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: We use third-party open-source software in connection with the development and deployment of our software applications and will likely use third-party open-source software in the future. Some open-source licenses require that source code that is developed using open-source software be made available to the public at no cost and that any modifications or derivative works to certain open-source software continue to be licensed under open-source licenses, which in some circumstances could include valuable proprietary code. In some circumstances this could require valuable proprietary code to be made available as open-source software, and may also prohibit charging fees to licensees. While we employ practices designed to monitor our compliance with the licenses of open-source software and try to ensure that we do not use any of the open-source software in a manner that would require us to disclose our proprietary source code or preclude us from charging fees, we cannot guarantee that we will be successful. We cannot guarantee that all open-source software is reviewed prior to use in our platform, or that our developers have not incorporated (and will not in the future incorporate) open-source software into our products and offerings without our knowledge. Furthermore, there are an increasing number of open-source software license types, almost none of which have been tested in a court of law, resulting in a dearth of guidance regarding the proper legal interpretation of such licenses. As a result, there is a risk that open-source software licenses could be construed in a manner that imposes unanticipated conditions or restrictions on our ability to market or provide our products and offerings. If we were to receive a claim of non-compliance with the terms of any of our open-source licenses, we may be required purchase a costly license, to publicly release certain portions of our proprietary source code, limit or prohibit our use of some or all of our software, or expend substantial time and resources to re-engineer some or all of our software. We could also be precluded from charging fees for third-party use of our proprietary code.
Assistant: