Update README.md

README.md

@@ -16,23 +16,35 @@ This model is a fine-tuned model of Roberta-large applied on RACE
 
 #### How to use
 
-```pythonimport datasets
-from transformers import RobertaTokenizer, RobertaForMultipleChoice
-tokenizer = RobertaTokenizer.from_pretrained('LIAMF-USP/roberta-large-finetuned-race')
-model = RobertaForMultipleChoice.from_pretrained("LIAMF-USP/roberta-large-finetuned-race")
+```python
+
+import datasets
+from transformers import RobertaTokenizer
+from transformers import  RobertaForMultipleChoice
+
+tokenizer = RobertaTokenizer.from_pretrained(
+'LIAMF-USP/roberta-large-finetuned-race')
+model = RobertaForMultipleChoice.from_pretrained(
+"LIAMF-USP/roberta-large-finetuned-race")
 dataset = datasets.load_dataset(
     "race",
     "all",
     split=["train", "validation", "test"],
 )training_examples = dataset[0]
 evaluation_examples = dataset[1]
-test_examples = dataset[2]example=training_examples[0] example_id = example["example_id"]question = example["question"]
+test_examples = dataset[2]
+
+example=training_examples[0] 
+example_id = example["example_id"]
+question = example["question"]
 context = example["article"]
 options = example["options"]
 label_example = example["answer"]
-label_map = {label: i for i, label in enumerate(["A", "B", "C", "D"])}
+label_map = {label: i 
+    for i, label in enumerate(["A", "B", "C", "D"])}
 choices_inputs = []
-for ending_idx, (_, ending) in enumerate(zip(context, options)):
+for ending_idx, (_, ending) in enumerate(
+                                zip(context, options)):
     if question.find("_") != -1:
         # fill in the banks questions
         question_option = question.replace("_", ending)
@@ -51,8 +63,8 @@ label = label_map[label_example]
 input_ids = [x["input_ids"] for x in choices_inputs]
 attention_mask = (
     [x["attention_mask"] for x in choices_inputs]
-     # as the senteces follow the same structure, just one of them is
-     # necessary to check
+     # as the senteces follow the same structure, 
+     #just one of them is necessary to check
     if "attention_mask" in choices_inputs[0]
     else None
 )