781 lines
29 KiB
Python
781 lines
29 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Very heavily inspired by the official evaluation script for SQuAD version 2.0 which was modified by XLNet authors to
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update `find_best_threshold` scripts for SQuAD V2.0
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In addition to basic functionality, we also compute additional statistics and plot precision-recall curves if an
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additional na_prob.json file is provided. This file is expected to map question ID's to the model's predicted
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probability that a question is unanswerable.
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"""
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import collections
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import json
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import math
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import re
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import string
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from ...models.bert import BasicTokenizer
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from ...utils import logging
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logger = logging.get_logger(__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 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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qas_id = example.qas_id
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gold_answers = [answer["text"] for answer in example.answers if normalize_answer(answer["text"])]
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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 qas_id not in preds:
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print(f"Missing prediction for {qas_id}")
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continue
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prediction = preds[qas_id]
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exact_scores[qas_id] = max(compute_exact(a, prediction) for a in gold_answers)
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f1_scores[qas_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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return collections.OrderedDict(
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[
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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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("total", total),
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]
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)
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else:
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total = len(qid_list)
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return collections.OrderedDict(
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[
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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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("total", total),
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]
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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[f"{prefix}_{k}"] = new_eval[k]
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def find_best_thresh_v2(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 i, 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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has_ans_score, has_ans_cnt = 0, 0
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for qid in qid_list:
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if not qid_to_has_ans[qid]:
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continue
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has_ans_cnt += 1
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if qid not in scores:
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continue
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has_ans_score += scores[qid]
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return 100.0 * best_score / len(scores), best_thresh, 1.0 * has_ans_score / has_ans_cnt
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def find_all_best_thresh_v2(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
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best_exact, exact_thresh, has_ans_exact = find_best_thresh_v2(preds, exact_raw, na_probs, qid_to_has_ans)
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best_f1, f1_thresh, has_ans_f1 = find_best_thresh_v2(preds, f1_raw, na_probs, 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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main_eval["has_ans_exact"] = has_ans_exact
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main_eval["has_ans_f1"] = has_ans_f1
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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, qid_to_has_ans):
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best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
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best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, 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, preds, no_answer_probs=None, no_answer_probability_threshold=1.0):
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qas_id_to_has_answer = {example.qas_id: bool(example.answers) for example in examples}
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has_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if has_answer]
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no_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if not has_answer]
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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(
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exact, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold
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)
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f1_threshold = apply_no_ans_threshold(f1, no_answer_probs, qas_id_to_has_answer, 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, f1_threshold, 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, f1_threshold, 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, qas_id_to_has_answer)
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return evaluation
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def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
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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 to the
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# 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 between
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# `pred_text` and `orig_text` to get a character-to-character alignment. This
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# can fail in certain cases in which case we just return `orig_text`.
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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(do_lower_case=do_lower_case)
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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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if verbose_logging:
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logger.info(f"Unable to find text: '{pred_text}' in '{orig_text}'")
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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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if verbose_logging:
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logger.info(f"Length not equal after stripping spaces: '{orig_ns_text}' vs '{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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if verbose_logging:
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logger.info("Couldn't map start position")
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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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if verbose_logging:
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logger.info("Couldn't map end position")
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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_best_indexes(logits, n_best_size):
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"""Get the n-best logits from a list."""
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index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
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best_indexes = []
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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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best_indexes.append(index_and_score[i][0])
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return best_indexes
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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 compute_predictions_logits(
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all_examples,
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all_features,
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all_results,
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n_best_size,
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max_answer_length,
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do_lower_case,
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output_prediction_file,
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output_nbest_file,
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output_null_log_odds_file,
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verbose_logging,
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version_2_with_negative,
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null_score_diff_threshold,
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tokenizer,
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):
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"""Write final predictions to the json file and log-odds of null if needed."""
