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294 lines
9.8 KiB
Python
294 lines
9.8 KiB
Python
#!/usr/bin/python3
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# Copyright 2019 Google LLC
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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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# https://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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r"""
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DEEPSET DOCSTRING:
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A modified version of the script from here:
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https://github.com/google/retrieval-qa-eval/blob/master/nq_to_squad.py
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Edits have been made by deepset in order to create a dev set for Haystack benchmarking.
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Input should be the official NQ dev set (v1.0-simplified-nq-dev-all.jsonl.gz)
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Expected numbers are:
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Converted 7830 NQ records into 5678 SQuAD records.
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Removed samples: yes/no: 177 multi_short: 648 non_para 1192 long_ans_only: 130 errors: 5
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Removed annotations: long_answer: 4610 short_answer: 953 no_answer: ~1006
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where:
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multi_short - annotations where there are multiple disjoint short answers
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non_para - where the annotation occurs in an html element that is not a paragraph
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ORIGINAL DOCSTRING:
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Convert the Natural Questions dataset into SQuAD JSON format.
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To use this utility, first follow the directions at the URL below to download
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the complete training dataset.
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https://ai.google.com/research/NaturalQuestions/download
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Next, run this program, specifying the data you wish to convert. For instance,
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the invocation:
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python nq_to_squad.py\
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--data_pattern=/usr/local/data/tnq/v1.0/train/*.gz\
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--output_file=/usr/local/data/tnq/v1.0/train.json
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will process all training data and write the results into `train.json`. This
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file can, in turn, be provided to squad_eval.py using the --squad argument.
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"""
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import argparse
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import glob
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import gzip
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import json
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import logging
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import os
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import re
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# Dropped samples
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n_yn = 0
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n_ms = 0
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n_non_p = 0
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n_long_ans_only = 0
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n_error = 0
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# Dropped annotations
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n_long_ans = 0
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n_no_ans = 0
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n_short = 0
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def clean_text(start_token, end_token, doc_tokens, doc_bytes, ignore_final_whitespace=True):
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"""Remove HTML tags from a text span and reconstruct proper spacing."""
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text = ""
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for index in range(start_token, end_token):
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token = doc_tokens[index]
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if token["html_token"]:
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continue
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text += token["token"]
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# Add a single space between two tokens iff there is at least one
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# whitespace character between them (outside of an HTML tag). For example:
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#
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# token1 token2 ==> Add space.
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# token1</B> <B>token2 ==> Add space.
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# token1</A>token2 ==> No space.
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# token1<A href="..." title="...">token2 ==> No space.
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# token1<SUP>2</SUP>token2 ==> No space.
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next_token = token
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last_index = end_token if ignore_final_whitespace else end_token + 1
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for next_token in doc_tokens[index + 1 : last_index]:
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if not next_token["html_token"]:
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break
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chars = doc_bytes[token["end_byte"] : next_token["start_byte"]].decode("utf-8")
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# Since some HTML tags are missing from the token list, we count '<' and
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# '>' to detect if we're inside a tag.
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unclosed_brackets = 0
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for char in chars:
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if char == "<":
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unclosed_brackets += 1
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elif char == ">":
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unclosed_brackets -= 1
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elif unclosed_brackets == 0 and re.match(r"\s", char):
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# Add a single space after this token.
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text += " "
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break
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return text
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def get_anno_type(annotation):
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long_answer = annotation["long_answer"]
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short_answers = annotation["short_answers"]
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yes_no_answer = annotation["yes_no_answer"]
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if len(short_answers) > 1:
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return "multi_short"
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elif yes_no_answer != "NONE":
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return yes_no_answer
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elif len(short_answers) == 1:
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return "short_answer"
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elif len(short_answers) == 0:
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if long_answer["start_token"] == -1:
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return "no_answer"
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else:
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return "long_answer"
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def reduce_annotations(anno_types, answers):
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"""
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In cases where there is annotator disagreement, this fn picks either only the short_answers or only the no_answers,
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depending on which is more numerous, with a bias towards picking short_answers.
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Note: By this stage, all long_answer annotations and all samples with yes/no answer have been removed.
