537 lines
19 KiB
Python
537 lines
19 KiB
Python
import math
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import collections
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import string
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import re
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import logging
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from federatedscope.register import register_metric
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logger = logging.getLogger(__name__)
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def normalize_answer(s):
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'''Lower text and remove punctuation, articles and extra whitespace.'''
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def remove_articles(text):
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regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
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return re.sub(regex, ' ', text)
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def white_space_fix(text):
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return ' '.join(text.split())
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def remove_punc(text):
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exclude = set(string.punctuation)
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return ''.join(ch for ch in text if ch not in exclude)
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def lower(text):
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return text.lower()
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return white_space_fix(remove_articles(remove_punc(lower(s))))
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def get_tokens(s):
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if not s:
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return []
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return normalize_answer(s).split()
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def compute_exact(a_gold, a_pred):
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return int(normalize_answer(a_gold) == normalize_answer(a_pred))
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def compute_f1(a_gold, a_pred):
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gold_toks = get_tokens(a_gold)
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pred_toks = get_tokens(a_pred)
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common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
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num_same = sum(common.values())
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if len(gold_toks) == 0 or len(pred_toks) == 0:
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# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
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return int(gold_toks == pred_toks)
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if num_same == 0:
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return 0
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precision = 1.0 * num_same / len(pred_toks)
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recall = 1.0 * num_same / len(gold_toks)
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f1 = (2 * precision * recall) / (precision + recall)
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return f1
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def get_raw_scores(examples, preds):
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'''
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Computes the exact and f1 scores from the examples and the model
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predictions
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'''
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exact_scores = {}
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f1_scores = {}
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for example in examples:
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qa_id = example.qa_id
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gold_answers = [
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answer['text'] for answer in example.val_answer
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if normalize_answer(answer['text'])
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]
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if not gold_answers:
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# For unanswerable questions, only correct answer is empty string
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gold_answers = ['']
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if qa_id not in preds:
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print('Missing prediction for %s' % qa_id)
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continue
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prediction = preds[qa_id]
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exact_scores[qa_id] = max(
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compute_exact(a, prediction) for a in gold_answers)
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f1_scores[qa_id] = max(compute_f1(a, prediction) for a in gold_answers)
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return exact_scores, f1_scores
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def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
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new_scores = {}
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for qid, s in scores.items():
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pred_na = na_probs[qid] > na_prob_thresh
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if pred_na:
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new_scores[qid] = float(not qid_to_has_ans[qid])
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else:
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new_scores[qid] = s
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return new_scores
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def make_eval_dict(exact_scores, f1_scores, qid_list=None):
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if not qid_list:
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total = len(exact_scores)
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exact = 100.0 * sum(exact_scores.values()) / total
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f1 = 100.0 * sum(f1_scores.values()) / total
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return collections.OrderedDict([
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('exact', exact),
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('f1', f1),
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('exact_and_f1', (exact + f1) / 2),
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('total', total),
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])
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else:
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total = len(qid_list)
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exact = 100.0 * sum(exact_scores[k] for k in qid_list) / total
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f1 = 100.0 * sum(f1_scores[k] for k in qid_list) / total
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return collections.OrderedDict([
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('exact', exact),
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('f1', f1),
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('exact_and_f1', (exact + f1) / 2),
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('total', total),
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])
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def merge_eval(main_eval, new_eval, prefix):
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for k in new_eval:
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main_eval['%s_%s' % (prefix, k)] = new_eval[k]
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def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
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num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
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cur_score = num_no_ans
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best_score = cur_score
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best_thresh = 0.0
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qid_list = sorted(na_probs, key=lambda k: na_probs[k])
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for _, qid in enumerate(qid_list):
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if qid not in scores:
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continue
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if qid_to_has_ans[qid]:
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diff = scores[qid]
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else:
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if preds[qid]:
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diff = -1
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else:
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diff = 0
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cur_score += diff
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if cur_score > best_score:
