# Vocab
To create vocab.txt file, run **make_new_vocab.py**

# Prep dataset
**prep_dataset_training**: Format and split dataset, so it can be used for training. Adapt which dataset version to make!

# train German FoodBERT
**language_modeling**

This was executed on Google Colab with the following parameters:

!python /content/drive/MyDrive/masterarbeit/language_modeling.py --output_dir="/content/drive/MyDrive/masterarbeit/output" --model_type=bert --model_name=bert-base-german-cased --do_train --train_data_file="/content/drive/MyDrive/masterarbeit/data/training_data.txt" --do_eval --eval_data_file="/content/drive/MyDrive/masterarbeit/data/testing_data.txt" --mlm --line_by_line --per_device_train_batch_size=8 --gradient_accumulation_steps=2 --per_device_eval_batch_size=8 --save_total_limit=5 --save_steps=10000 --logging_steps=10000 --evaluation_strategy=epoch --model_name_or_path="bert-base-german-cased"  

The exclamation mark at the beginning of the line is only needed on Google Colab and can be omitted when executing locally. Paths need to be adjusted when executing.


# Vocab Files:
**bert-base-german-cased_tokenizer.json**: original bert-base-german-cased tokenizer file
**bert_vocab.txt**: original bert-base-german-cased vocab
**used_ingredients**: all ingredients in dataset
**vocab.txt**: German FoodBERT vocabulary