sst2.xml

<schema name="sst2">
    <system>
        #### Task Description ####
        You are tasked with binary sentiment analysis. Analyze the provided examples carefully to understand the task. Each example consists of a sentence and its corresponding sentiment label. The label `1` represents positive sentiment, and the label `0` represents negative sentiment. After reviewing the examples, classify the LAST sentence based on its sentiment. Respond with a single word, either `0` or `1`.

        #### Response Format ####
        Respond in a JSON format with the following structure:
        {"label": sentiment_label}]
        Do NOT provide any explanations or additional text. Do NOT include the labels of the examples in your response.

        #### Examples ####
        Here are a few example interactions:
    </system>
    <user>
    </user>

  <module name="module_1">
    Sentence: hide new secretions from the parental units
    Label: 0
  </module>

  <module name="module_2">
    Sentence: contains no wit , only labored gags
    Label: 0
  </module>

  <module name="module_3">
    Sentence: that loves its characters and communicates something rather beautiful about human nature
    Label: 1
  </module>

  <module name="module_4">
    Sentence: remains utterly satisfied to remain the same throughout
    Label: 0
  </module>

  <module name="module_5">
    Sentence: on the worst revenge-of-the-nerds clichés the filmmakers could dredge up
    Label: 0
  </module>

  <module name="module_6">
    Sentence: that 's far too tragic to merit such superficial treatment
    Label: 0
  </module>

  <module name="module_7">
    Sentence: demonstrates that the director of such hollywood blockbusters as patriot games can still turn out a small , personal film with an emotional wallop .
    Label: 1
  </module>

  <module name="module_8">
    Sentence: of saucy
    Label: 1
  </module>

  <module name="module_9">
    Sentence: a depressed fifteen-year-old 's suicidal poetry
    Label: 0
  </module>

  <module name="module_10">
    Sentence: are more deeply thought through than in most ` right-thinking ' films
    Label: 1
  </module>
</schema>