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indolem_ntp.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import json
+import datasets
+from nusacrowd.utils import schemas
+
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@article{DBLP:journals/corr/abs-2011-00677,
+  author    = {Fajri Koto and
+               Afshin Rahimi and
+               Jey Han Lau and
+               Timothy Baldwin},
+  title     = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
+               Model for Indonesian {NLP}},
+  journal   = {CoRR},
+  volume    = {abs/2011.00677},
+  year      = {2020},
+  url       = {https://arxiv.org/abs/2011.00677},
+  eprinttype = {arXiv},
+  eprint    = {2011.00677},
+  timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_DATASETNAME = "indolem_ntp"
+
+_DESCRIPTION = """\
+NTP (Next Tweet prediction) is one of the comprehensive Indonesian benchmarks that given a list of tweets and an option, we predict if the option is the next tweet or not.
+This task is similar to the next sentence prediction (NSP) task used to train BERT (Devlin et al., 2019).
+In NTP, each instance consists of a Twitter thread (containing 2 to 4 tweets) that we call the premise, and four possible options for the next tweet, one of which is the actual response from the original thread.
+
+Train: 5681 threads
+Development: 811 threads
+Test: 1890 threads
+"""
+
+_HOMEPAGE = "https://indolem.github.io/"
+
+_LICENSE = "Creative Commons Attribution 4.0"
+
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://raw.githubusercontent.com/indolem/indolem/main/next_tweet_prediction/data/train.json",
+        "validation": "https://raw.githubusercontent.com/indolem/indolem/main/next_tweet_prediction/data/dev.json",
+        "test": "https://raw.githubusercontent.com/indolem/indolem/main/next_tweet_prediction/data/test.json",
+    }
+}
+
+_SUPPORTED_TASKS = [Tasks.NEXT_SENTENCE_PREDICTION]
+
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class IndolemNTPDataset(datasets.GeneratorBasedBuilder):
+    """NTP (Next Tweet prediction) is based on next sentence prediction (NSP), consists of a Twitter thread (containing  2 to 4 tweets) and four possible options for the next tweet, one of which is the actual response from the original thread."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="indolem_ntp_source",
+            version=SOURCE_VERSION,
+            description="Indolem NTP source schema",
+            schema="source",
+            subset_id="indolem_ntp",
+        ),
+        NusantaraConfig(
+            name="indolem_ntp_nusantara_pairs",
+            version=NUSANTARA_VERSION,
+            description="Indolem NTP Nusantara schema",
+            schema="nusantara_pairs",
+            subset_id="indolem_ntp",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "indolem_ntp_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tweets": datasets.Value("string"),
+                    "next_tweet": datasets.Value("string"),
+                    "label": datasets.Value("int8"),
+                }
+            )
+        elif self.config.schema == "nusantara_pairs":
+            features = schemas.pairs_features([0, 1])
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir["validation"],
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        data = self._read_data(filepath)
+        if self.config.schema == "source":
+            for i, row in enumerate(data):
+                ex = {
+                    "id": str(i),
+                    "tweets": row[0],
+                    "next_tweet": row[1],
+                    "label": row[2],
+                }
+                yield i, ex
+
+        elif self.config.schema == "nusantara_pairs":
+            for i, row in enumerate(data):
+                ex = {
+                    "id": str(i),
+                    "text_1": row[0],
+                    "text_2": row[1],
+                    "label": row[2],
+                }
+                yield i, ex
+
+    def _read_data(self, fname):
+        data = json.load(open(fname, "r"))
+        results = []
+        for datum in data:
+            tweets = " ".join(datum["tweets"])
+            for key, option in datum["next_tweet"]:
+                results.append((tweets, option, key))
+        return results