Update README.md

README.md

@@ -37,55 +37,55 @@ It achieves the following results on the evaluation set:
 ## Usage Example
 
 ```python
-  from transformers import BertTokenizer, BertForSequenceClassification, TrainingArguments, Trainer, DataCollatorWithPadding
-  import datasets
-
-  model = BertForSequenceClassification.from_pretrained('VityaVitalich/bert-tiny-sst2')
-  tokenizer = BertTokenizer.from_pretrained('M-FAC/bert-tiny-finetuned-sst2')
-
-  def create_data(tokenizer):
-    train_set = datasets.load_dataset('sst2', split='train').remove_columns(['idx'])
-    val_set = datasets.load_dataset('sst2', split='validation').remove_columns(['idx'])
-
-    def tokenize_func(examples):
-        return tokenizer(examples["sentence"], max_length=128, padding='max_length', truncation=True)
-
-    encoded_dataset_train = train_set.map(tokenize_func, batched=True)
-    encoded_dataset_test = val_set.map(tokenize_func, batched=True)
-    data_collator = DataCollatorWithPadding(tokenizer)
-    return encoded_dataset_train, encoded_dataset_test, data_collator
-
-  encoded_dataset_train, encoded_dataset_test, data_collator = create_data(tokenizer)
-
-  training_args = TrainingArguments(
-      output_dir='./results',
-      learning_rate=3e-5,
-      per_device_train_batch_size=128,
-      per_device_eval_batch_size=128,
-      load_best_model_at_end=True,
-      num_train_epochs=5,
-      weight_decay=0.1,
-      fp16=True,
-      fp16_full_eval=True,
-      evaluation_strategy="epoch",
-      seed=42,
-      save_strategy = "epoch",
-      save_total_limit=5,
-      logging_strategy="epoch",
-      report_to="all",
-  )
-  
-  
-  trainer = Trainer(
-      model=model,
-      args=training_args,
-      train_dataset=encoded_dataset_train,
-      eval_dataset=encoded_dataset_test,
-      data_collator=data_collator,
-      compute_metrics=compute_metrics,
-  )
-
-  trainer.evaluate(encoded_dataset_test)
+from transformers import BertTokenizer, BertForSequenceClassification, TrainingArguments, Trainer, DataCollatorWithPadding
+import datasets
+
+model = BertForSequenceClassification.from_pretrained('VityaVitalich/bert-tiny-sst2')
+tokenizer = BertTokenizer.from_pretrained('M-FAC/bert-tiny-finetuned-sst2')
+
+def create_data(tokenizer):
+  train_set = datasets.load_dataset('sst2', split='train').remove_columns(['idx'])
+  val_set = datasets.load_dataset('sst2', split='validation').remove_columns(['idx'])
+
+  def tokenize_func(examples):
+      return tokenizer(examples["sentence"], max_length=128, padding='max_length', truncation=True)
+
+  encoded_dataset_train = train_set.map(tokenize_func, batched=True)
+  encoded_dataset_test = val_set.map(tokenize_func, batched=True)
+  data_collator = DataCollatorWithPadding(tokenizer)
+  return encoded_dataset_train, encoded_dataset_test, data_collator
+
+encoded_dataset_train, encoded_dataset_test, data_collator = create_data(tokenizer)
+
+training_args = TrainingArguments(
+    output_dir='./results',
+    learning_rate=3e-5,
+    per_device_train_batch_size=128,
+    per_device_eval_batch_size=128,
+    load_best_model_at_end=True,
+    num_train_epochs=5,
+    weight_decay=0.1,
+    fp16=True,
+    fp16_full_eval=True,
+    evaluation_strategy="epoch",
+    seed=42,
+    save_strategy = "epoch",
+    save_total_limit=5,
+    logging_strategy="epoch",
+    report_to="all",
+)
+
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=encoded_dataset_train,
+    eval_dataset=encoded_dataset_test,
+    data_collator=data_collator,
+    compute_metrics=compute_metrics,
+)
+
+trainer.evaluate(encoded_dataset_test)
 ```