Create transformer.py

transformer.py

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+from transformers import BertTokenizer, TFBertForSequenceClassification
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.losses import SparseCategoricalCrossentropy
+
+# Load pre-trained BERT model and tokenizer
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
+
+# Tokenize sample text
+inputs = tokenizer("Hello, this is a sentiment analysis example.", return_tensors="tf")
+
+# Fine-tuning BERT model
+model.compile(optimizer=Adam(learning_rate=3e-5), loss=SparseCategoricalCrossentropy(from_logits=True))
+model.fit(inputs['input_ids'], [1], epochs=1)  # Dummy training example