Introduction

This fork is an slightly modification to be able to train the large model in the Squad 2.0 dataset using a RTX 2080 (8GB) GPU.

The modifications are:

• Use FP-16
• Reduce batch_size to 4
• Reduce seq_len to 340.
• Train half of the network, ie, layers 12, 13..., 23. Freeze the others (1, 2, ... 11)
• Replace the FC layers (1024 -> 1) to a deeper FC layer (512 -> 256 -> 1) for start_logits, end_logits and CLS.

The files changed are:

• scripts/gpu_squad_base_GPU.sh
• run_squad_GPU.py
• model_utils_GPU.py
• function_builder_GPU.py

With those modifications I could achieve 86,23 F1-Score on the Squad-2.0 dev_set, training for 85000 steps (~ 3 epochs of the full dataset). This training took about 5-6 hours.

best_exact 83.4077318285185
best_exact_thresh -1.920951247215271
best_f1 86.23180344890973
best_f1_thresh -1.8610079288482666
has_ans_exact 0.8658906882591093
has_ans_f1 0.9299826812846799

I consider a very good result, since it is trained in a very limited hardware.

For those who has TPU access, could use the original implementation, traing all the layers, replacing the single FC Layer for a deeper FC layer and see how it improves the network.