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Model: dbmdz/german-gpt2
Source: Original Platform

README.md

@@ -0,0 +1,90 @@
+---
+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 🤗
+