def METHOD_NAME(model: ov.Model, validation_dataset: Iterable[Any]) -> float:
    compiled_model = ie.compile_model(model, device_name="CPU")
    output_layer = compiled_model.output(0)
    metric = evaluate.load("glue", TASK_NAME)
    INPUT_NAMES = [x.any_name for x in compiled_model.inputs]
    for batch in validation_dataset:
        inputs = [np.expand_dims(np.asarray(batch[key], dtype=np.int64), 0) for key in INPUT_NAMES]
        outputs = compiled_model(inputs)[output_layer]
        predictions = outputs[0].argmax(axis=-1)
        metric.add_batch(predictions=[predictions], references=[batch["labels"]])
    metrics = metric.compute()
    f1_score = metrics["f1"]
    return f1_score