Add model_cards

model_cards/deepset/gbert-base/README.md

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+# German BERT base
+
+Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to train our model and show that it outperforms its predecessors.  
+
+## Overview  
+**Paper:** [here](https://arxiv.org/pdf/2010.10906.pdf)  
+**Architecture:** BERT base  
+**Language:** German  
+
+## Performance  
+```
+GermEval18 Coarse: 78.17
+GermEval18 Fine:   50.90
+GermEval14:        87.98
+```
+
+See also:  
+deepset/gbert-base
+deepset/gbert-large
+deepset/gelectra-base
+deepset/gelectra-large
+deepset/gelectra-base-generator
+deepset/gelectra-large-generator
+
+## Authors
+Branden Chan: `branden.chan [at] deepset.ai`
+Stefan Schweter: `stefan [at] schweter.eu`
+Timo Möller: `timo.moeller [at] deepset.ai`
+
+## About us
+![deepset logo](https://raw.githubusercontent.com/deepset-ai/FARM/master/docs/img/deepset_logo.png)
+
+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)
+- [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)

model_cards/deepset/gbert-large/README.md

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+# German BERT large
+
+Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to train our model and show that it outperforms its predecessors.  
+
+## Overview  
+**Paper:** [here](https://arxiv.org/pdf/2010.10906.pdf)  
+**Architecture:** BERT large  
+**Language:** German  
+
+## Performance  
+```
+GermEval18 Coarse: 80.08
+GermEval18 Fine:   52.48
+GermEval14:        88.16
+```
+
+See also:  
+deepset/gbert-base
+deepset/gbert-large
+deepset/gelectra-base
+deepset/gelectra-large
+deepset/gelectra-base-generator
+deepset/gelectra-large-generator
+
+## Authors
+Branden Chan: `branden.chan [at] deepset.ai`
+Stefan Schweter: `stefan [at] schweter.eu`
+Timo Möller: `timo.moeller [at] deepset.ai`
+
+## About us
+![deepset logo](https://raw.githubusercontent.com/deepset-ai/FARM/master/docs/img/deepset_logo.png)
+
+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)
+- [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)
+
+

model_cards/deepset/gelectra-base-generator/README.md

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+# German ELECTRA base generator
+
+Released, Oct 2020, this is the generator component of the German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to train our model.
+
+The generator is useful for performing masking experiments. If you are looking for a regular language model for embedding extraction, or downstream tasks like NER, classification or QA, please use deepset/gelectra-base.
+
+## Overview  
+**Paper:** [here](https://arxiv.org/pdf/2010.10906.pdf)  
+**Architecture:** ELECTRA base (generator)
+**Language:** German  
+
+See also:  
+deepset/gbert-base
+deepset/gbert-large
+deepset/gelectra-base
+deepset/gelectra-large
+deepset/gelectra-base-generator
+deepset/gelectra-large-generator
+
+## Authors
+Branden Chan: `branden.chan [at] deepset.ai`
+Stefan Schweter: `stefan [at] schweter.eu`
+Timo Möller: `timo.moeller [at] deepset.ai`
+
+## About us
+![deepset logo](https://raw.githubusercontent.com/deepset-ai/FARM/master/docs/img/deepset_logo.png)
+
+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)
+- [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)

model_cards/deepset/gelectra-base/README.md

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+# German ELECTRA base
+
+Released, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to train our model. Our evaluation suggests that this model is somewhat undertrained. For best performance from a base sized model, we recommend deepset/gbert-base
+
+## Overview  
+**Paper:** [here](https://arxiv.org/pdf/2010.10906.pdf)  
+**Architecture:** ELECTRA base (discriminator)
+**Language:** German  
+
+## Performance  
+```
+GermEval18 Coarse: 76.02
+GermEval18 Fine:   42.22
+GermEval14:        86.02
+```
+
+See also:  
+deepset/gbert-base
+deepset/gbert-large
+deepset/gelectra-base
+deepset/gelectra-large
+deepset/gelectra-base-generator
+deepset/gelectra-large-generator
+
+## Authors
+Branden Chan: `branden.chan [at] deepset.ai`
+Stefan Schweter: `stefan [at] schweter.eu`
+Timo Möller: `timo.moeller [at] deepset.ai`
+
+## About us
+![deepset logo](https://raw.githubusercontent.com/deepset-ai/FARM/master/docs/img/deepset_logo.png)
+
+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)
+- [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)

model_cards/deepset/gelectra-large-generator/README.md

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+# German ELECTRA large generator
+
+Released, Oct 2020, this is the generator component of the German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to train our model.
+
+The generator is useful for performing masking experiments. If you are looking for a regular language model for embedding extraction, or downstream tasks like NER, classification or QA, please use deepset/gelectra-large.
+
+## Overview  
+**Paper:** [here](https://arxiv.org/pdf/2010.10906.pdf)  
+**Architecture:** ELECTRA large (generator)  
+**Language:** German  
+
+## Performance  
+```
+GermEval18 Coarse: 80.70
+GermEval18 Fine:   55.16
+GermEval14:        88.95
+```
+
+See also:  
+deepset/gbert-base
+deepset/gbert-large
+deepset/gelectra-base
+deepset/gelectra-large
+deepset/gelectra-base-generator
+deepset/gelectra-large-generator
+
+## Authors
+Branden Chan: `branden.chan [at] deepset.ai`
+Stefan Schweter: `stefan [at] schweter.eu`
+Timo Möller: `timo.moeller [at] deepset.ai`
+
+## About us
+![deepset logo](https://raw.githubusercontent.com/deepset-ai/FARM/master/docs/img/deepset_logo.png)
+
+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)
+- [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)
+
+

model_cards/deepset/gelectra-large/README.md

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+# German ELECTRA large
+
+Released, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to train our model and show that this is the state of the art German language model.
+
+## Overview  
+**Paper:** [here](https://arxiv.org/pdf/2010.10906.pdf)  
+**Architecture:** ELECTRA large (discriminator)
+**Language:** German  
+
+## Performance  
+```
+GermEval18 Coarse: 80.70
+GermEval18 Fine:   55.16
+GermEval14:        88.95
+```
+
+See also:  
+deepset/gbert-base
+deepset/gbert-large
+deepset/gelectra-base
+deepset/gelectra-large
+deepset/gelectra-base-generator
+deepset/gelectra-large-generator
+
+## Authors
+Branden Chan: `branden.chan [at] deepset.ai`
+Stefan Schweter: `stefan [at] schweter.eu`
+Timo Möller: `timo.moeller [at] deepset.ai`
+
+## About us
+![deepset logo](https://raw.githubusercontent.com/deepset-ai/FARM/master/docs/img/deepset_logo.png)
+
+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)
+- [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)