bert large uncased

Introduction

BERT (Bidirectional Encoder Representations from Transformers) is a transformers model pretrained on a large corpus of English data using a masked language modeling objective. It was introduced by Devlin et al. in 2018 and became a foundational model for various natural language processing tasks.

Architecture

The BERT Large model is composed of:

• 24 layers
• 1024 hidden dimensions
• 16 attention heads
• 336M parameters

It supports tasks such as masked language modeling and next sentence prediction, allowing the model to understand the context and relationships between words and phrases in a sentence.

Training

BERT was pretrained using two primary objectives:

• Masked Language Modeling (MLM): Randomly masks 15% of the words in the input and predicts them based on the surrounding context.
• Next Sentence Prediction (NSP): Concatenates two sentences and predicts if they follow each other in the original text.

The training data included BookCorpus and English Wikipedia, and the model was trained using 4 cloud TPUs for one million steps.

Guide: Running Locally

Basic Steps

1. Install Transformers and PyTorch or TensorFlow:

   pip install transformers torch  # for PyTorch
   pip install transformers tensorflow  # for TensorFlow
   

2. Load the Model:

   • For PyTorch:

     from transformers import BertTokenizer, BertModel
     tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
     model = BertModel.from_pretrained('bert-large-uncased')
     

   • For TensorFlow:

     from transformers import BertTokenizer, TFBertModel
     tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
     model = TFBertModel.from_pretrained('bert-large-uncased')
     

3. Use the Model:

   text = "Replace me by any text you'd like."
   encoded_input = tokenizer(text, return_tensors='pt')  # or 'tf' for TensorFlow
   output = model(**encoded_input)
   

Cloud GPUs

To accelerate computations, consider using cloud services that offer GPUs, such as AWS EC2, Google Cloud Platform, or Azure. These platforms provide scalable resources for handling large models like BERT.

License

BERT is released under the Apache License 2.0, which allows for both personal and commercial use with proper attribution.