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bert-base-uncased for QA

Overview

Language model: bert-base-uncased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Infrastructure: 1x Tesla v100

Hyperparameters

batch_size = 32
n_epochs = 3
base_LM_model = "bert-base-uncased"
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64

Performance

"exact": 73.67977764676156
"f1": 77.87647139308865

Authors

  • Timo Möller: timo.moeller [at] deepset.ai
  • Julian Risch: julian.risch [at] deepset.ai
  • Malte Pietsch: malte.pietsch [at] deepset.ai
  • Michel Bartels: michel.bartels [at] deepset.ai

    About us

    deepset logo We bring NLP to the industry via open source!
    Our focus: Industry specific language models & large scale QA systems.

Some of our work:

Get in touch: Twitter | LinkedIn | Slack | GitHub Discussions | Website

By the way: we're hiring!

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Dataset used to train deepset/bert-base-uncased-squad2