Overview

Language model: gbert-large-sts

Language: German
Training data: German STS benchmark train and dev set
Eval data: German STS benchmark test set
Infrastructure: 1x V100 GPU
Published: August 12th, 2021

Details

• We trained a gbert-large model on the task of estimating semantic similarity of German-language text pairs. The dataset is a machine-translated version of the STS benchmark, which is available here.

Hyperparameters

batch_size = 16
n_epochs = 4
warmup_ratio = 0.1
learning_rate = 2e-5
lr_schedule = LinearWarmup

Performance

Stay tuned... and watch out for new papers on arxiv.org ;)

Authors

• Julian Risch: julian.risch [at] deepset.ai
• Timo Möller: timo.moeller [at] deepset.ai
• Julian Gutsch: julian.gutsch [at] deepset.ai
• Malte Pietsch: malte.pietsch [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 | Website

By the way: we're hiring!