Delete coqa.py
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coqa.py
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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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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"""CoQA dataset.
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This `CoQA` adds the "additional_answers" feature that's missing in the original
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datasets version:
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https://github.com/huggingface/datasets/blob/master/datasets/coqa/coqa.py
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"""
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import json
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import datasets
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_CITATION = """\
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@misc{reddy2018coqa,
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title={CoQA: A Conversational Question Answering Challenge},
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author={Siva Reddy and Danqi Chen and Christopher D. Manning},
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year={2018},
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eprint={1808.07042},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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"""
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_DESCRIPTION = """\
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CoQA is a large-scale dataset for building Conversational Question Answering
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systems. The goal of the CoQA challenge is to measure the ability of machines to
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understand a text passage and answer a series of interconnected questions that
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appear in a conversation.
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"""
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_HOMEPAGE = "https://stanfordnlp.github.io/coqa/"
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_LICENSE = "Different licenses depending on the content (see https://stanfordnlp.github.io/coqa/ for details)"
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_URLS = {
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"train": "https://downloads.cs.stanford.edu/nlp/data/coqa/coqa-train-v1.0.json",
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"validation": "https://downloads.cs.stanford.edu/nlp/data/coqa/coqa-dev-v1.0.json",
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}
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# `additional_answers` are not available in the train set so we fill them with
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# empty dicts of the same form.
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_EMPTY_ADDITIONAL_ANSWER = {
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"0": [
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{
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"span_start": -1,
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"span_end": -1,
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"span_text": "",
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"input_text": "",
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"turn_id": -1,
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}
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],
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"1": [
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{
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"span_start": -1,
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"span_end": -1,
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"span_text": "",
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"input_text": "",
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"turn_id": -1,
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}
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],
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"2": [
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{
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"span_start": -1,
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"span_end": -1,
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"span_text": "",
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"input_text": "",
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"turn_id": -1,
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}
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],
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}
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class Coqa(datasets.GeneratorBasedBuilder):
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"""CoQA is a large-scale dataset for building Conversational Question Answering systems."""
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VERSION = datasets.Version("0.0.1")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name="coqa", version=VERSION, description="The CoQA dataset."
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),
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]
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def _info(self):
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features = datasets.Features(
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{
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"id": datasets.Value("string"),
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"source": datasets.Value("string"),
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"story": datasets.Value("string"),
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"questions": datasets.features.Sequence(
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{
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"input_text": datasets.Value("string"),
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"turn_id": datasets.Value("int32"),
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}
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),
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"answers": datasets.features.Sequence(
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{
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"span_start": datasets.Value("int32"),
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"span_end": datasets.Value("int32"),
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"span_text": datasets.Value("string"),
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"input_text": datasets.Value("string"),
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"turn_id": datasets.Value("int32"),
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}
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),
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"additional_answers": {
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"0": datasets.features.Sequence(
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{
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"span_start": datasets.Value("int32"),
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"span_end": datasets.Value("int32"),
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"span_text": datasets.Value("string"),
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"input_text": datasets.Value("string"),
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"turn_id": datasets.Value("int32"),
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}
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),
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"1": datasets.features.Sequence(
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{
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"span_start": datasets.Value("int32"),
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"span_end": datasets.Value("int32"),
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"span_text": datasets.Value("string"),
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"input_text": datasets.Value("string"),
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"turn_id": datasets.Value("int32"),
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}
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),
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"2": datasets.features.Sequence(
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{
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"span_start": datasets.Value("int32"),
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"span_end": datasets.Value("int32"),
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"span_text": datasets.Value("string"),
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"input_text": datasets.Value("string"),
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"turn_id": datasets.Value("int32"),
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}
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),
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},
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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urls = {"train": _URLS["train"], "validation": _URLS["validation"]}
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data_dirs = dl_manager.download_and_extract(urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": data_dirs["train"],
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"split": datasets.Split.TRAIN,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": data_dirs["validation"],
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"split": datasets.Split.VALIDATION,
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},
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),
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]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, filepath, split):
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with open(filepath, encoding="utf-8") as f:
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data = json.load(f)
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for row in data["data"]:
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id = row["id"]
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source = row["source"]
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story = row["story"]
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questions = [
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{"input_text": q["input_text"], "turn_id": q["turn_id"]}
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for q in row["questions"]
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]
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answers = [
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{
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"span_start": a["span_start"],
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"span_end": a["span_end"],
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"span_text": a["span_text"],
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"input_text": a["input_text"],
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"turn_id": a["turn_id"],
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}
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for a in row["answers"]
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]
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if split == datasets.Split.TRAIN:
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additional_answers = _EMPTY_ADDITIONAL_ANSWER
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else:
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additional_answers = {
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"0": [
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{
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"span_start": a0["span_start"],
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"span_end": a0["span_end"],
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"span_text": a0["span_text"],
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"input_text": a0["input_text"],
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"turn_id": a0["turn_id"],
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}
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for a0 in row["additional_answers"]["0"]
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],
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"1": [
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{
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"span_start": a1["span_start"],
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"span_end": a1["span_end"],
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"span_text": a1["span_text"],
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"input_text": a1["input_text"],
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"turn_id": a1["turn_id"],
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}
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for a1 in row["additional_answers"]["1"]
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],
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"2": [
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{
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"span_start": a2["span_start"],
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"span_end": a2["span_end"],
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"span_text": a2["span_text"],
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"input_text": a2["input_text"],
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"turn_id": a2["turn_id"],
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}
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for a2 in row["additional_answers"]["2"]
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],
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}
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yield row["id"], {
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"id": id,
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"story": story,
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"source": source,
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"questions": questions,
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"answers": answers,
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"additional_answers": additional_answers,
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}
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