Datasets:
Update URL for datasets
#2
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gentaiscool
- opened
- indonlu.py +49 -49
indonlu.py
CHANGED
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@@ -38,7 +38,7 @@ and analyzing natural language understanding systems for Bahasa Indonesia.
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_INDONLU_HOMEPAGE = "https://www.indobenchmark.com/"
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_INDONLU_LICENSE = "https://raw.githubusercontent.com/
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class IndonluConfig(datasets.BuilderConfig):
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@@ -93,12 +93,12 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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different emotion labels: sadness, anger, love, fear, and happy."""
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),
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text_features={"tweet": "tweet"},
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# label classes sorted refer to https://github.com/
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label_classes=["sadness", "anger", "love", "fear", "happy"],
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label_column="label",
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train_url="https://raw.githubusercontent.com/
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valid_url="https://raw.githubusercontent.com/
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test_url="https://raw.githubusercontent.com/
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citation=textwrap.dedent(
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"""\
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@inproceedings{saputri2018emotion,
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@@ -122,12 +122,12 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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dataset: positive, negative, and neutral."""
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),
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text_features={"text": "text"},
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# label classes sorted refer to https://github.com/
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label_classes=["positive", "neutral", "negative"],
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label_column="label",
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train_url="https://raw.githubusercontent.com/
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valid_url="https://raw.githubusercontent.com/
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test_url="https://raw.githubusercontent.com/
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citation=textwrap.dedent(
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"""\
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@inproceedings{purwarianti2019improving,
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@@ -151,12 +151,12 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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negative, and neutral."""
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),
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text_features={"sentence": "sentence"},
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# label classes sorted refer to https://github.com/
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label_classes=["negative", "neutral", "positive"],
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label_column=["fuel", "machine", "others", "part", "price", "service"],
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train_url="https://raw.githubusercontent.com/
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valid_url="https://raw.githubusercontent.com/
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test_url="https://raw.githubusercontent.com/
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citation=textwrap.dedent(
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"""\
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@inproceedings{ilmania2018aspect,
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@@ -181,7 +181,7 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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of the same aspect but for different objects (e.g., cleanliness of bed and toilet)."""
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),
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text_features={"sentence": "sentence"},
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# label classes sorted refer to https://github.com/
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label_classes=["neg", "neut", "pos", "neg_pos"],
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label_column=[
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"ac",
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@@ -195,9 +195,9 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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"tv",
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"wifi",
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],
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train_url="https://raw.githubusercontent.com/
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valid_url="https://raw.githubusercontent.com/
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test_url="https://raw.githubusercontent.com/
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citation=textwrap.dedent(
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"""\
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@inproceedings{azhar2019multi,
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@@ -223,12 +223,12 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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"hypothesis": "hypothesis",
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"category": "category",
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},
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# label classes sorted refer to https://github.com/
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label_classes=["NotEntail", "Entail_or_Paraphrase"],
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label_column="label",
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train_url="https://raw.githubusercontent.com/
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valid_url="https://raw.githubusercontent.com/
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test_url="https://raw.githubusercontent.com/
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citation=textwrap.dedent(
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"""\
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@inproceedings{setya2018semi,
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@@ -248,7 +248,7 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention."""
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),
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text_features={"tokens": "tokens"},
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-
# label classes sorted refer to https://github.com/
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label_classes=[
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"B-PPO",
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"B-KUA",
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@@ -278,9 +278,9 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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"B-VBE",
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],
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label_column="pos_tags",
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train_url="https://raw.githubusercontent.com/
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valid_url="https://raw.githubusercontent.com/
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test_url="https://raw.githubusercontent.com/
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citation=textwrap.dedent(
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"""\
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@inproceedings{hoesen2018investigating,
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@@ -302,7 +302,7 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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the experimental setting used by Kurniawan and Aji (2018)"""
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),
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text_features={"tokens": "tokens"},
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-
# label classes sorted refer to https://github.com/
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label_classes=[
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"B-PR",
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"B-CD",
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@@ -347,9 +347,9 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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"B-X",
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],
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label_column="pos_tags",
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train_url="https://raw.githubusercontent.com/
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valid_url="https://raw.githubusercontent.com/
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test_url="https://raw.githubusercontent.com/
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citation=textwrap.dedent(
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"""\
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@inproceedings{dinakaramani2014designing,
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@@ -380,12 +380,12 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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Inside-Outside-Beginning (IOB) tagging representation with two kinds of tags, aspect and sentiment."""
