readme: ---
  language: de
  
  widget:
  - text: "In einer schockierenden Entdeckung fanden Wissenschaftler eine Herde Einhörner, die in einem abgelegenen, zuvor unerforschten Tal in den Anden lebten."
  
  license: mit
  ---
  
  # GerPT2
  
  German large and small versions of GPT2:
  
  - https://huggingface.co/benjamin/gerpt2
  - https://huggingface.co/benjamin/gerpt2-large
  
  See the [GPT2 model card](https://huggingface.co/gpt2) for considerations on limitations and bias. See the [GPT2 documentation](https://huggingface.co/transformers/model_doc/gpt2.html) for details on GPT2.
  
  ## Comparison to [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2)
  
  I evaluated both GerPT2-large and the other German GPT2, [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2) on the [CC-100](http://data.statmt.org/cc-100/) dataset and on the German Wikipedia:
  
  |                   | CC-100 (PPL) | Wikipedia (PPL) |
  |-------------------|--------------|-----------------|
  | dbmdz/german-gpt2 | 49.47        | 62.92           |
  | GerPT2            | 24.78        | 35.33           |
  | GerPT2-large      | __16.08__    | __23.26__       |
  |                   |              |                 |
  
  See the script `evaluate.py` in the [GerPT2 Github repository](https://github.com/bminixhofer/gerpt2) for the code.
  
  ## Usage
  
  ```python
  from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("benjamin/gerpt2-large")
  model = AutoModelForCausalLM.from_pretrained("benjamin/gerpt2-large")
  
  prompt = "<your prompt>"
  
  pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
  print(pipe(prompt)[0]["generated_text"])
  ```
  
  Also, two tricks might improve the generated text:
  
  ```python
  output = model.generate(
      # during training an EOS token was used to mark the beginning of each text
      # so it can help to insert it at the start
      torch.tensor(
          [tokenizer.eos_token_id] + tokenizer.encode(prompt)
      ).unsqueeze(0),
      do_sample=True,
      # try setting bad_words_ids=[[0]] to disallow generating an EOS token, without this the model is
      # prone to ending generation early because a significant number of texts from the training corpus
      # is quite short
      bad_words_ids=[[0]],
      max_length=max_length,
  )[0]
  print(tokenizer.decode(output))
  ```
  
  ## Training details
  
  GerPT2-large is trained on the entire German data from the [CC-100 Corpus](http://data.statmt.org/cc-100/) and weights were initialized from the [English GPT2 model](https://huggingface.co/gpt2-large). 
  GerPT2-large was trained with:
  
  - a batch size of 256
  - using OneCycle learning rate with a maximum of 5e-3
  - with AdamW with a weight decay of 0.01
  - for 2 epochs
  
  Training took roughly 12 days on 8 TPUv3 cores.
  
  To train GerPT2-large, follow these steps. Scripts are located in the [Github repository](https://github.com/bminixhofer/gerpt2):
  
  0. Download and unzip training data from http://data.statmt.org/cc-100/.
  1. Train a tokenizer using `prepare/train_tokenizer.py`. As training data for the tokenizer I used a random subset of 5% of the CC-100 data.
  2. (optionally) generate a German input embedding matrix with `prepare/generate_aligned_wte.py`. This uses a neat trick to semantically map tokens from the English tokenizer to tokens from the German tokenizer using aligned word embeddings. E. g.:
  
  ```
  ĠMinde -> Ġleast
  Ġjed -> Ġwhatsoever
  flughafen -> Air
  vermittlung -> employment
  teilung -> ignment
  ĠInterpretation -> Ġinterpretation
  Ġimport -> Ġimported
  hansa -> irl
  genehmigungen -> exempt
  ĠAuflist -> Ġlists
  Ġverschwunden -> Ġdisappeared
  ĠFlyers -> ĠFlyers
  Kanal -> Channel
  Ġlehr -> Ġteachers
  Ġnahelie -> Ġconvenient
  gener -> Generally
  mitarbeiter -> staff
  ```
  
  This helps a lot on a trial run I did, although I wasn't able to do a full comparison due to budget and time constraints. To use this WTE matrix it can be passed via the `wte_path` to the training script. Credit to [this blogpost](https://medium.com/@pierre_guillou/faster-than-training-from-scratch-fine-tuning-the-english-gpt-2-in-any-language-with-hugging-f2ec05c98787) for the idea of initializing GPT2 from English weights. 
  
  3. Tokenize the corpus using `prepare/tokenize_text.py`. This generates files for train and validation tokens in JSON Lines format.
  4. Run the training script `train.py`! `run.sh` shows how this was executed for the full run with config `configs/tpu_large.json`.
  
  ## License
  
  GerPT2 is licensed under the MIT License.
  
  ## Citing
  
  Please cite GerPT2 as follows:
  
  ```
  @misc{Minixhofer_GerPT2_German_large_2020,
  author = {Minixhofer, Benjamin},
  doi = {10.5281/zenodo.5509984},
  month = {12},
  title = {{GerPT2: German large and small versions of GPT2}},
  url = {https://github.com/bminixhofer/gerpt2},
  year = {2020}
  }
  ```
  
  ## Acknowledgements
  
  Thanks to [Hugging Face](https://huggingface.co) for awesome tools and infrastructure.
  Huge thanks to [Artus Krohn-Grimberghe](https://twitter.com/artuskg) at [LYTiQ](https://www.lytiq.de/) for making this possible by spons