In this repository we release (yet another) GPT-2 model, that was trained on ~90 GB from the "German colossal, clean Common Crawl corpus" (GC4).
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 😉
Disclaimer: the presented and trained language models in this repository are for research only purposes. The GC4 corpus - that was used for training - contains crawled texts from the internet. Thus, this GPT-2 model can be considered as highly biased, resulting in a model that encodes stereotypical associations along gender, race, ethnicity and disability status. Before using and working with the released checkpoints, it is highly recommended to read:
from Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell.
The aim of this released GPT-2 model for German is to boost research on (large) pre-trained language models for German, especially for identifying biases and how to prevent them, as most research is currently done for English only.
- 17.10.2021: We highly recommend to try the Text Generation Pipeline in Transformers. The quality of the generated text from the Inference Widget here can be lower.
- 06.09.2021: Initial release. Detailed information about training parameters coming soon.
The following code snippet can be used to generate text with this German GPT-2 model:
from transformers import pipeline model_name = "stefan-it/german-gpt2-larger" pipe = pipeline('text-generation', model=model_name, tokenizer=model_name) text = pipe("Der Sinn des Lebens ist es", max_length=200)["generated_text"] print(text)
The following archives are used for training the (first version) of this GPT-2 model:
Details and URLs can be found on the GC4 page.
Archives are then extracted and NLTK (
german model) is used to sentence split the corpus.
This results in a total training corpus size of 90GB.
We use the recently re-trained
dbmdz/german-gpt2 (version 2!)
model as back-bone model. Thus, the tokenizer and vocab is the same as used in the
The model was trained on a v3-8 TPU, with the following parameters:
python ./run_clm_flax.py --output_dir=/mnt/datasets/german-gpt2-larger/ --name_or_path dbmdz/german-gpt2 --do_train --do_eval --block_size=512 --per_device_train_batch_size=16 --per_device_eval_batch_size=16 --learning_rate=5e-3 --warmup_steps=1000 --adam_beta1=0.9 --adam_beta2=0.98 --weight_decay=0.01 --overwrite_output_dir --num_train_epochs=20 --logging_steps=500 --save_steps=2500 --eval_steps=2500 --train_file /mnt/datasets/gc4/train.txt --validation_file /mnt/datasets/gc4/validation.txt --preprocessing_num_workers 16
Training took around 17 days for 20 epochs.
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 team, it is possible to download this model from their S3 storage 🤗
This project heavily profited from the amazing Hugging Face Community Week. Many thanks for the great organization and discussions during and after the week!
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