Update README.md

README.md

@@ -56,24 +56,24 @@ from torch.nn.utils.rnn import pad_sequence
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 
 def tokenize(sample):
-sample["card_name"] = tokenizer(sample["card_name"])["input_ids"]
-sample["type_line"] = tokenizer(sample["type_line"])["input_ids"]
-sample["oracle_text"] = tokenizer(sample["oracle_text"])["input_ids"]
-return sample
+    sample["card_name"] = tokenizer(sample["card_name"])["input_ids"]
+    sample["type_line"] = tokenizer(sample["type_line"])["input_ids"]
+    sample["oracle_text"] = tokenizer(sample["oracle_text"])["input_ids"]
+    return sample
 
 tokenized_dataset = train_dataset.map(tokenize)
 
 def collate_fn(sequences):
-# Pad the sequences to the maximum length in the batch
-card_names = [torch.tensor(sequence['card_name']) for sequence in sequences]
-type_line = [torch.tensor(sequence['type_line']) for sequence in sequences]
-oracle_text = [torch.tensor(sequence['oracle_text']) for sequence in sequences]
+    # Pad the sequences to the maximum length in the batch
+    card_names = [torch.tensor(sequence['card_name']) for sequence in sequences]
+    type_line = [torch.tensor(sequence['type_line']) for sequence in sequences]
+    oracle_text = [torch.tensor(sequence['oracle_text']) for sequence in sequences]
 
-padded_card_name = pad_sequence(card_names, batch_first=True, padding_value=0)
-padded_type_line = pad_sequence(type_line, batch_first=True, padding_value=0)
-padded_oracle_text = pad_sequence(oracle_text, batch_first=True, padding_value=0)
+    padded_card_name = pad_sequence(card_names, batch_first=True, padding_value=0)
+    padded_type_line = pad_sequence(type_line, batch_first=True, padding_value=0)
+    padded_oracle_text = pad_sequence(oracle_text, batch_first=True, padding_value=0)
 
-return {'card_name': padded_card_name, 'type_line': padded_type_line, 'padded_oracle_text': padded_oracle_text}
+    return {'card_name': padded_card_name, 'type_line': padded_type_line, 'padded_oracle_text': padded_oracle_text}
 
 loader = torch.utils.data.DataLoader(tokenized_dataset, collate_fn=collate_fn, batch_size=4)