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: Included in the commercial real estate loans described above are agricultural real estate loans totaling $185.3 million, or 8.7% of our loan portfolio, net, at June 30, 2020. Agricultural real estate lending involves a greater degree of risk and typically involves larger loans to single borrowers than lending on single-family residences. Payments on agricultural real estate loans are dependent on the profitable operation or management of the farm property securing the loan. The success of the farm may be affected by many factors outside the control of the farm borrower, including adverse weather conditions that prevent the planting of a crop or limit crop yields (such as hail, drought and floods), loss of livestock due to disease or other factors, declines in market prices for agricultural products (both domestically and internationally) and the impact of government regulations (including changes in price supports, subsidies, and environmental regulations). In addition, many farms are dependent on a limited number of key individuals whose injury or death may significantly affect the successful operation of the farm. If the cash flow from a farming operation is diminished, the borrower’s ability to repay the loan may be impaired. The primary agricultural activity in our market areas is livestock, dairy, poultry, rice, timber, soybeans, wheat, melons, corn, and cotton. Accordingly, adverse circumstances affecting these activities could have an adverse effect on our agricultural real estate loan portfolio. Our agricultural real estate lending has grown significantly since June 30, 2010 when these loans totaled $28.3 million, or 6.6% of our loan portfolio, and we intend to continue to grow this portion of our loan portfolio.
Assistant: