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: In the event that our loan customers do not repay their loans according to their terms, and the collateral securing the repayment of these loans is insufficient to cover any remaining loan balance, including expenses of collecting the loan and managing and liquidating the collateral, we could experience significant loan losses or increase our provision for loan losses or both, which could have a material adverse effect on our operating results. At December 31, 2021, our allowance for loan losses was $6.7 million, which represented 0.64% of total loans and 895.33% of nonperforming loans as of that date. In determining the amount of our allowance for loan losses, we rely on internal and external loan reviews, our historical experience and our evaluation of economic conditions, among other factors. In addition, we make various estimates and assumptions about the collectability of our loan portfolio, including the creditworthiness of our borrowers and the value of the real estate and other assets, if any, serving as collateral for the repayment of our loans. We also make judgments concerning our legal positions and the priority of our liens and security interests in contested legal or bankruptcy proceedings, and at times, we may lack sufficient information to establish adequate specific reserves for loans involved in such proceedings. We base these estimates, assumptions and judgments on information that we consider reliable, but if an estimate, assumption or judgment that we make ultimately proves to be incorrect, additional provisions to our allowance for loan losses may become necessary. In addition, our emphasis on loan and lease growth and on increasing our portfolios of commercial business loans, as well as any future credit deterioration, including as a result of COVID-19, could require us to increase our allowance for loan losses in the future. In addition, as an integral part of their supervisory and/or examination process, the OCC periodically reviews the methodology for and the sufficiency of the allowance for loan losses. The OCC has the authority to require us to recognize additions to the allowance based on their inclusion, exclusion or modification of risk factors or differences in judgments of information available to them at the time of their examination.
Assistant: