| --- |
| language: de |
|
|
| widget: |
| - text: "Heute ist sehr schönes Wetter in" |
|
|
| license: mit |
| --- |
| |
| # 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](https://huggingface.co/transformers/model_doc/gpt2.html) documentation. |
|
|
| # Changelog |
|
|
| 16.08.2021: Public release of re-trained version of our German GPT-2 model with better results. |
|
|
| 15.11.2020: Initial release. Please use the tag `v1.0` for [this older version](https://huggingface.co/dbmdz/german-gpt2/tree/v1.0). |
|
|
| # Training corpora |
|
|
| 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). |
|
|
| 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. |
|
|
| With the previously mentioned awesome Tokenizers library we created a 50K byte-level BPE vocab based on the training corpora. |
|
|
| 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 |
| can be found in the official JAX/FLAX documentation [here](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/README.md) |
| from Transformers. |
|
|
| # Using the model |
|
|
| The model itself can be used in this way: |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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)[0]["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,' |
| ``` |
|
|
| # License |
|
|
| All models are licensed under [MIT](LICENSE). |
|
|
| # Huggingface model hub |
|
|
| All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz). |
|
|
| # Contact (Bugs, Feedback, Contribution and more) |
|
|
| For questions about our BERT models just open an issue |
| [here](https://github.com/stefan-it/german-gpt/issues/new) 🤗 |
|
|
| # Acknowledgments |
|
|
| Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). |
| Thanks for providing access to the TFRC ❤️ |
|
|
| Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, |
| it is possible to download both cased and uncased models from their S3 storage 🤗 |
|
|
|
|