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if output_prediction_file:
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logger.info(f"Writing predictions to: {output_prediction_file}")
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if output_nbest_file:
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logger.info(f"Writing nbest to: {output_nbest_file}")
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if output_null_log_odds_file and version_2_with_negative:
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logger.info(f"Writing null_log_odds to: {output_null_log_odds_file}")
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example_index_to_features = collections.defaultdict(list)
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for feature in all_features:
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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 all_results:
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unique_id_to_result[result.unique_id] = result
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_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
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"PrelimPrediction", ["feature_index", "start_index", "end_index", "start_logit", "end_logit"]
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)
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all_predictions = collections.OrderedDict()
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all_nbest_json = collections.OrderedDict()
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scores_diff_json = collections.OrderedDict()
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for example_index, example in enumerate(all_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 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_best_indexes(result.start_logits, n_best_size)
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end_indexes = _get_best_indexes(result.end_logits, n_best_size)
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# if we could have irrelevant answers, get the min score of irrelevant
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if version_2_with_negative:
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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, e.g., predict
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# that the start of the span is in the question. We throw out all
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# 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.token_to_orig_map:
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continue
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if end_index not in feature.token_to_orig_map:
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continue
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if not feature.token_is_max_context.get(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_length:
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continue
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prelim_predictions.append(
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_PrelimPrediction(
|
|
feature_index=feature_index,
|
|
start_index=start_index,
|
|
end_index=end_index,
|
|
start_logit=result.start_logits[start_index],
|
|
end_logit=result.end_logits[end_index],
|
|
)
|
|
)
|
|
if version_2_with_negative:
|
|
prelim_predictions.append(
|
|
_PrelimPrediction(
|
|
feature_index=min_null_feature_index,
|
|
start_index=0,
|
|
end_index=0,
|
|
start_logit=null_start_logit,
|
|
end_logit=null_end_logit,
|
|
)
|
|
)
|
|
prelim_predictions = sorted(prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True)
|
|
|
|
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
|
|
"NbestPrediction", ["text", "start_logit", "end_logit"]
|
|
)
|
|
|
|
seen_predictions = {}
|
|
nbest = []
|
|
for pred in prelim_predictions:
|
|
if len(nbest) >= n_best_size:
|
|
break
|
|
feature = features[pred.feature_index]
|
|
if pred.start_index > 0: # this is a non-null prediction
|
|
tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
|
|
orig_doc_start = feature.token_to_orig_map[pred.start_index]
|
|
orig_doc_end = feature.token_to_orig_map[pred.end_index]
|
|
orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
|
|
|
|
tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
|
|
|
|
# tok_text = " ".join(tok_tokens)
|
|
#
|
|
# # De-tokenize WordPieces that have been split off.
|
|
# tok_text = tok_text.replace(" ##", "")
|
|
# tok_text = tok_text.replace("##", "")
|
|
|
|
# Clean whitespace
|
|
tok_text = tok_text.strip()
|
|
tok_text = " ".join(tok_text.split())
|
|
orig_text = " ".join(orig_tokens)
|
|
|
|
final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
|
|
if final_text in seen_predictions:
|
|
continue
|
|
|
|
seen_predictions[final_text] = True
|
|
else:
|
|
final_text = ""
|
|
seen_predictions[final_text] = True
|
|
|
|
nbest.append(_NbestPrediction(text=final_text, start_logit=pred.start_logit, end_logit=pred.end_logit))
|
|
# if we didn't include the empty option in the n-best, include it
|
|
if version_2_with_negative:
|
|
if "" not in seen_predictions:
|
|
nbest.append(_NbestPrediction(text="", start_logit=null_start_logit, end_logit=null_end_logit))
|
|
|
|
# In very rare edge cases we could only have single null prediction.
|
|
# So we just create a nonce prediction in this case to avoid failure.
|
|
if len(nbest) == 1:
