squad.py

import datasets
from math import exp
from lm_eval.base import rf
from lm_eval.metrics import f1_score, mean
from . common import HFTask
from functools import partial
from packaging import version


def _squad_metric(predictions, references):
    squad_metric = datasets.load_metric("squad_v2")
    return squad_metric.compute(predictions=predictions, references=references)


def _squad_agg(key, items):
    predictions, references = zip(*items)

    return _squad_metric(predictions=predictions, references=references)[key]


class SQuAD2(HFTask):
    VERSION = 1
    DATASET_PATH = "squad_v2"
    DATASET_NAME = None

    # HF changed squad on us so we have to make sure we aren't running the old one
    assert version.parse(datasets.__version__) >= version.parse("1.11.0"), "datasets v1.11.0 or later required for SQuAD"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return False

    def training_docs(self):
        return self.data["train"]

    def validation_docs(self):
        return self.data["validation"]

    def fewshot_description(self):
        # TODO: figure out description
        return ""

    def doc_to_text(self, doc):
        return 'Title: ' + doc['title'] + '\n\n' + 'Background: ' + doc['context'] + '\n\n' + 'Question: ' + doc['question'] + '\n\n' + 'Answer:'

    def doc_to_target(self, doc):
        answer_list = doc['answers']['text']
        if len(answer_list) > 0:
            answer = answer_list[0]
        else:
            answer = 'unanswerable'
        return " " + answer

    def construct_requests(self, doc, ctx):
        """ Uses RequestFactory to construct Requests and returns an iterable of 
        Requests which will be sent to the LM.

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :param ctx: str
            The context string, generated by fewshot_context. This includes the natural 
            language description, as well as the few shot examples, and the question
            part of the document for `doc`. 
        """
        continuation = rf.greedy_until(ctx, ['\n'])
        is_unanswerable = rf.loglikelihood(ctx, " " + "unanswerable")
        return continuation, is_unanswerable
    
    def process_results(self, doc, results):
        """Take a single document and the LM results and evaluates, returning a 
        dict where keys are the names of submetrics and values are the values of 
        the metric for that one document

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :param results:
            The results of the requests created in construct_requests.
        """
        continuation, (logprob_unanswerable, _) = results

        no_answer_probability = exp(logprob_unanswerable)
        
        predictions = {
            'id': doc['id'],
            'prediction_text': continuation,
            'no_answer_probability': no_answer_probability,
        }

        references = {
            'id': doc['id'],
            'answers': doc['answers'],
        }

        return { 
            'exact': (predictions, references), # Exact match (the normalized answer exactly match the gold answer)
            'f1': (predictions, references), #  The F-score of predicted tokens versus the gold answer
            'HasAns_exact': (predictions, references), # Exact match (the normalized answer exactly match the gold answer)
            'HasAns_f1': (predictions, references), # The F-score of predicted tokens versus the gold answer
            'NoAns_exact': (predictions, references), # Exact match (the normalized answer exactly match the gold answer)
            'NoAns_f1': (predictions, references), # The F-score of predicted tokens versus the gold answer
            'best_exact': (predictions, references), # Best exact match (with varying threshold)
            'best_f1': (predictions, references), # Best F1 (with varying threshold)
        }

    def aggregation(self):
        """
        :returns: {str: [float] -> float}
            A dictionary where keys are the names of submetrics and values are 
            functions that aggregate a list of metrics
        """
        return { 
            'exact': partial(_squad_agg, 'exact'), # Exact match (the normalized answer exactly match the gold answer)
            'f1': partial(_squad_agg, 'f1'), #  The F-score of predicted tokens versus the gold answer
            'HasAns_exact': partial(_squad_agg, 'HasAns_exact'), # Exact match (the normalized answer exactly match the gold answer)
            'HasAns_f1': partial(_squad_agg, 'HasAns_f1'), # The F-score of predicted tokens versus the gold answer
            'NoAns_exact': partial(_squad_agg, 'NoAns_exact'), # Exact match (the normalized answer exactly match the gold answer)
            'NoAns_f1': partial(_squad_agg, 'NoAns_f1'), # The F-score of predicted tokens versus the gold answer
            'best_exact': partial(_squad_agg, 'best_exact'), # Best exact match (with varying threshold)
            'best_f1': partial(_squad_agg, 'best_f1'), # Best F1 (with varying threshold)
        }

    def higher_is_better(self):
        """
        :returns: {str: bool}
            A dictionary where keys are the names of submetrics and values are 
            whether a higher value of the submetric is better
        """
        return { 
            'exact': True, # Exact match (the normalized answer exactly match the gold answer)
            'f1': True, #  The F-score of predicted tokens versus the gold answer
            'HasAns_exact': True, # Exact match (the normalized answer exactly match the gold answer)
            'HasAns_f1': True, # The F-score of predicted tokens versus the gold answer
            'NoAns_exact': True, # Exact match (the normalized answer exactly match the gold answer)
            'NoAns_f1': True, # The F-score of predicted tokens versus the gold answer
            'best_exact': True, # Best exact match (with varying threshold)
            'best_f1': True, # Best F1 (with varying threshold)
        }