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model.py

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+from datasets import load_dataset
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+import tensorflow as tf
+
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
+
+dataset = load_dataset('imdb')
+train_data = dataset['train']
+test_data = dataset['test']
+
+tokenized_train = tokenizer(train_data['text'], truncation=True, padding=True, return_tensors="tf")
+tokenized_test = tokenizer(test_data['text'], truncation=True, padding=True, return_tensors="tf")
+
+outputs = model(**tokenized_test)
+logits = outputs.logits
+pred = tf.argmax(logits, axis=-1)