Language model: gbert-large-sts
Training data: German STS benchmark train and dev set
Eval data: German STS benchmark test set
Infrastructure: 1x V100 GPU
Published: August 12th, 2021
- 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.
batch_size = 16 n_epochs = 4 warmup_ratio = 0.1 learning_rate = 2e-5 lr_schedule = LinearWarmup
Stay tuned... and watch out for new papers on arxiv.org ;)
- 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
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")
By the way: we're hiring!
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