German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on various texts for German.
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 😉
Note: The model was initially released under an anonymous alias (
anonymous-german-nlp/german-gpt2) so we now "de-anonymize" it.
More details about GPT-2 can be found in the great Hugging Face documentation.
15.11.2020: Initial release.
We use pretty much the same corpora as used for training the DBMDZ BERT model, that can be found in this repository.
Thanks to the awesome Hugging Face team, it is possible to create byte-level BPE with their awesome Tokenizers library.
With the previously mentioned awesome Tokenizers library we created a 52K byte-level BPE vocab based on the training corpora.
After creating the vocab, we could train the GPT-2 for German on one TPU over the complete training corpus (three epochs).
Using the model
The model itself can be used in this way:
from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("dbmdz/german-gpt2") model = AutoModelWithLMHead.from_pretrained("dbmdz/german-gpt2")
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:
from transformers import pipeline pipe = pipeline('text-generation', model="dbmdz/german-gpt2", tokenizer="dbmdz/german-gpt2") text = pipe("Der Sinn des Lebens ist es", max_length=100)["generated_text"] print(text)
This could output this beautiful text:
Der Sinn des Lebens ist es, im Geist zu verweilen, aber nicht in der Welt zu sein, sondern ganz im Geist zu leben. Die Menschen beginnen, sich nicht nach der Natur und nach der Welt zu richten, sondern nach der Seele,'
All models are licensed under MIT.
Huggingface model hub
All models are available on the Huggingface model hub.
Contact (Bugs, Feedback, Contribution and more)
For questions about our BERT models just open an issue here 🤗
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️
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