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Overview

Language model: deepset/tinybert-6L-768D-squad2
Language: English
Training data: SQuAD 2.0 training set x 20 augmented + SQuAD 2.0 training set without augmentation Eval data: SQuAD 2.0 dev set Infrastructure: 1x V100 GPU
Published: Dec 8th, 2021

Details

  • haystack's intermediate layer and prediction layer distillation features were used for training (based on TinyBERT). deepset/bert-base-uncased-squad2 was used as the teacher model and huawei-noah/TinyBERT_General_6L_768D was used as the student model.

Hyperparameters

Intermediate layer distillation

batch_size = 26
n_epochs = 5
max_seq_len = 384
learning_rate = 5e-5
lr_schedule = LinearWarmup
embeds_dropout_prob = 0.1
temperature = 1

Prediction layer distillation

batch_size = 26
n_epochs = 5
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
embeds_dropout_prob = 0.1
temperature = 1
distillation_loss_weight = 1.0

Performance

"exact": 71.87736882001179
"f1": 76.36111895973675

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.

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Dataset used to train deepset/tinybert-6l-768d-squad2