Datasets:
Upload DiscoEval.py
Browse files- DiscoEval.py +98 -99
DiscoEval.py
CHANGED
@@ -15,7 +15,6 @@
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import os
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import io
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import datasets
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-
import DiscoEvalConstants
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import pickle
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import logging
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@@ -221,135 +220,135 @@ class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.1.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name=
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version=VERSION,
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description="Sentence positioning dataset from arXiv",
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),
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datasets.BuilderConfig(
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name=
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version=VERSION,
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description="Sentence positioning dataset from ROCStory",
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),
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datasets.BuilderConfig(
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name=
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version=VERSION,
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description="Sentence positioning dataset from Wikipedia",
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),
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datasets.BuilderConfig(
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name=
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version=VERSION,
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description="Discourse Coherence dataset from chat",
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),
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datasets.BuilderConfig(
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name=
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version=VERSION,
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description="Discourse Coherence dataset from Wikipedia",
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),
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datasets.BuilderConfig(
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name=
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version=VERSION,
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description="The RST Discourse Treebank dataset ",
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),
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datasets.BuilderConfig(
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name=
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version=VERSION,
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description="The Penn Discourse Treebank - Explicit dataset.",
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),
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datasets.BuilderConfig(
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name=
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version=VERSION,
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description="The Penn Discourse Treebank - Implicit dataset.",
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),
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datasets.BuilderConfig(
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name=
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version=VERSION,
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description="The SSP dataset.",
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),
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datasets.BuilderConfig(
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name=
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version=VERSION,
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description="The BSO Task with the arxiv dataset.",
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),
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datasets.BuilderConfig(
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name=
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version=VERSION,
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description="The BSO Task with the wiki dataset.",
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),
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datasets.BuilderConfig(
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name=
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version=VERSION,
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description="The BSO Task with the rocstory dataset.",
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),
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]
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def _info(self):
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if self.config.name in [
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features_dict = {
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for i in range(
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}
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features_dict[
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names=list(
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)
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features = datasets.Features(features_dict)
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elif self.config.name in [
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features_dict = {
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-
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for i in range(
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}
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features_dict[
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names=list(
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)
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features = datasets.Features(features_dict)
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elif self.config.name in [
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features_dict = {
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-
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for i in range(
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}
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features_dict[
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names=list(
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)
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features = datasets.Features(features_dict)
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elif self.config.name in [
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features_dict = {
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-
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for i in range(
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}
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features_dict[
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names=
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)
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features = datasets.Features(features_dict)
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elif self.config.name in [
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features_dict = {
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-
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for i in range(
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}
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features_dict[
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names=
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)
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features = datasets.Features(features_dict)
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elif self.config.name in [
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features_dict = {
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-
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for i in range(
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}
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features_dict[
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names=
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)
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features = datasets.Features(features_dict)
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elif self.config.name in [
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features_dict = {
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-
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for i in range(
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}
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features_dict[
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names=list(
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)
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features = datasets.Features(features_dict)
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@@ -361,41 +360,41 @@ class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
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)
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def _split_generators(self, dl_manager):
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if self.config.name in [
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data_dir =
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train_name =
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-
valid_name =
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test_name =
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-
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elif self.config.name in [
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-