|
|
nbest.insert(0, _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
|
|
|
|
# In very rare edge cases we could have no valid predictions. So we
|
|
# just create a nonce prediction in this case to avoid failure.
|
|
if not nbest:
|
|
nbest.append(_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
|
|
|
|
if len(nbest) < 1:
|
|
raise ValueError("No valid predictions")
|
|
|
|
total_scores = []
|
|
best_non_null_entry = None
|
|
for entry in nbest:
|
|
total_scores.append(entry.start_logit + entry.end_logit)
|
|
if not best_non_null_entry:
|
|
if entry.text:
|
|
best_non_null_entry = entry
|
|
|
|
probs = _compute_softmax(total_scores)
|
|
|
|
nbest_json = []
|
|
for i, entry in enumerate(nbest):
|
|
output = collections.OrderedDict()
|
|
output["text"] = entry.text
|
|
output["probability"] = probs[i]
|
|
output["start_logit"] = entry.start_logit
|
|
output["end_logit"] = entry.end_logit
|
|
nbest_json.append(output)
|
|
|
|
if len(nbest_json) < 1:
|
|
raise ValueError("No valid predictions")
|
|
|
|
if not version_2_with_negative:
|
|
all_predictions[example.qas_id] = nbest_json[0]["text"]
|
|
else:
|
|
# predict "" iff the null score - the score of best non-null > threshold
|
|
score_diff = score_null - best_non_null_entry.start_logit - (best_non_null_entry.end_logit)
|
|
scores_diff_json[example.qas_id] = score_diff
|
|
if score_diff > null_score_diff_threshold:
|
|
all_predictions[example.qas_id] = ""
|
|
else:
|
|
all_predictions[example.qas_id] = best_non_null_entry.text
|
|
all_nbest_json[example.qas_id] = nbest_json
|
|
|
|
if output_prediction_file:
|
|
with open(output_prediction_file, "w") as writer:
|
|
writer.write(json.dumps(all_predictions, indent=4) + "\n")
|
|
|
|
if output_nbest_file:
|
|
with open(output_nbest_file, "w") as writer:
|
|
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
|
|
|
|
if output_null_log_odds_file and version_2_with_negative:
|
|
with open(output_null_log_odds_file, "w") as writer:
|
|
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
|
|
|
|
return all_predictions
|
|
|
|
|
|
def compute_predictions_log_probs(
|
|
all_examples,
|
|
all_features,
|
|
all_results,
|
|
n_best_size,
|
|
max_answer_length,
|
|
output_prediction_file,
|
|
output_nbest_file,
|
|
output_null_log_odds_file,
|
|
start_n_top,
|
|
end_n_top,
|
|
version_2_with_negative,
|
|
tokenizer,
|
|
verbose_logging,
|
|
):
|
|
"""
|
|
XLNet write prediction logic (more complex than Bert's). Write final predictions to the json file and log-odds of
|
|
null if needed.
|
|
|
|
Requires utils_squad_evaluate.py
|
|
"""
|
|
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
|
|
"PrelimPrediction", ["feature_index", "start_index", "end_index", "start_log_prob", "end_log_prob"]
|
|
)
|
|
|
|
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
|
|
"NbestPrediction", ["text", "start_log_prob", "end_log_prob"]
|
|
)
|
|
|
|
logger.info(f"Writing predictions to: {output_prediction_file}")
|
|
|
|
example_index_to_features = collections.defaultdict(list)
|
|
for feature in all_features:
|
|
example_index_to_features[feature.example_index].append(feature)
|
|
|
|
unique_id_to_result = {}
|
|
for result in all_results:
|
|
unique_id_to_result[result.unique_id] = result
|
|
|
|
all_predictions = collections.OrderedDict()
|
|
all_nbest_json = collections.OrderedDict()
|
|
scores_diff_json = collections.OrderedDict()
|
|
|
|
for example_index, example in enumerate(all_examples):
|
|
features = example_index_to_features[example_index]
|
|
|
|
prelim_predictions = []
|
|
# keep track of the minimum score of null start+end of position 0
|
|
score_null = 1000000 # large and positive
|
|
|
|
for feature_index, feature in enumerate(features):
|
|
result = unique_id_to_result[feature.unique_id]
|
|
|
|
cur_null_score = result.cls_logits
|
|
|
|
# if we could have irrelevant answers, get the min score of irrelevant
|
|
score_null = min(score_null, cur_null_score)
|
|
|
|
for i in range(start_n_top):
|
|
for j in range(end_n_top):
|
|
start_log_prob = result.start_logits[i]
|
|
start_index = result.start_top_index[i]
|
|
|
|
j_index = i * end_n_top + j
|
|
|
|
end_log_prob = result.end_logits[j_index]
|
|
end_index = result.end_top_index[j_index]
