividing text into smaller units, such as words or sentences. Stemming and Lemmatization: Reducing words to their base or root forms. Stopword Removal: Removing common words (like “and”, “the”, “is”) that may not carry significant meaning.