data_dir =
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-
train_name =
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-
valid_name =
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-
test_name =
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-
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-
elif self.config.name in [
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-
data_dir =
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-
train_name =
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-
valid_name =
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-
test_name =
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-
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elif self.config.name in [
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-
data_dir =
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-
train_name =
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-
valid_name =
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-
test_name =
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-
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-
elif self.config.name in [
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data_dir = os.path.join(
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-
train_name =
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-
valid_name =
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-
test_name =
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-
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-
elif self.config.name in [
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-
data_dir =
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-
train_name =
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-
valid_name =
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-
test_name =
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urls_to_download = {
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"train": data_dir + "/" + train_name,
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@@ -436,28 +435,28 @@ class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
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logger = logging.getLogger(__name__)
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logger.info(f"Current working dir: {os.getcwd()}")
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logger.info("generating examples from = %s", filepath)
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if self.config.name ==
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data = pickle.load(open(filepath, "rb"))
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for key, line in enumerate(data):
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-
example = {
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example[
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yield key, example
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else:
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with io.open(filepath, mode='r', encoding='utf-8') as f:
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for key, line in enumerate(f):
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line = line.strip().split("\t")
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-
example = {
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-
if self.config.name ==
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-
example[
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-
if self.config.name ==
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-
example[
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-
elif self.config.name in (
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-
example[
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-
elif self.config.name ==
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-
example[
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elif self.config.name in (
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-
example[
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elif self.config.name in (
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-
example[
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yield key, example
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import os
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import io
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import datasets
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import pickle
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import logging
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VERSION = datasets.Version("1.1.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name=SPARXIV,
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version=VERSION,
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description="Sentence positioning dataset from arXiv",
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),
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datasets.BuilderConfig(
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name=SPROCSTORY,
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version=VERSION,
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description="Sentence positioning dataset from ROCStory",
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),
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datasets.BuilderConfig(
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name=SPWIKI,
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version=VERSION,
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description="Sentence positioning dataset from Wikipedia",
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),
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datasets.BuilderConfig(
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name=DCCHAT,
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version=VERSION,
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description="Discourse Coherence dataset from chat",
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),
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datasets.BuilderConfig(
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name=DCWIKI,
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version=VERSION,
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description="Discourse Coherence dataset from Wikipedia",
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),
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datasets.BuilderConfig(
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name=RST,
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version=VERSION,
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description="The RST Discourse Treebank dataset ",
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),
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datasets.BuilderConfig(
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name=PDTB_E,
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version=VERSION,
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description="The Penn Discourse Treebank - Explicit dataset.",
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),
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datasets.BuilderConfig(
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name=PDTB_I,
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version=VERSION,
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description="The Penn Discourse Treebank - Implicit dataset.",
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),
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datasets.BuilderConfig(
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name=SSPABS,
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version=VERSION,
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description="The SSP dataset.",
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),
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datasets.BuilderConfig(
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name=BSOARXIV,
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version=VERSION,
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description="The BSO Task with the arxiv dataset.",
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),
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datasets.BuilderConfig(
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name=BSOWIKI,
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version=VERSION,
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description="The BSO Task with the wiki dataset.",
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),
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datasets.BuilderConfig(
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name=BSOROCSTORY,
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version=VERSION,
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description="The BSO Task with the rocstory dataset.",
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),
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]
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def _info(self):
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if self.config.name in [SPARXIV, SPROCSTORY, SPWIKI]:
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features_dict = {
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TEXT_COLUMN_NAME[i]: datasets.Value('string')
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for i in range(SP_TEXT_COLUMNS)
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}
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features_dict[LABEL_NAME] = datasets.ClassLabel(
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names=list(SP_LABELS.values()),
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)
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features = datasets.Features(features_dict)
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elif self.config.name in [BSOARXIV, BSOWIKI, BSOROCSTORY]:
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features_dict = {
