class ChatDataset(Dataset):
def __init__(self, filepath, tok_vocab, max_seq_len=128) -> None:
self.filepath = filepath
self.data = pd.read_csv(self.filepath)
self.bos_token = '<s>'
self.eos_token = '</s>'
self.max_seq_len = max_seq_len
self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=tok_vocab,
bos_token=self.bos_token,
eos_token=self.eos_token,
unk_token='<unk>',
pad_token='<pad>',
mask_token='<mask>')
def __len__(self):
return len(self.data)
def make_input_id_mask(self, tokens, index):
input_id = self.tokenizer.convert_tokens_to_ids(tokens)
attention_mask = [1] * len(input_id)
if len(input_id) < self.max_seq_len:
while len(input_id) < self.max_seq_len:
input_id += [self.tokenizer.pad_token_id]
attention_mask += [0]
else:
# logging.warning(f'exceed max_seq_len for given article : {index}')
input_id = input_id[:self.max_seq_len -
1] + [self.tokenizer.eos_token_id]
attention_mask = attention_mask[:self.max_seq_len]
return input_id, attention_mask
def __getitem__(self, index):
record = self.data.iloc[index]
q, a = record['Q'], record['A']
q_tokens = [self.bos_token] + \
self.tokenizer.tokenize(q) + [self.eos_token]
a_tokens = [self.bos_token] + \
self.tokenizer.tokenize(a) + [self.eos_token]
encoder_input_id, encoder_attention_mask = self.make_input_id_mask(
q_tokens, index)
decoder_input_id, decoder_attention_mask = self.make_input_id_mask(
a_tokens, index)
labels = self.tokenizer.convert_tokens_to_ids(
a_tokens[1:(self.max_seq_len + 1)])
if len(labels) < self.max_seq_len:
while len(labels) < self.max_seq_len:
# for cross entropy loss masking
labels += [-100]
return {
'input_ids':
np.array(encoder_input_id, dtype=np.int_),
'attention_mask':
np.array(encoder_attention_mask, dtype=np.float_),
'decoder_input_ids':
np.array(decoder_input_id, dtype=np.int_),
'decoder_attention_mask':
np.array(decoder_attention_mask, dtype=np.float_),
'labels':
np.array(labels, dtype=np.int_)
}
def main(args):
data = np.load(args.data, allow_pickle=True)
tokenizer_path = args.tokenizer
tokenizer = PreTrainedTokenizerFast(tokenizer_file=tokenizer_path,
max_len=512,
mask_token="<mask>",
pad_token="<pad>")
tokenizer._tokenizer.post_processor = BertProcessing(
("</s>", tokenizer.convert_tokens_to_ids("</s>")),
("<s>", tokenizer.convert_tokens_to_ids("<s>")),
)
config = RobertaConfig(
vocab_size=tokenizer.vocab_size,
max_position_embeddings=514,
num_attention_heads=12,
num_hidden_layers=6,
type_vocab_size=1,
)
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer,
mlm=True,
mlm_probability=0.15)
dataset = PhoneDatasetMLM(data, tokenizer)
model = RobertaForMaskedLM(config=config)
training_args = TrainingArguments(
output_dir=args.output_dir,
overwrite_output_dir=True,
num_train_epochs=1,
per_device_train_batch_size=64,
logging_steps=2,
save_steps=10_000,
save_total_limit=2,
prediction_loss_only=True,
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset,
)
trainer.train()
trainer.save_model(args.output_dir)