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best_score = cur_score
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best_thresh = na_probs[qid]
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return 100.0 * best_score / len(scores), best_thresh
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def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs,
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qid_to_has_ans):
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best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs,
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qid_to_has_ans)
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best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs,
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qid_to_has_ans)
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main_eval['best_exact'] = best_exact
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main_eval['best_exact_thresh'] = exact_thresh
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main_eval['best_f1'] = best_f1
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main_eval['best_f1_thresh'] = f1_thresh
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def squad_evaluate(examples,
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preds,
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no_answer_probs=None,
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no_answer_probability_threshold=1.0):
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qa_id_to_has_answer = {
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example.qa_id: bool(example.val_answer)
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for example in examples
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}
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has_answer_qids = [
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qa_id for qa_id, has_answer in qa_id_to_has_answer.items()
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if has_answer
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]
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no_answer_qids = [
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qa_id for qa_id, has_answer in qa_id_to_has_answer.items()
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if not has_answer
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]
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if no_answer_probs is None:
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no_answer_probs = {k: 0.0 for k in preds}
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exact, f1 = get_raw_scores(examples, preds)
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exact_threshold = apply_no_ans_threshold(exact, no_answer_probs,
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qa_id_to_has_answer,
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no_answer_probability_threshold)
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f1_threshold = apply_no_ans_threshold(f1, no_answer_probs,
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qa_id_to_has_answer,
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no_answer_probability_threshold)
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evaluation = make_eval_dict(exact_threshold, f1_threshold)
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if has_answer_qids:
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has_ans_eval = make_eval_dict(exact_threshold,
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f1_threshold,
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qid_list=has_answer_qids)
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merge_eval(evaluation, has_ans_eval, 'HasAns')
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if no_answer_qids:
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no_ans_eval = make_eval_dict(exact_threshold,
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f1_threshold,
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qid_list=no_answer_qids)
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merge_eval(evaluation, no_ans_eval, 'NoAns')
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if no_answer_probs:
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find_all_best_thresh(evaluation, preds, exact, f1, no_answer_probs,
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qa_id_to_has_answer)
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return evaluation
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def get_final_text(pred_text, orig_text):
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'''Project the tokenized prediction back to the original text.'''
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# When we created the data, we kept track of the alignment between original
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# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
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# now `orig_text` contains the span of our original text corresponding
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# to the span that we predicted.
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#
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# However, `orig_text` may contain extra characters that we don't want in
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# our prediction.
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#
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# For example, let's say:
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# pred_text = steve smith
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# orig_text = Steve Smith's
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#
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# We don't want to return `orig_text` because it contains the extra ''s'.
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#
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# We don't want to return `pred_text` because it's already been normalized
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# (the SQuAD eval script also does punctuation stripping/lower casing but
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# our tokenizer does additional normalization like stripping accent
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# characters).
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#
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# What we really want to return is 'Steve Smith'.
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#
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# Therefore, we have to apply a semi-complicated alignment heuristic
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# between `pred_text` and `orig_text` to get a character-to-character
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# alignment. This can fail in certain cases in which case we just return
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# `orig_text`.
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from transformers import BasicTokenizer
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def _strip_spaces(text):
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ns_chars = []
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ns_to_s_map = collections.OrderedDict()
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for (i, c) in enumerate(text):
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if c == ' ':
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continue
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ns_to_s_map[len(ns_chars)] = i
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ns_chars.append(c)
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ns_text = ''.join(ns_chars)
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return (ns_text, ns_to_s_map)
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# We first tokenize `orig_text`, strip whitespace from the result
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# and `pred_text`, and check if they are the same length. If they are
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# NOT the same length, the heuristic has failed. If they are the same
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# length, we assume the characters are one-to-one aligned.
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tokenizer = BasicTokenizer()
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tok_text = ' '.join(tokenizer.tokenize(orig_text))
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start_position = tok_text.find(pred_text)
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if start_position == -1:
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return orig_text
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end_position = start_position + len(pred_text) - 1
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(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
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(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
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if len(orig_ns_text) != len(tok_ns_text):
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return orig_text
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# We then project the characters in `pred_text` back to `orig_text` using
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# the character-to-character alignment.
