About the BERT Model (Large Language Model)

BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model that focuses on pre-training deep bidirectional representations from unlabeled text. This approach enables the model to understand the context of a word based on all of its surroundings (left and right of the word). BERT has achieved state-of-the-art results in a wide range of natural language processing tasks, showcasing its versatility and effectiveness.

Overview

• Use Case: Natural language understanding tasks including question answering, language inference, sentiment analysis, and named entity recognition
• Creator: Google AI Language (Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova)
• Architecture: Transformer-based encoder with bidirectional self-attention using Masked Language Model (MLM) and Next Sentence Prediction (NSP) pre-training objectives
• Parameters: 110M
• Release Date: 2018
• License: Apache 2.0
• Context Length: 512 tokens

GPU Memory Requirements

Default (FP16) inference requires approximately 0.25 GB of GPU memory.

Training Data

BooksCorpus (800M words) and English Wikipedia (2,500M words)

Evaluation Benchmarks

• GLUE
• SQuAD 1.1
• SQuAD 2.0
• SWAG

References

• https://arxiv.org/abs/1810.04805
• https://huggingface.co/bert-base-uncased
• https://github.com/google-research/bert

Notes

• Parameter count is for BERT-base model; BERT-large has 340M parameters
• GPU memory requirements are approximate for inference with batch size 1; gpuMemoryRequirementGB represents FP16 precision
• Care should be taken in applications that could amplify biases present in training data
• BERT is an encoder-only transformer model designed for natural language understanding tasks, not text generation