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This leaves just no_answer and short_answers"""
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for at in set(anno_types):
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assert at in ("no_answer", "short_answer")
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if anno_types.count("short_answer") >= anno_types.count("no_answer"):
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majority = "short_answer"
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is_impossible = False
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else:
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majority = "no_answer"
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is_impossible = True
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answers = [a for at, a in zip(anno_types, answers) if at == majority]
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reduction = len(anno_types) - len(answers)
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assert reduction < 3
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if not is_impossible:
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global n_no_ans
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n_no_ans += reduction
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else:
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global n_short
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n_short += reduction
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answers = []
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return answers, is_impossible
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def nq_to_squad(record):
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"""Convert a Natural Questions record to SQuAD format."""
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doc_bytes = record["document_html"].encode("utf-8")
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doc_tokens = record["document_tokens"]
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question_text = record["question_text"]
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question_text = question_text[0].upper() + question_text[1:] + "?"
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answers = []
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anno_types = []
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for annotation in record["annotations"]:
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anno_type = get_anno_type(annotation)
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long_answer = annotation["long_answer"]
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short_answers = annotation["short_answers"]
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if anno_type.lower() in ["yes", "no"]:
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global n_yn
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n_yn += 1
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return
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# Skip examples that don't have exactly one short answer.
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# Note: Consider including multi-span short answers.
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if anno_type == "multi_short":
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global n_ms
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n_ms += 1
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return
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elif anno_type == "short_answer":
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short_answer = short_answers[0]
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# Skip examples corresponding to HTML blocks other than <P>.
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long_answer_html_tag = doc_tokens[long_answer["start_token"]]["token"]
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if long_answer_html_tag != "<P>":
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global n_non_p
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n_non_p += 1
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return
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answer = clean_text(short_answer["start_token"], short_answer["end_token"], doc_tokens, doc_bytes)
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before_answer = clean_text(
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0, short_answer["start_token"], doc_tokens, doc_bytes, ignore_final_whitespace=False
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)
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elif anno_type == "no_answer":
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answer = ""
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before_answer = ""
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# Throw out long answer annotations
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elif anno_type == "long_answer":
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global n_long_ans
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n_long_ans += 1
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continue
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anno_types.append(anno_type)
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answer = {"answer_start": len(before_answer), "text": answer}
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answers.append(answer)
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if len(answers) == 0:
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global n_long_ans_only
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n_long_ans_only += 1
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return
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answers, is_impossible = reduce_annotations(anno_types, answers)
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paragraph = clean_text(0, len(doc_tokens), doc_tokens, doc_bytes)
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return {
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"title": record["document_title"],
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"paragraphs": [
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{
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"context": paragraph,
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"qas": [
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{
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"answers": answers,
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"id": record["example_id"],
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"question": question_text,
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"is_impossible": is_impossible,
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}
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],
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}
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],
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}
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def main():
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parser = argparse.ArgumentParser(description="Convert the Natural Questions to SQuAD JSON format.")
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parser.add_argument(
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"--data_pattern",
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dest="data_pattern",
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help=("A file pattern to match the Natural Questions " "dataset."),
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metavar="PATTERN",
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required=True,
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)
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parser.add_argument(
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"--version", dest="version", help="The version label in the output file.", metavar="LABEL", default="nq-train"
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)
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parser.add_argument(
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"--output_file",
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dest="output_file",
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help="The name of the SQuAD JSON formatted output file.",
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metavar="FILE",
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default="nq_as_squad.json",
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)
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args = parser.parse_args()
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root = logging.getLogger()
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root.setLevel(logging.DEBUG)
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records = 0
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nq_as_squad = {"version": args.version, "data": []}
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for file in sorted(glob.iglob(args.data_pattern)):
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logging.info("opening %s", file)
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with gzip.GzipFile(file, "r") as f:
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for line in f:
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records += 1
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nq_record = json.loads(line)
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try:
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squad_record = nq_to_squad(nq_record)
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except:
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squad_record = None
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global n_error
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n_error += 1
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if squad_record:
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nq_as_squad["data"].append(squad_record)
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if records % 100 == 0:
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logging.info("processed %s records", records)
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print("Converted %s NQ records into %s SQuAD records." % (records, len(nq_as_squad["data"])))
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print(
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f"Removed samples: yes/no: {n_yn} multi_short: {n_ms} non_para {n_non_p} long_ans_only: {n_long_ans_only} errors: {n_error}"
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)
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print(f"Removed annotations: long_answer: {n_long_ans} short_answer: {n_short} no_answer: ~{n_no_ans}")
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with open(args.output_file, "w") as f:
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json.dump(nq_as_squad, f, indent=4)
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if __name__ == "__main__":
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main()
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