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