Overview

Language model: deepset/roberta-base-squad2-distilled
Language: English
Training data: SQuAD 2.0 training set Eval data: SQuAD 2.0 dev set Infrastructure: 4x V100 GPU
Published: Dec 8th, 2021

Details

• haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model.

Hyperparameters

batch_size = 80
n_epochs = 4
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
embeds_dropout_prob = 0.1
temperature = 1.5
distillation_loss_weight = 0.75

Performance

"exact": 79.8366040596311
"f1": 83.916407079888

Authors

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

About us

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deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.

Some of our other work:

• Distilled roberta-base-squad2 (aka "tinyroberta-squad2")
• German BERT (aka "bert-base-german-cased")
• GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")

Get in touch and join the Haystack community

<p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>.

We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p>

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By the way: we're hiring!