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--- |
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language: de |
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widget: |
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- text: "Heute ist sehr schönes Wetter in" |
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license: mit |
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--- |
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# German GPT-2 model |
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In this repository we release (yet another) GPT-2 model, that was trained on various texts for German. |
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The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" as the English GPT-3 model. We do not plan extensive PR or staged releases for this model 😉 |
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**Note**: The model was initially released under an anonymous alias (`anonymous-german-nlp/german-gpt2`) so we now "de-anonymize" it. |
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More details about GPT-2 can be found in the great [Hugging Face](https://huggingface.co/transformers/model_doc/gpt2.html) documentation. |
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# Changelog |
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16.08.2021: Public release of re-trained version of our German GPT-2 model with better results. |
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15.11.2020: Initial release. Please use the tag `v1.0` for [this older version](https://huggingface.co/dbmdz/german-gpt2/tree/v1.0). |
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# Training corpora |
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We use pretty much the same corpora as used for training the DBMDZ BERT model, that can be found in [this repository](https://github.com/dbmdz/berts). |
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Thanks to the awesome Hugging Face team, it is possible to create byte-level BPE with their awesome [Tokenizers](https://github.com/huggingface/tokenizers) library. |
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With the previously mentioned awesome Tokenizers library we created a 50K byte-level BPE vocab based on the training corpora. |
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After creating the vocab, we could train the GPT-2 for German on a v3-8 TPU over the complete training corpus for 20 epochs. All hyperparameters |
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can be found in the official JAX/FLAX documentation [here](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/README.md) |
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from Transformers. |
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# Using the model |
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The model itself can be used in this way: |
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```python |
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from transformers import AutoTokenizer, AutoModelWithLMHead |
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tokenizer = AutoTokenizer.from_pretrained("dbmdz/german-gpt2") |
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model = AutoModelWithLMHead.from_pretrained("dbmdz/german-gpt2") |
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``` |
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However, text generation is a bit more interesting, so here's an example that shows how to use the great Transformers *Pipelines* for generating text: |
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```python |
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from transformers import pipeline |
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pipe = pipeline('text-generation', model="dbmdz/german-gpt2", |
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tokenizer="dbmdz/german-gpt2") |
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text = pipe("Der Sinn des Lebens ist es", max_length=100)[0]["generated_text"] |
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print(text) |
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``` |
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This could output this beautiful text: |
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``` |
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Der Sinn des Lebens ist es, im Geist zu verweilen, aber nicht in der Welt zu sein, sondern ganz im Geist zu leben. |
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Die Menschen beginnen, sich nicht nach der Natur und nach der Welt zu richten, sondern nach der Seele,' |
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``` |
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# License |
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All models are licensed under [MIT](LICENSE). |
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# Huggingface model hub |
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All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz). |
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# Contact (Bugs, Feedback, Contribution and more) |
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For questions about our BERT models just open an issue |
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[here](https://github.com/stefan-it/german-gpt/issues/new) 🤗 |
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# Acknowledgments |
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Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). |
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Thanks for providing access to the TFRC ❤️ |
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Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, |
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it is possible to download both cased and uncased models from their S3 storage 🤗 |
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