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from typing import Dict, Any |
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def form_text(example: Dict[str, Any]) -> Dict[str, Any]: |
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""" |
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Given an example from the glue mnli dataset, generate a prompt version example in the format: |
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mnli hypothesis: {hypothesis} premise: {premise} target: {class_label}<|endoftext|> |
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This format can be used to finetune the model as a Causal Languange Model. |
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""" |
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hypothesis = example['hypothesis'] |
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premise = example['premise'] |
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class_label = ['entailment', 'neutral', 'contradiction'][example['label']] |
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example[ |
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'text'] = f'mnli hypothesis: {hypothesis} premise: {premise} target: {class_label}<|endoftext|>' |
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return example |
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def split_text(example: Dict[str, Any]) -> Dict[str, Any]: |
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""" |
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Given an example in the format |
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mnli hypothesis: {hypothesis} premise: {premise} target: {class_label}<|endoftext|> |
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split it in the prompt to be used for validation (excluding the target) and the class label. |
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""" |
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partition = example['text'].rpartition(' ') |
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example['prompt_text'] = partition[0] |
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example['class_label'] = partition[2].replace('<|endoftext|>', '') |
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return example |
