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
We bring NLP to the industry via open source!
Our focus: Industry specific language models & large scale QA systems.
Some of our work:
- German BERT (aka "bert-base-german-cased")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")
- FARM
- Haystack
Get in touch: Twitter | LinkedIn | Discord | GitHub Discussions | Website
By the way: we're hiring!
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Dataset used to train deepset/bert-base-uncased-squad2
Space using deepset/bert-base-uncased-squad2 1
Evaluation results
- Exact Match on squad_v2validation set verified75.653
- F1 on squad_v2validation set verified78.619