bert_uncased_ L 4_ H 256_ A 4

Introduction

This documentation covers the BERT Miniature models, a set of 24 compact BERT variants. These models are designed for environments with limited computational resources, offering an alternative to larger models by maintaining effectiveness through pre-training and fine-tuning processes. The BERT Miniature models are particularly effective in knowledge distillation scenarios where a larger, more accurate model serves as a teacher.

Architecture

The BERT Miniature models follow the standard BERT architecture, optimized for smaller sizes. These models are trained with WordPiece masking and are uncased English models. They are available in varying sizes defined by parameters such as layers (L) and hidden dimensions (H). For instance, the BERT-Tiny model has L=2 and H=128, while the BERT-Mini model has L=4 and H=256.

Training

Each BERT Miniature model can be fine-tuned using the same methods as the original BERT models. The models were evaluated on the GLUE benchmark, with hyperparameters optimized for each task. The training process involved:

• Batch sizes: 8, 16, 32, 64, 128
• Learning rates: 3e-4, 1e-4, 5e-5, 3e-5
• Training duration: 4 epochs

Guide: Running Locally

To run the BERT Miniature models locally, follow these steps:

1. Setup Environment:

   • Ensure Python and the Hugging Face Transformers library are installed.
   • Install PyTorch or TensorFlow, depending on your preference.

2. Download Model:

   • Select and download a model from the Hugging Face Model Hub, e.g., BERT-Mini via the link: BERT-Mini.

3. Load Model:

   • Use the Transformers library to load the model and tokenizer:

     from transformers import BertTokenizer, BertModel
     
     tokenizer = BertTokenizer.from_pretrained('google/bert_uncased_L-4_H-256_A-4')
     model = BertModel.from_pretrained('google/bert_uncased_L-4_H-256_A-4')
     

4. Inference:

   • Prepare input data and perform inference using the model.

5. Fine-tuning:

   • Adapt the model to your specific task by fine-tuning with your dataset.

Consider using cloud GPU providers like AWS, Google Cloud, or Azure for enhanced computational resources and efficiency.

License

The BERT Miniature models are licensed under the Apache 2.0 License. This allows for both personal and commercial use, distribution, and modification, provided that the original license is retained and any modifications are documented.