Reformat code with autopep8

2 files changed, 7 insertions, 5 deletions

diff --git a/bot.py b/bot.py
index 8840a28..6dda1fd 100644
--- a/bot.py
+++ b/bot.py
@@ -6,8 +6,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 
 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 @@ model = AutoModelForCausalLM.from_pretrained(args.model)
 
 # 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)
 
 
diff --git a/train.py b/train.py
index 23422e0..ed6beb9 100644
--- a/train.py
+++ b/train.py
@@ -16,7 +16,8 @@ args = parser.parse_args()
 # 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 @@ lm_dataset = tokenized_dataset.map(group_texts, batched=True)
 
 # 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()