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),
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text_features={"tokens": "tokens"},
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-
# label classes sorted refer to https://github.com/
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label_classes=["I-SENTIMENT", "O", "I-ASPECT", "B-SENTIMENT", "B-ASPECT"],
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label_column="seq_label",
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train_url="https://raw.githubusercontent.com/
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valid_url="https://raw.githubusercontent.com/
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test_url="https://raw.githubusercontent.com/
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citation=textwrap.dedent(
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"""\
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@article{winatmoko2019aspect,
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@@ -413,12 +413,12 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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which represents the position of the keyphrase."""
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),
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text_features={"tokens": "tokens"},
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-
# label classes sorted refer to https://github.com/
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label_classes=["O", "B", "I"],
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label_column="seq_label",
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train_url="https://raw.githubusercontent.com/
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valid_url="https://raw.githubusercontent.com/
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test_url="https://raw.githubusercontent.com/
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citation=textwrap.dedent(
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"""\
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@inproceedings{mahfuzh2019improving,
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@@ -440,12 +440,12 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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ORGANIZATION (name of organization)."""
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),
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text_features={"tokens": "tokens"},
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-
# label classes sorted refer to https://github.com/
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label_classes=["I-PERSON", "B-ORGANISATION", "I-ORGANISATION", "B-PLACE", "I-PLACE", "O", "B-PERSON"],
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label_column="ner_tags",
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-
train_url="https://raw.githubusercontent.com/
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valid_url="https://raw.githubusercontent.com/
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test_url="https://raw.githubusercontent.com/
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citation=textwrap.dedent(
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"""\
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@online{nergrit2019,
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@@ -465,7 +465,7 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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EVT (name of the event), and FNB (name of food and beverage). The NERP dataset uses the IOB chunking format."""
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),
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text_features={"tokens": "tokens"},
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# label classes sorted refer to https://github.com/
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label_classes=[
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"I-PPL",
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"B-EVT",
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@@ -480,9 +480,9 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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"I-FNB",
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],
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label_column="ner_tags",
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train_url="https://raw.githubusercontent.com/
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valid_url="https://raw.githubusercontent.com/
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test_url="https://raw.githubusercontent.com/
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citation=textwrap.dedent(
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"""\
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@inproceedings{hoesen2018investigating,
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@@ -505,12 +505,12 @@ class Indonlu(datasets.GeneratorBasedBuilder):
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There are six categories of questions: date, location, name, organization, person, and quantitative."""
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),
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text_features={"question": "question", "passage": "passage"},
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# label classes sorted refer to https://github.com/
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label_classes=["O", "B", "I"],
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label_column="seq_label",
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-
train_url="https://raw.githubusercontent.com/
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-
valid_url="https://raw.githubusercontent.com/
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test_url="https://raw.githubusercontent.com/
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citation=textwrap.dedent(
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"""\
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@inproceedings{purwarianti2007machine,
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_INDONLU_HOMEPAGE = "https://www.indobenchmark.com/"
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+
_INDONLU_LICENSE = "https://raw.githubusercontent.com/IndoNLP/indonlu/master/LICENSE"
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class IndonluConfig(datasets.BuilderConfig):
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different emotion labels: sadness, anger, love, fear, and happy."""
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),
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text_features={"tweet": "tweet"},
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+
# label classes sorted refer to https://github.com/IndoNLP/indonlu/blob/master/utils/data_utils.py
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label_classes=["sadness", "anger", "love", "fear", "happy"],
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label_column="label",
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train_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/emot_emotion-twitter/train_preprocess.csv",
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valid_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/emot_emotion-twitter/valid_preprocess.csv",
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test_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/emot_emotion-twitter/test_preprocess.csv",
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citation=textwrap.dedent(
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"""\
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@inproceedings{saputri2018emotion,
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dataset: positive, negative, and neutral."""
