Update README.md

README.md

@@ -12,7 +12,6 @@ license: mit
 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.
@@ -22,81 +21,55 @@ See the [GPT2 model card](https://huggingface.co/gpt2) for considerations on lim
 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) |
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 |-------------------|--------------|-----------------|
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 | dbmdz/german-gpt2 | 49.47        | 62.92           |
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 | GerPT2            | 24.78        | 35.33           |
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 | 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
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 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
 tokenizer = AutoTokenizer.from_pretrained("benjamin/gerpt2-large")
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 model = AutoModelForCausalLM.from_pretrained("benjamin/gerpt2-large")
 
 prompt = "<your prompt>"
 
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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 print(pipe(prompt)[0]["generated_text"])
-
 ```
 
 Also, two tricks might improve the generated text:
 
 ```python
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 output = model.generate(
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     # during training an EOS token was used to mark the beginning of each text
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     # so it can help to insert it at the start
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     torch.tensor(
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         [tokenizer.eos_token_id] + tokenizer.encode(prompt)
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     ).unsqueeze(0),
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     do_sample=True,
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     # try setting bad_words_ids=[[0]] to disallow generating an EOS token, without this the model is
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     # prone to ending generation early because a significant number of texts from the training corpus
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     # is quite short
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     bad_words_ids=[[0]],
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     max_length=max_length,
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 )[0]
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 print(tokenizer.decode(output))
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 ```
 
 ## Training details
 
 GerPT2-large is trained on the entire German data (67GB) 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
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 - using OneCycle learning rate with a maximum of 5e-3
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 - with AdamW with a weight decay of 0.01
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 - for 2 epochs
 
 Training took roughly 12 days on 8 TPUv3 cores.
@@ -104,61 +77,54 @@ 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.
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 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.:
 
 ```
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 ĠMinde -> Ġleast
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 Ġjed -> Ġwhatsoever
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 flughafen -> Air
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 vermittlung -> employment
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 teilung -> ignment
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 ĠInterpretation -> Ġinterpretation
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 Ġimport -> Ġimported
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 hansa -> irl
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 genehmigungen -> exempt
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 ĠAuflist -> Ġlists
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 Ġverschwunden -> Ġdisappeared
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 ĠFlyers -> ĠFlyers
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 Kanal -> Channel
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 Ġlehr -> Ġteachers
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 Ġnahelie -> Ġconvenient
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 gener -> Generally
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 mitarbeiter -> staff
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 ```
 
 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.
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 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-large 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.