```python
import argparse
import numpy as np

# Argument parsing
parser = argparse.ArgumentParser(description="Sequence Classification Prediction")
parser.add_argument("--model_dir", type=str, required=True, help="Path to the model directory")
parser.add_argument("--tokenizer_vocab_path", type=str, required=True, help="Path to the tokenizer vocabulary file")
parser.add_argument("--inference_device", type=str, default="cpu", help="Inference device, e.g., cpu, cuda")
parser.add_argument("--runtime_backend", type=str, default="onnx", help="Runtime backend, e.g., onnx, torch")
parser.add_argument("--batch_size", type=int, default=32, help="Batch size for inference")
parser.add_argument("--sequence_length", type=int, default=128, help="Sequence length for input texts")
parser.add_argument("--logging_interval", type=int, default=10, help="Interval for logging progress")
parser.add_argument("--use_fp16", action="store_true", help="Use FP16 mode for inference")
parser.add_argument("--use_fast_tokenizer", action="store_true", help="Use fast tokenizer")
args = parser.parse_args()

def batchify(data, batch_size):
    return [data[i:i + batch_size] for i in range(0, len(data), batch_size)]

class SequenceClassificationPredictor:
    def __init__(self, model_dir, tokenizer_vocab_path, inference_device, runtime_backend):
        self.model_dir = model_dir
        self.tokenizer_vocab_path = tokenizer_vocab_path
        self.inference_device = inference_device
        self.runtime_backend = runtime_backend
        # Initialize tokenizer and runtime based on runtime_backend
        # This is a placeholder for actual initialization
        self.tokenizer = None
        self.runtime = None

    def preprocess(self, texts):
        # Tokenize texts and prepare input tensors
        # This is a placeholder for actual preprocessing
        return np.zeros((len(texts), args.sequence_length))

    def inference(self, input_tensors):
        # Perform model inference
        # This is a placeholder for actual inference
        return np.random.rand(len(input_tensors), 2)  # Dummy output

    def postprocess(self, inference_output):
        # Convert inference output to labels and confidence scores
        # This is a placeholder for actual postprocessing
        return [("label", 0.99) for _ in inference_output]

    def predict(self, texts):
        input_tensors = self.preprocess(texts)
        inference_output = self.inference(input_tensors)
        predictions = self.postprocess(inference_output)
        return predictions

def main():
    predictor = SequenceClassificationPredictor(args.model_dir, args.tokenizer_vocab_path, args.inference_device, args.runtime_backend)
    texts = ["This is a test.", "Another test sentence."]  # Placeholder for actual text input
    batches = batchify(texts, args.batch_size)
    for batch_id, batch in enumerate(batches):
        predictions = predictor.predict(batch)
        for example_id, (text, (label, confidence)) in enumerate(zip(batch, predictions)):
            print(f"Batch {batch_id}, Example {example_id}, Input: '{text}', Predicted Label: {label}, Confidence: {confidence}")

if __name__ == "__main__":
    main()
```