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TEXT_COLUMN_NAME[i]: datasets.Value('string')
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for i in range(BSO_TEXT_COLUMNS)
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}
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features_dict[LABEL_NAME] = datasets.ClassLabel(
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names=list(BSO_LABELS.values())
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)
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features = datasets.Features(features_dict)
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elif self.config.name in [DCCHAT, DCWIKI]:
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features_dict = {
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TEXT_COLUMN_NAME[i]: datasets.Value('string')
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for i in range(DC_TEXT_COLUMNS)
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}
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features_dict[LABEL_NAME] = datasets.ClassLabel(
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names=list(DC_LABELS.values())
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)
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features = datasets.Features(features_dict)
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+
elif self.config.name in [RST]:
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features_dict = {
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TEXT_COLUMN_NAME[i]: [datasets.Value('string')]
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+
for i in range(RST_TEXT_COLUMNS)
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}
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+
features_dict[LABEL_NAME] = datasets.ClassLabel(
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names=RST_LABELS
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)
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features = datasets.Features(features_dict)
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+
elif self.config.name in [PDTB_E]:
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features_dict = {
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+
TEXT_COLUMN_NAME[i]: datasets.Value('string')
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for i in range(PDTB_E_TEXT_COLUMNS)
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}
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features_dict[LABEL_NAME] = datasets.ClassLabel(
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names=PDTB_E_LABELS
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)
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features = datasets.Features(features_dict)
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elif self.config.name in [PDTB_I]:
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features_dict = {
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+
TEXT_COLUMN_NAME[i]: datasets.Value('string')
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for i in range(PDTB_I_TEXT_COLUMNS)
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}
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features_dict[LABEL_NAME] = datasets.ClassLabel(
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names=PDTB_I_LABELS
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)
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features = datasets.Features(features_dict)
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elif self.config.name in [SSPABS]:
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features_dict = {
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+
TEXT_COLUMN_NAME[i]: datasets.Value('string')
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for i in range(SSPABS_TEXT_COLUMNS)
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}
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+
features_dict[LABEL_NAME] = datasets.ClassLabel(
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names=list(SSPABS_LABELS.values())
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)
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features = datasets.Features(features_dict)
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)
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def _split_generators(self, dl_manager):
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if self.config.name in [SPARXIV, SPROCSTORY, SPWIKI]:
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data_dir = SP_DATA_DIR + "/" + SP_DIRS[self.config.name]
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train_name = SP_TRAIN_NAME
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valid_name = SP_VALID_NAME
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test_name = SP_TEST_NAME
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+
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elif self.config.name in [BSOARXIV, BSOWIKI, BSOROCSTORY]:
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data_dir = BSO_DATA_DIR + "/" + BSO_DIRS[self.config.name]
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train_name = BSO_TRAIN_NAME
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+
valid_name = BSO_VALID_NAME
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test_name = BSO_TEST_NAME
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+
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elif self.config.name in [DCCHAT, DCWIKI]:
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data_dir = DC_DATA_DIR + "/" + DC_DIRS[self.config.name]
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train_name = DC_TRAIN_NAME
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+
valid_name = DC_VALID_NAME
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+
test_name = DC_TEST_NAME
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+
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elif self.config.name in [RST]:
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data_dir = RST_DATA_DIR
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train_name = RST_TRAIN_NAME
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+
valid_name = RST_VALID_NAME
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+
test_name = RST_TEST_NAME
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+
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elif self.config.name in [PDTB_E, PDTB_I]:
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data_dir = os.path.join(PDTB_DATA_DIR, PDTB_DIRS[self.config.name])
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train_name = PDTB_TRAIN_NAME
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+
valid_name = PDTB_VALID_NAME
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+
test_name = PDTB_TEST_NAME
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+
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elif self.config.name in [SSPABS]:
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data_dir = SSPABS_DATA_DIR
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train_name = SSPABS_TRAIN_NAME
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valid_name = SSPABS_VALID_NAME
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test_name = SSPABS_TEST_NAME
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urls_to_download = {
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"train": data_dir + "/" + train_name,
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logger = logging.getLogger(__name__)
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logger.info(f"Current working dir: {os.getcwd()}")
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logger.info("generating examples from = %s", filepath)
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438 |
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if self.config.name == RST:
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data = pickle.load(open(filepath, "rb"))
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for key, line in enumerate(data):
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example = {TEXT_COLUMN_NAME[i]: sent for i, sent in enumerate(line[1:])}
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example[LABEL_NAME] = RST_DATA_DIR[line[0]]
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yield key, example
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else:
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with io.open(filepath, mode='r', encoding='utf-8') as f:
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for key, line in enumerate(f):
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line = line.strip().split("\t")
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+
example = {TEXT_COLUMN_NAME[i]: sent for i, sent in enumerate(line[1:])}
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if self.config.name == PDTB_E:
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example[LABEL_NAME] = PDTB_E_LABELS[int(line[0])]
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+
if self.config.name == PDTB_I:
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example[LABEL_NAME] = PDTB_I_LABELS[int(line[0])]
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elif self.config.name in (DCCHAT, DCWIKI):
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example[LABEL_NAME] = DC_LABELS[line[0]]
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elif self.config.name == SSPABS:
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example[LABEL_NAME] = SSPABS_LABELS[line[0]]
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elif self.config.name in (SPWIKI, SPROCSTORY, SPARXIV):
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example[LABEL_NAME] = SP_LABELS[line[0]]
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elif self.config.name in (BSOARXIV, BSOWIKI, BSOROCSTORY):
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example[LABEL_NAME] = BSO_LABELS[line[0]]
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yield key, example
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