Update README.md

README.md

@@ -7,108 +7,158 @@ widget:
 license: mit
 ---
 
-# GerPT2
+# GerPT2-large
 
-A small German GPT2.
+German large and small versions of GPT2:
 
-Also check out [GerPT2-large](https://huggingface.co/benjamin/gerpt2-large), a large version of this model.
+- 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 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:
+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           |
-|                   |              |                 |
+
+| 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")
-model = AutoModelForCausalLM.from_pretrained("benjamin/gerpt2")
+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 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). 
-GerPT2 was trained with:
+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
+
 - using OneCycle learning rate with a maximum of 5e-3
+
 - with AdamW with a weight decay of 0.01
-- for 7 epochs
 
-Training took roughly 6 days on 8 TPUv3 cores.
+- for 2 epochs
 
-To train GerPT2, follow these steps. Scripts are located in the [Github repository](https://github.com/bminixhofer/gerpt2):
+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.json`.
+
+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.
+GerPT2-large is licensed under the MIT License.
 
 ## Acknowledgements
 
 Thanks to [Hugging Face](https://huggingface.co) for awesome tools and infrastructure.
-Special thanks to [PetFinder.my](https://www.petfinder.my/) for generously sponsoring the resources used for training.
+
+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.