Reformat code with autopep8

Changes

2 changed files (+7/-5)

• bot.py
  • @@ -6,8 +6,7 @@
    parser = ArgumentParser() parser.add_argument('-t', '--token', help='Mastodon application access token') - parser.add_argument('-i', '--input', default='i am', - help='initial input text for prediction') + parser.add_argument('-i', '--input', help='initial input text for prediction') parser.add_argument('-m', '--model', default='model', help='path to load saved model') args = parser.parse_args()
  • @@ -19,7 +18,8 @@
    # Run the input through the model inputs = tokenizer.encode(args.input, return_tensors="pt") - output = tokenizer.decode(model.generate(inputs, do_sample=True, max_length=25, top_p=0.9, temperature=0.8)[0]) + output = tokenizer.decode(model.generate( + inputs, do_sample=True, max_length=25, top_p=0.9, temperature=0.8)[0]) print(output)
• train.py
  • @@ -16,7 +16,8 @@
    # Load and tokenize dataset raw_dataset = load_dataset('text', data_files={'train': args.input}, keep_linebreaks=True) tokenizer = AutoTokenizer.from_pretrained('distilgpt2', use_fast=True) - tokenized_dataset = raw_dataset.map(lambda examples : tokenizer(examples['text']), batched=True, remove_columns='text') + tokenized_dataset = raw_dataset.map(lambda examples: tokenizer(examples['text']), + batched=True, remove_columns='text') # Generate chunks of block_size
  • @@ -44,6 +45,7 @@
    # Create and train the model model = AutoModelForCausalLM.from_pretrained('distilgpt2') - trainer = Trainer(model, TrainingArguments(output_dir=args.output), default_data_collator, lm_dataset['train']) + trainer = Trainer(model, TrainingArguments(output_dir=args.output), + default_data_collator, lm_dataset['train']) trainer.train() trainer.save_model()