|
--- |
|
language: de |
|
license: mit |
|
datasets: |
|
- wikipedia |
|
- OPUS |
|
- OpenLegalData |
|
- oscar |
|
--- |
|
|
|
# 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 |
|
 |
|
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/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) |
|
|
|
By the way: [we're hiring!](http://www.deepset.ai/jobs) |
|
|
