System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: Our results of operations depend to a great extent on our net interest and loan related income, which is related to the difference between the interest rates earned on interest-earning assets such as loans and investment securities, and the interest rates paid on interest-bearing liabilities such as borrowings under our credit facilities. We are exposed to interest rate risk because our interest-earning assets and interest-bearing liabilities do not react uniformly or concurrently to changes in interest rates. The interest rates of borrowings under some of our credit facilities are based on floating interest rates and are sensitive to factors that are beyond our control, including domestic and international economic conditions and the policies of various governmental and regulatory agencies, including the Federal Reserve. The monetary policies of the Federal Reserve, implemented through open market operations, the federal funds rate targets, the discount rate for banking borrowings and reserve requirements, affect prevailing interest rates. For instance, between August 2019 and March 2020, the Federal Open Market Committee of the Federal Reserve Board decreased its target range for short-term interest rates by 200 basis points, while between March 2022 and December 2023, it raised interest rates by 525 basis points and indicated that further changes may occur in 2024. A material change in any of these policies could affect the cost of borrowings under our credit facilities which in turn could have an adverse effect on our business, results of operations and financial condition.
Assistant: