Training: BioASQ 10B with SQUAD sampled evenly to match the same samples as BioASQ 10B Eval: BioASQ 9B Eval with SQUAD Eval sampled evenly to match the same samples as BioASQ 9B Eval

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Went from untrained exact match: 60.9% (f1 71.8%) to exact match: 95.2% (96.6% f1) on BioASQ 9B held out training set. Scores on SQUAD+BioASQ remained stable at exact match: 72.5% (f1 81.4%) to 88.5% (f1 93.3%).

Framework versions

Transformers 4.18.0
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1

scite/roberta-base-squad2-nq-bioasq

roberta-base-squad2-nq-bioasq

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

scite/
roberta-base-squad2-nq-bioasq