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tok_s_to_ns_map = {}
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for (i, tok_index) in tok_ns_to_s_map.items():
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tok_s_to_ns_map[tok_index] = i
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orig_start_position = None
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if start_position in tok_s_to_ns_map:
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ns_start_position = tok_s_to_ns_map[start_position]
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if ns_start_position in orig_ns_to_s_map:
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orig_start_position = orig_ns_to_s_map[ns_start_position]
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if orig_start_position is None:
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return orig_text
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orig_end_position = None
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if end_position in tok_s_to_ns_map:
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ns_end_position = tok_s_to_ns_map[end_position]
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if ns_end_position in orig_ns_to_s_map:
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orig_end_position = orig_ns_to_s_map[ns_end_position]
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if orig_end_position is None:
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return orig_text
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output_text = orig_text[orig_start_position:(orig_end_position + 1)]
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return output_text
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def get_topk_indices(logits, n_best_size):
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index_and_score = sorted(enumerate(logits),
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key=lambda x: x[1],
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reverse=True)
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topk_indices = []
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for i in range(len(index_and_score)):
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if i >= n_best_size:
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break
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topk_indices.append(index_and_score[i][0])
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return topk_indices
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def _compute_softmax(scores):
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'''Compute softmax probability over raw logits.'''
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if not scores:
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return []
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max_score = None
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for score in scores:
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if max_score is None or score > max_score:
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max_score = score
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exp_scores = []
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total_sum = 0.0
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for score in scores:
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x = math.exp(score - max_score)
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exp_scores.append(x)
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total_sum += x
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probs = []
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for score in exp_scores:
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probs.append(score / total_sum)
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return probs
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def create_squad_answer_texts(examples, encoded_inputs, results, n_best_size,
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max_answer_len, null_score_diff_threshold):
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_PrelimPrediction = collections.namedtuple('PrelimPrediction', [
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'feature_index', 'start_index', 'end_index', 'start_logit', 'end_logit'
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])
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_NbestPrediction = collections.namedtuple(
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'NbestPrediction', ['text', 'start_logit', 'end_logit'])
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example_index_to_features = collections.defaultdict(list)
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for feature in encoded_inputs:
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example_index_to_features[feature.example_index].append(feature)
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unique_id_to_result = {}
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for result in results:
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unique_id_to_result[result.unique_id] = result
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predicted_answer_texts = collections.OrderedDict()
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for (example_index, example) in enumerate(examples):
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features = example_index_to_features[example_index]
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prelim_predictions = []
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# keep track of the minimum score of null start+end of position 0
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score_null = 1000000 # large and positive
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min_null_feature_index = 0 # the paragraph slice with min null score
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null_start_logit = 0 # the start logit at the slice with min null
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# score
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null_end_logit = 0 # the end logit at the slice with min null score
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for (feature_index, feature) in enumerate(features):
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result = unique_id_to_result[feature.unique_id]
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start_indexes = get_topk_indices(result.start_logits, n_best_size)
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end_indexes = get_topk_indices(result.end_logits, n_best_size)
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# if we could have irrelevant answers, get the min score of
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# irrelevant
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feature_null_score = result.start_logits[0] + result.end_logits[0]
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if feature_null_score < score_null:
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score_null = feature_null_score
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min_null_feature_index = feature_index
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null_start_logit = result.start_logits[0]
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null_end_logit = result.end_logits[0]
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for start_index in start_indexes:
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for end_index in end_indexes:
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# We could hypothetically create invalid predictions,
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# e.g., predict that the start of the span is in the
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# question. We throw out all invalid predictions.
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if start_index >= len(feature.tokens):
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continue
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if end_index >= len(feature.tokens):
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continue
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if start_index not in feature.context_subtok_to_tok_idx:
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continue
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if end_index not in feature.context_subtok_to_tok_idx:
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continue
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if not feature.is_max_context_token.get(
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start_index, False):
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continue
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if end_index < start_index:
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continue
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length = end_index - start_index + 1
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if length > max_answer_len:
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continue
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prelim_predictions.append(
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_PrelimPrediction(
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feature_index=feature_index,
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start_index=start_index,
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end_index=end_index,
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start_logit=result.start_logits[start_index],
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end_logit=result.end_logits[end_index]))
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prelim_predictions.append(
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_PrelimPrediction(feature_index=min_null_feature_index,
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start_index=0,
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end_index=0,
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start_logit=null_start_logit,
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end_logit=null_end_logit))
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prelim_predictions = sorted(prelim_predictions,
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key=lambda x:
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(x.start_logit + x.end_logit),
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reverse=True)
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seen_predictions = {}
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nbest = []
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for pred in prelim_predictions:
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if len(nbest) >= n_best_size:
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break
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feature = features[pred.feature_index]
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if pred.start_index > 0: # this is a non-null prediction
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tok_tokens = \
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feature.tokens[pred.start_index:(pred.end_index + 1)]
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orig_doc_start = \
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feature.context_subtok_to_tok_idx[pred.start_index]
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orig_doc_end = \
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feature.context_subtok_to_tok_idx[pred.end_index]
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orig_tokens = \
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example.context_tokens[orig_doc_start:(orig_doc_end + 1)]
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tok_text = ' '.join(tok_tokens)
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# De-tokenize WordPieces that have been split off.
