In this tutorial, you'll learn how to pre-train BERT-base from scratch using a Habana Gaudi-based DL1 instance on AWS. By leveraging the Hugging Face Transformers, Optimum Habana, and Datasets libraries, you can efficiently perform masked language modeling—one of BERT's original pre-training tasks.
What is BERT?
BERT (Bidirectional Encoder Representations from Transformers) is a natural language processing model developed by Google AI in 2018. It excels at over 11 common language tasks, including sentiment analysis and named entity recognition.
What is Masked Language Modeling (MLM)?
MLM enables bidirectional learning by masking a word in a sentence and forcing the model to predict it using surrounding context. For example: "Dang! I'm out fishing and a huge trout just [MASK] my line!"
Step-by-Step Guide
1. Prepare the Dataset
Start by installing required packages and logging into the Hugging Face Hub. The original BERT was trained on Wikipedia and BookCorpus. Load and merge these datasets using the Datasets library:
from datasets import concatenate_datasets, load_dataset
bookcorpus = load_dataset("bookcorpus", split="train")
wiki = load_dataset("wikipedia", "20220301.en", split="train")
wiki = wiki.remove_columns([col for col in wiki.column_names if col != "text"])
raw_datasets = concatenate_datasets([bookcorpus, wiki])
Note: Advanced preprocessing like deduplication is recommended for production use.
2. Train a Tokenizer
Train a new tokenizer on your dataset using BertTokenizerFast from Transformers. This converts text into token IDs suitable for model input.
3. Preprocess the Dataset
Apply the tokenizer to the dataset, creating attention masks and token type IDs if needed.
4. Pre-train BERT on Habana Gaudi
Use the Optimum Habana library to efficiently run the pre-training on a Habana Gaudi accelerator. The optimized script handles distributed training and mixed precision.
Requirements
- AWS account with DL1 instance quota
- AWS CLI installed and configured
- Hugging Face Hub account
Helpful Resources
This tutorial covers steps 1 to 3 (CPU-intensive) on a c6i.12xlarge instance and step 4 on a DL1 instance. The resulting model and tokenizer can be pushed to the Hugging Face Hub.