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),
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text_features={"text": "text"},
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+
# label classes sorted refer to https://github.com/IndoNLP/indonlu/blob/master/utils/data_utils.py
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label_classes=["positive", "neutral", "negative"],
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label_column="label",
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train_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/smsa_doc-sentiment-prosa/train_preprocess.tsv",
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+
valid_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/smsa_doc-sentiment-prosa/valid_preprocess.tsv",
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+
test_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/smsa_doc-sentiment-prosa/test_preprocess.tsv",
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citation=textwrap.dedent(
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"""\
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@inproceedings{purwarianti2019improving,
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negative, and neutral."""
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),
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text_features={"sentence": "sentence"},
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+
# label classes sorted refer to https://github.com/IndoNLP/indonlu/blob/master/utils/data_utils.py
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label_classes=["negative", "neutral", "positive"],
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label_column=["fuel", "machine", "others", "part", "price", "service"],
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+
train_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/casa_absa-prosa/train_preprocess.csv",
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+
valid_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/casa_absa-prosa/valid_preprocess.csv",
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+
test_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/casa_absa-prosa/test_preprocess.csv",
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citation=textwrap.dedent(
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"""\
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@inproceedings{ilmania2018aspect,
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of the same aspect but for different objects (e.g., cleanliness of bed and toilet)."""
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),
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text_features={"sentence": "sentence"},
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+
# label classes sorted refer to https://github.com/IndoNLP/indonlu/blob/master/utils/data_utils.py
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label_classes=["neg", "neut", "pos", "neg_pos"],
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label_column=[
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"ac",
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"tv",
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"wifi",
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],
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train_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/hoasa_absa-airy/train_preprocess.csv",
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+
valid_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/hoasa_absa-airy/valid_preprocess.csv",
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+
test_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/hoasa_absa-airy/test_preprocess.csv",
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citation=textwrap.dedent(
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"""\
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@inproceedings{azhar2019multi,
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"hypothesis": "hypothesis",
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"category": "category",
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},
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+
# label classes sorted refer to https://github.com/IndoNLP/indonlu/blob/master/utils/data_utils.py
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label_classes=["NotEntail", "Entail_or_Paraphrase"],
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label_column="label",
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+
train_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/wrete_entailment-ui/train_preprocess.csv",
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+
valid_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/wrete_entailment-ui/valid_preprocess.csv",
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+
test_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/wrete_entailment-ui/test_preprocess.csv",
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citation=textwrap.dedent(
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"""\
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@inproceedings{setya2018semi,
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Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention."""
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),
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text_features={"tokens": "tokens"},
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+
# label classes sorted refer to https://github.com/IndoNLP/indonlu/blob/master/utils/data_utils.py
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label_classes=[
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"B-PPO",
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"B-KUA",
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"B-VBE",
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],
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label_column="pos_tags",
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+
train_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/posp_pos-prosa/train_preprocess.txt",
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+
valid_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/posp_pos-prosa/valid_preprocess.txt",
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+
test_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/posp_pos-prosa/test_preprocess.txt",
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citation=textwrap.dedent(
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"""\
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@inproceedings{hoesen2018investigating,
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the experimental setting used by Kurniawan and Aji (2018)"""
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),
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text_features={"tokens": "tokens"},
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+
# label classes sorted refer to https://github.com/IndoNLP/indonlu/blob/master/utils/data_utils.py
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label_classes=[
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"B-PR",
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"B-CD",
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"B-X",
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],
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label_column="pos_tags",
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+
train_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/bapos_pos-idn/train_preprocess.txt",
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+
valid_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/bapos_pos-idn/valid_preprocess.txt",
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+
test_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/bapos_pos-idn/test_preprocess.txt",
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citation=textwrap.dedent(
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"""\
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@inproceedings{dinakaramani2014designing,
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Inside-Outside-Beginning (IOB) tagging representation with two kinds of tags, aspect and sentiment."""
