import logging import datasets import numpy as np logger = logging.getLogger(__name__) try: import sacrebleu from rouge_score import rouge_scorer, scoring except ImportError as e: raise type(e)( "Required packages not installed. Please install the required packages via `pip install rouge_score sacrebleu`" ) def process_results_mc2(doc, results): lls, is_greedy = zip(*results) # Split on the first `0` as everything before it is true (`1`). split_idx = list(doc["mc2_targets"]["labels"]).index(0) # Compute the normalized probability mass for the correct answer. ll_true, ll_false = lls[:split_idx], lls[split_idx:] p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false)) p_true = p_true / (sum(p_true) + sum(p_false)) accuracy = sum(p_true) # TODO. alternatively # new = (sum(p_true)/p_true) / ((sum(p_true)/p_true) + (sum(p_false)/p_false)) return {"acc": accuracy} # , "mc2_new":new} def process_docs_gen(dataset: datasets.Dataset) -> datasets.Dataset: return dataset.map(preprocess_function) def preprocess_function(examples): def _format_answers(answers): formatted_answers = [] for answer in answers: answer = answer.strip() if len(answer): # Add a period after all answers. if answer[-1] != ".": formatted_answers.append(answer + ".") else: formatted_answers.append(answer) return formatted_answers if "correct_answers" in examples: incorrect_answers = _format_answers(examples["incorrect_answers"]) correct_answers = _format_answers(examples["correct_answers"]) if examples["lang"] == "en" and "I have no comment." not in correct_answers: correct_answers.append("I have no comment.") elif ( examples["lang"] == "ca" and "No tinc cap comentari." not in correct_answers ): correct_answers.append("No tinc cap comentari.") elif ( examples["lang"] == "es" and "No tengo ningún comentario." not in correct_answers ): correct_answers.append("No tengo ningún comentario.") elif examples["lang"] == "eu" and "Iruzkinik ez." not in correct_answers: correct_answers.append("Iruzkinik ez.") elif ( examples["lang"] == "gl" and "Non teño ningún comentario." not in correct_answers ): correct_answers.append("Non teño ningún comentario.") return { "question": examples["question"].strip(), "correct_answers": correct_answers, "incorrect_answers": incorrect_answers, "best_answer": examples["best_answer"], } def process_results_gen(doc, results): completion = results[0] true_refs, false_refs = doc["correct_answers"], doc["incorrect_answers"] all_refs = true_refs + false_refs # Process the sentence-level BLEURT, BLEU, and ROUGE for similarity measures. # # BLEURT # bleurt_scores_true = self.bleurt.compute( # predictions=[completion] * len(true_refs), references=true_refs # )["scores"] # bleurt_scores_false = self.bleurt.compute( # predictions=[completion] * len(false_refs), references=false_refs # )["scores"] # bleurt_correct = max(bleurt_scores_true) # bleurt_incorrect = max(bleurt_scores_false) # bleurt_max = bleurt_correct # bleurt_diff = bleurt_correct - bleurt_incorrect # bleurt_acc = int(bleurt_correct > bleurt_incorrect) # BLEU bleu_scores = [bleu([[ref]], [completion]) for ref in all_refs] bleu_correct = np.nanmax(bleu_scores[: len(true_refs)]) bleu_incorrect = np.nanmax(bleu_scores[len(true_refs) :]) bleu_max = bleu_correct bleu_diff = bleu_correct - bleu_incorrect bleu_acc = int(bleu_correct > bleu_incorrect) # ROUGE-N # rouge_scores = [rouge([ref], [completion]) for ref in all_refs] # # ROUGE-1 # rouge1_scores = [score["rouge1"] for score in rouge_scores] # rouge1_correct = np.nanmax(rouge1_scores[: len(true_refs)]) # rouge1_incorrect = np.nanmax(rouge1_scores[len(true_refs) :]) # rouge1_max = rouge1_correct # rouge1_diff = rouge1_correct - rouge1_incorrect # rouge1_acc = int(rouge1_correct > rouge1_incorrect) # # ROUGE-2 # rouge2_scores = [score["rouge2"] for score in rouge_scores] # rouge2_correct = np.nanmax(rouge2_scores[: len(true_refs)]) # rouge2_incorrect = np.nanmax(rouge2_scores[len(true_refs) :]) # rouge2_max = rouge2_correct # rouge2_diff = rouge2_correct - rouge2_incorrect # rouge2_acc = int(rouge2_correct > rouge2_incorrect) # # ROUGE-L # rougeL_scores = [score["rougeLsum"] for score in rouge_scores] # rougeL_correct = np.nanmax(rougeL_scores[: len(true_refs)]) # rougeL_incorrect = np.nanmax(rougeL_scores[len(true_refs) :]) # rougeL_max = rougeL_correct # rougeL_diff = rougeL_correct - rougeL_incorrect # rougeL_acc = int(rougeL_correct > rougeL_incorrect) return { # "bleurt_max": bleurt_max, # "bleurt_acc": bleurt_acc, # "bleurt_diff": bleurt_diff, "bleu_max": bleu_max, "bleu_acc": bleu_acc, "bleu_diff": bleu_diff, # "rouge1_max": rouge1_max, # "rouge1_acc": rouge1_acc, # "rouge1_diff": rouge1_diff, # "rouge2_max": rouge2_max, # "rouge2_acc": rouge2_acc, # "rouge2_diff": rouge2_diff, # "rougeL_max": rougeL_max, # "rougeL_acc": rougeL_acc, # "rougeL_diff": rougeL_diff, } def bleu(refs, preds): """ Returns `t5` style BLEU scores. See the related implementation: https://github.com/google-research/text-to-text-transfer-transformer/blob/3d10afd51ba97ac29eb66ae701eca274488202f7/t5/evaluation/metrics.py#L41 :param refs: A `list` of `list` of reference `str`s. :param preds: A `list` of predicted `str`s. """ score = sacrebleu.corpus_bleu( preds, refs, smooth_method="exp", smooth_value=0.0, force=False, lowercase=False, tokenize="intl", use_effective_order=False, ).score return score def rouge(refs, preds): """ Returns `t5` style ROUGE scores. See the related implementation: https://github.com/google-research/text-to-text-transfer-transformer/blob/3d10afd51ba97ac29eb66ae701eca274488202f7/t5/evaluation/metrics.py#L68 :param refs: A `list` of reference `strs`. :param preds: A `list` of predicted `strs`. """ rouge_types = ["rouge1", "rouge2", "rougeLsum"] scorer = rouge_scorer.RougeScorer(rouge_types) # Add newlines between sentences to correctly compute `rougeLsum`. def _prepare_summary(summary): summary = summary.replace(" . ", ".\n") return summary # Accumulate confidence intervals. aggregator = scoring.BootstrapAggregator() for ref, pred in zip(refs, preds): ref = _prepare_summary(ref) pred = _prepare_summary(pred) aggregator.add_scores(scorer.score(ref, pred)) result = aggregator.aggregate() return {type: result[type].mid.fmeasure * 100 for type in rouge_types}