|
|
|
|
# We could hypothetically create invalid predictions, e.g., predict
|
|
# that the start of the span is in the question. We throw out all
|
|
# invalid predictions.
|
|
if start_index >= feature.paragraph_len - 1:
|
|
continue
|
|
if end_index >= feature.paragraph_len - 1:
|
|
continue
|
|
|
|
if not feature.token_is_max_context.get(start_index, False):
|
|
continue
|
|
if end_index < start_index:
|
|
continue
|
|
length = end_index - start_index + 1
|
|
if length > max_answer_length:
|
|
continue
|
|
|
|
prelim_predictions.append(
|
|
_PrelimPrediction(
|
|
feature_index=feature_index,
|
|
start_index=start_index,
|
|
end_index=end_index,
|
|
start_log_prob=start_log_prob,
|
|
end_log_prob=end_log_prob,
|
|
)
|
|
)
|
|
|
|
prelim_predictions = sorted(
|
|
prelim_predictions, key=lambda x: (x.start_log_prob + x.end_log_prob), reverse=True
|
|
)
|
|
|
|
seen_predictions = {}
|
|
nbest = []
|
|
for pred in prelim_predictions:
|
|
if len(nbest) >= n_best_size:
|
|
break
|
|
feature = features[pred.feature_index]
|
|
|
|
# XLNet un-tokenizer
|
|
# Let's keep it simple for now and see if we need all this later.
|
|
#
|
|
# tok_start_to_orig_index = feature.tok_start_to_orig_index
|
|
# tok_end_to_orig_index = feature.tok_end_to_orig_index
|
|
# start_orig_pos = tok_start_to_orig_index[pred.start_index]
|
|
# end_orig_pos = tok_end_to_orig_index[pred.end_index]
|
|
# paragraph_text = example.paragraph_text
|
|
# final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
|
|
|
|
# Previously used Bert untokenizer
|
|
tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
|
|
orig_doc_start = feature.token_to_orig_map[pred.start_index]
|
|
orig_doc_end = feature.token_to_orig_map[pred.end_index]
|
|
orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
|
|
tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
|
|
|
|
# Clean whitespace
|
|
tok_text = tok_text.strip()
|
|
tok_text = " ".join(tok_text.split())
|
|
orig_text = " ".join(orig_tokens)
|
|
|
|
if hasattr(tokenizer, "do_lower_case"):
|
|
do_lower_case = tokenizer.do_lower_case
|
|
else:
|
|
do_lower_case = tokenizer.do_lowercase_and_remove_accent
|
|
|
|
final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
|
|
|
|
if final_text in seen_predictions:
|
|
continue
|
|
|
|
seen_predictions[final_text] = True
|
|
|
|
nbest.append(
|
|
_NbestPrediction(text=final_text, start_log_prob=pred.start_log_prob, end_log_prob=pred.end_log_prob)
|
|
)
|
|
|
|
# In very rare edge cases we could have no valid predictions. So we
|
|
# just create a nonce prediction in this case to avoid failure.
|
|
if not nbest:
|
|
nbest.append(_NbestPrediction(text="", start_log_prob=-1e6, end_log_prob=-1e6))
|
|
|
|
total_scores = []
|
|
best_non_null_entry = None
|
|
for entry in nbest:
|
|
total_scores.append(entry.start_log_prob + entry.end_log_prob)
|
|
if not best_non_null_entry:
|
|
best_non_null_entry = entry
|
|
|
|
probs = _compute_softmax(total_scores)
|
|
|
|
nbest_json = []
|
|
for i, entry in enumerate(nbest):
|
|
output = collections.OrderedDict()
|
|
output["text"] = entry.text
|
|
output["probability"] = probs[i]
|
|
output["start_log_prob"] = entry.start_log_prob
|
|
output["end_log_prob"] = entry.end_log_prob
|
|
nbest_json.append(output)
|
|
|
|
if len(nbest_json) < 1:
|
|
raise ValueError("No valid predictions")
|
|
if best_non_null_entry is None:
|
|
raise ValueError("No valid predictions")
|
|
|
|
score_diff = score_null
|
|
scores_diff_json[example.qas_id] = score_diff
|
|
# note(zhiliny): always predict best_non_null_entry
|
|
# and the evaluation script will search for the best threshold
|
|
all_predictions[example.qas_id] = best_non_null_entry.text
|
|
|
|
all_nbest_json[example.qas_id] = nbest_json
|
|
|
|
with open(output_prediction_file, "w") as writer:
|
|
writer.write(json.dumps(all_predictions, indent=4) + "\n")
|
|
|
|
with open(output_nbest_file, "w") as writer:
|
|
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
|
|
|
|
if version_2_with_negative:
|
|
with open(output_null_log_odds_file, "w") as writer:
|
|
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
|
|
|
|
return all_predictions
|