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Reformat code with autopep8

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This commit is contained in:
Anthony Wang 2022-02-22 17:55:04 -06:00
parent 8dab77d61b
commit f08e5bfc5f
Signed by: a
GPG key ID: BC96B00AEC5F2D76
2 changed files with 7 additions and 5 deletions
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6
bot.py
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@ -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)

6
train.py
View file

@ -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()