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