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),
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text_features={"tokens": "tokens"},
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+
# label classes sorted refer to https://github.com/IndoNLP/indonlu/blob/master/utils/data_utils.py
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label_classes=["I-SENTIMENT", "O", "I-ASPECT", "B-SENTIMENT", "B-ASPECT"],
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label_column="seq_label",
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+
train_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/terma_term-extraction-airy/train_preprocess.txt",
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+
valid_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/terma_term-extraction-airy/valid_preprocess.txt",
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+
test_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/terma_term-extraction-airy/test_preprocess.txt",
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citation=textwrap.dedent(
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"""\
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@article{winatmoko2019aspect,
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which represents the position of the keyphrase."""
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),
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text_features={"tokens": "tokens"},
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+
# label classes sorted refer to https://github.com/IndoNLP/indonlu/blob/master/utils/data_utils.py
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label_classes=["O", "B", "I"],
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label_column="seq_label",
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+
train_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/keps_keyword-extraction-prosa/train_preprocess.txt",
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+
valid_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/keps_keyword-extraction-prosa/valid_preprocess.txt",
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+
test_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/keps_keyword-extraction-prosa/test_preprocess.txt",
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citation=textwrap.dedent(
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"""\
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@inproceedings{mahfuzh2019improving,
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ORGANIZATION (name of organization)."""
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),
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text_features={"tokens": "tokens"},
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+
# label classes sorted refer to https://github.com/IndoNLP/indonlu/blob/master/utils/data_utils.py
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label_classes=["I-PERSON", "B-ORGANISATION", "I-ORGANISATION", "B-PLACE", "I-PLACE", "O", "B-PERSON"],
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label_column="ner_tags",
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+
train_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nergrit_ner-grit/train_preprocess.txt",
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+
valid_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nergrit_ner-grit/valid_preprocess.txt",
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+
test_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nergrit_ner-grit/test_preprocess.txt",
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citation=textwrap.dedent(
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"""\
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@online{nergrit2019,
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|
| 465 |
EVT (name of the event), and FNB (name of food and beverage). The NERP dataset uses the IOB chunking format."""
|
| 466 |
),
|
| 467 |
text_features={"tokens": "tokens"},
|
| 468 |
+
# label classes sorted refer to https://github.com/IndoNLP/indonlu/blob/master/utils/data_utils.py
|
| 469 |
label_classes=[
|
| 470 |
"I-PPL",
|
| 471 |
"B-EVT",
|
|
|
|
| 480 |
"I-FNB",
|
| 481 |
],
|
| 482 |
label_column="ner_tags",
|
| 483 |
+
train_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nerp_ner-prosa/train_preprocess.txt",
|
| 484 |
+
valid_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nerp_ner-prosa/valid_preprocess.txt",
|
| 485 |
+
test_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nerp_ner-prosa/test_preprocess.txt",
|
| 486 |
citation=textwrap.dedent(
|
| 487 |
"""\
|
| 488 |
@inproceedings{hoesen2018investigating,
|
|
|
|
| 505 |
There are six categories of questions: date, location, name, organization, person, and quantitative."""
|
| 506 |
),
|
| 507 |
text_features={"question": "question", "passage": "passage"},
|
| 508 |
+
# label classes sorted refer to https://github.com/IndoNLP/indonlu/blob/master/utils/data_utils.py
|
| 509 |
label_classes=["O", "B", "I"],
|
| 510 |
label_column="seq_label",
|
| 511 |
+
train_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/facqa_qa-factoid-itb/train_preprocess.csv",
|
| 512 |
+
valid_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/facqa_qa-factoid-itb/valid_preprocess.csv",
|
| 513 |
+
test_url="https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/facqa_qa-factoid-itb/test_preprocess.csv",
|
| 514 |
citation=textwrap.dedent(
|
| 515 |
"""\
|
| 516 |
@inproceedings{purwarianti2007machine,
|