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from datasets import load_dataset |
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import pandas as pd |
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def get_data(sample_size): |
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dataset = load_dataset("esnli") |
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df = dataset['train'].to_pandas() |
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esnli_train_df = df.dropna(subset=['hypothesis', 'explanation_1']) |
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prompt_template = """You are an advanced AI trained to understand and explain natural language relationships. I will give you a pair of sentences: a premise and a hypothesis. Your task is to determine the relationship between them and provide a detailed explanation of your reasoning process. The possible relationships are "Entailment," "Contradiction," or "Neutral." |
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Instructions: |
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Read the given premise and hypothesis carefully. |
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Identify the relationship between them based on the following definitions: |
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Entailment: The hypothesis logically follows from the premise. |
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Contradiction: The hypothesis directly contradicts the premise. |
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Neutral: The hypothesis neither logically follows from nor contradicts the premise. |
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Provide the relationship (Entailment, Contradiction, or Neutral). |
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Explain in about ten words your reasoning to justify your conclusion. |
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Example: |
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Premise: "A man is playing a guitar." |
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Hypothesis: "A man is making music." |
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Relationship: Entailment |
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Explanation: Playing guitar inherently involves creating music, fulfilling the hypothesis. |
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Now, try it with the following pair: |
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Premise: "{premise}" |
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Hypothesis: "{hypothesis}" |
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Relationship: |
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""" |
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def generate_prompts(df): |
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prompts = [] |
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for _, row in df.iterrows(): |
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prompt = prompt_template.format(premise=row['premise'], hypothesis=row['hypothesis']) |
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prompts.append({ |
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'question': prompt, |
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'answer': {0: 'Entailment', 1: 'Neutral', 2: 'Contradiction'}[row['label']], |
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'reference_explanation': row['explanation_1'] |
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}) |
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return prompts |
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sample_df = esnli_train_df.sample(n=sample_size, random_state=42) |
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prompts_data = generate_prompts(sample_df) |
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prompts_df = pd.DataFrame(prompts_data) |
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return prompts_df |
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if __name__ == '__main__': |
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sample_size = 5 |
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print(get_data(sample_size)) |
