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Model: benjamin/gerpt2-large Source: Original Platform
2026-06-08 05:31:17 +08:00
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+---
+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 sponsoring the resources used for training.