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tok_text = tok_text.replace(' ##', '')
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tok_text = tok_text.replace('##', '')
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# Clean whitespace
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tok_text = tok_text.strip()
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tok_text = ' '.join(tok_text.split())
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orig_text = ' '.join(orig_tokens)
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final_text = get_final_text(tok_text, orig_text)
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if final_text in seen_predictions:
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continue
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seen_predictions[final_text] = True
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else:
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final_text = ''
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seen_predictions[final_text] = True
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nbest.append(
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_NbestPrediction(text=final_text,
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start_logit=pred.start_logit,
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end_logit=pred.end_logit))
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# if we didn't include the empty option in the n-best, include it
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if '' not in seen_predictions:
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nbest.append(
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_NbestPrediction(text='',
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start_logit=null_start_logit,
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end_logit=null_end_logit))
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# In very rare edge cases we could only have single null prediction.
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# So we just create a nonce prediction in this case to avoid failure.
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if len(nbest) == 1:
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nbest.insert(
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0,
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_NbestPrediction(text='empty', start_logit=0.0, end_logit=0.0))
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# In very rare edge cases we could have no valid predictions. So we
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# just create a nonce prediction in this case to avoid failure.
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if not nbest:
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nbest.append(
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_NbestPrediction(text='empty', start_logit=0.0, end_logit=0.0))
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total_scores = []
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best_non_null_entry = None
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for entry in nbest:
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total_scores.append(entry.start_logit + entry.end_logit)
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if not best_non_null_entry:
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if entry.text:
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best_non_null_entry = entry
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score_diff = \
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score_null - best_non_null_entry.start_logit - \
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best_non_null_entry.end_logit
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if score_diff > null_score_diff_threshold:
|
|
predicted_answer_texts[example.qa_id] = ''
|
|
else:
|
|
predicted_answer_texts[example.qa_id] = best_non_null_entry.text
|
|
|
|
return predicted_answer_texts
|
|
|
|
|
|
def compute_squad_metrics(examples,
|
|
encoded_inputs,
|
|
results,
|
|
n_best_size,
|
|
max_answer_len,
|
|
null_score_diff_threshold=None,
|
|
return_text=False):
|
|
predicted_answer_texts = create_squad_answer_texts(
|
|
examples, encoded_inputs, results, n_best_size, max_answer_len,
|
|
null_score_diff_threshold)
|
|
raw_metrics = squad_evaluate(examples, predicted_answer_texts)
|
|
metrics = {
|
|
k: v
|
|
for k, v in raw_metrics.items() if k in ('exact', 'f1', 'exact_and_f1')
|
|
}
|
|
|
|
if return_text:
|
|
return predicted_answer_texts
|
|
return metrics
|
|
|
|
|
|
def load_squad_metrics(ctx, **kwargs):
|
|
examples = ctx.get('{}_examples'.format(ctx.cur_split))
|
|
encoded_inputs = ctx.get('{}_encoded'.format(ctx.cur_split))
|
|
results = ctx.squad_results
|
|
n_best_size = ctx.cfg.model.n_best_size
|
|
max_answer_len = ctx.cfg.model.max_answer_len
|
|
null_score_diff_threshold = ctx.cfg.model.null_score_diff_threshold
|
|
|
|
metrics = compute_squad_metrics(examples, encoded_inputs, results,
|
|
n_best_size, max_answer_len,
|
|
null_score_diff_threshold)
|
|
return metrics
|
|
|
|
|
|
def call_squad_metric(types):
|
|
if 'squad' in types:
|
|
the_larger_the_better = True
|
|
return 'squad', load_squad_metrics, the_larger_the_better
|
|
|
|
|
|
register_metric('squad', call_squad_metric)
|