Instructions to use deepset/gelectra-base-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gelectra-base-generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="deepset/gelectra-base-generator")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("deepset/gelectra-base-generator") model = AutoModelForMaskedLM.from_pretrained("deepset/gelectra-base-generator") - Notebooks
- Google Colab
- Kaggle
| language: de | |
| license: mit | |
| datasets: | |
| - wikipedia | |
| - OPUS | |
| - OpenLegalData | |
| # 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 | |
| <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> | |
| <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> | |
| <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> | |
| </div> | |
| <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> | |
| <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> | |
| </div> | |
| </div> | |
| [deepset](http://deepset.ai/) is the company behind the production-ready open-source AI framework [Haystack](https://haystack.deepset.ai/). | |
| Some of our other work: | |
| - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")](https://huggingface.co/deepset/tinyroberta-squad2) | |
| - [German BERT](https://deepset.ai/german-bert), [GermanQuAD and GermanDPR](https://deepset.ai/germanquad), [German embedding model](https://huggingface.co/mixedbread-ai/deepset-mxbai-embed-de-large-v1) | |
| - [deepset Cloud](https://www.deepset.ai/deepset-cloud-product), [deepset Studio](https://www.deepset.ai/deepset-studio) | |
| ## Get in touch and join the Haystack community | |
| <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>. | |
| We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> | |
| [Twitter](https://twitter.com/Haystack_AI) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://haystack.deepset.ai/) | [YouTube](https://www.youtube.com/@deepset_ai) | |
| By the way: [we're hiring!](http://www.deepset.ai/jobs) |