readme: ---
  language: de
  license: mit
  tags:
  - exbert
  ---
  
  ## 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](https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark), which is available [here](https://github.com/t-systems-on-site-services-gmbh/german-STSbenchmark).
  
  ## 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
  ![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo)
  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")](https://deepset.ai/german-bert)
  - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
  - [FARM](https://github.com/deepset-ai/FARM)
  - [Haystack](https://github.com/deepset-ai/haystack/)
  
  Get in touch:
  [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Website](https://deepset.ai)  
  
  By the way: [we're hiring!](http://www.deepset.ai/jobs)
  