gerpt2-large / README.md

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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.