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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 ~100 GB from the ["German colossal, clean Common Crawl corpus" ](https://german-nlp-group.github.io/projects/gc4-corpus.html). |
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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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--- |
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**Disclaimer**: the presented and trained language models in this repository are for **research only** purposes. |
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The GC4 corpus - that was used for training - contains crawled texts from the internet. Thus, this GPT-2 model can |
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be considered as highly biased, resulting in a model that encodes stereotypical associations along gender, race, |
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ethnicity and disability status. Before using and working with the released checkpoints, it is highly recommended |
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to read: |
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[On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf) |
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from Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell. |
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The aim of this released GPT-2 model for German is to boost research on (large) pre-trained language models for German, especially |
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for identifying biases and how to prevent them, as most research is currently done for English only. |
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--- |
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# Changelog |
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06.09.2021: Initial release. Detailed information about training parameters follow soon. |
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# Text Generation |
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The following code snippet can be used to generate text with this German GPT-2 model: |
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```python |
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from transformers import pipeline |
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model_name = "stefan-it/german-gpt2-larger" |
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pipe = pipeline('text-generation', model=model_name, tokenizer=model_name) |
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text = pipe("Der Sinn des Lebens ist es", max_length=200)[0]["generated_text"] |
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print(text) |
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``` |
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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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This project heavily profited from the amazing Hugging Face |
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[Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104). |
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Many thanks for the great organization and discussions during and after the week! |
