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stb_ext.py

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+import io
+
+import conllu
+import datasets
+
+from seacrowd.utils.common_parser import load_ud_data_as_seacrowd_kb
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils import schemas
+from seacrowd.utils.constants import DEFAULT_SEACROWD_VIEW_NAME, DEFAULT_SOURCE_VIEW_NAME, Licenses, Tasks
+
+_DATASETNAME = "stb_ext"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
+
+_LANGUAGES = ["eng"]
+_LOCAL = False
+_CITATION = """\
+@article{wang2019genesis,
+title={From genesis to creole language: Transfer learning for singlish universal dependencies parsing and POS tagging},
+author={Wang, Hongmin and Yang, Jie and Zhang, Yue},
+journal={ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)},
+volume={19},
+number={1},
+pages={1--29},
+year={2019},
+publisher={ACM New York, NY, USA}
+}
+"""
+
+_DESCRIPTION = """\
+We adopt the Universal Dependencies protocol for constructing the Singlish dependency treebank, both as a new resource
+for the low-resource languages and to facilitate knowledge transfer from English. Briefly, the STB-EXT dataset offers
+a 3-times larger training set, while keeping the same dev and test sets from STB-ACL. We provide treebanks with both
+gold-standard as well as automatically generated POS tags.
+"""
+
+_HOMEPAGE = "https://github.com/wanghm92/Sing_Par/tree/master/TALLIP19_dataset/treebank"
+
+_LICENSE = Licenses.MIT.value
+
+_PREFIX = "https://raw.githubusercontent.com/wanghm92/Sing_Par/master/TALLIP19_dataset/treebank/"
+_URLS = {
+    "gold_pos": {
+        "train": _PREFIX + "gold_pos/train.ext.conll",
+    },
+    "en_ud_autopos": {"train": _PREFIX + "en-ud-autopos/en-ud-train.conllu.autoupos", "validation": _PREFIX + "en-ud-autopos/en-ud-dev.conllu.ann.auto.epoch24.upos", "test": _PREFIX + "en-ud-autopos/en-ud-test.conllu.ann.auto.epoch24.upos"},
+    "auto_pos_multiview": {
+        "train": _PREFIX + "auto_pos/multiview/train.autopos.multiview.conll",
+        "validation": _PREFIX + "auto_pos/multiview/dev.autopos.multiview.conll",
+        "test": _PREFIX + "auto_pos/multiview/test.autopos.multiview.conll",
+    },
+    "auto_pos_stack": {
+        "train": _PREFIX + "auto_pos/stack/train.autopos.stack.conll",
+        "validation": _PREFIX + "auto_pos/stack/dev.autopos.stack.conll",
+        "test": _PREFIX + "auto_pos/stack/test.autopos.stack.conll",
+    },
+}
+_POSTAGS = ["ADJ", "ADP", "ADV", "AUX", "CONJ", "DET", "INTJ", "NOUN", "NUM", "PART", "PRON", "PROPN", "PUNCT", "SCONJ", "SYM", "VERB", "X", "root"]
+_SUPPORTED_TASKS = [Tasks.POS_TAGGING, Tasks.DEPENDENCY_PARSING]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+def config_constructor(subset_id, schema, version):
+    return SEACrowdConfig(name=f"{_DATASETNAME}_{subset_id}_{schema}",
+                          version=datasets.Version(version), description=_DESCRIPTION,
+                          schema=schema, subset_id=subset_id)
+
+
+class StbExtDataset(datasets.GeneratorBasedBuilder):
+    """This is a seacrowd dataloader for the STB-EXT dataset, which offers a 3-times larger training set, while keeping
+    the same dev and test sets from STB-ACL. It provides treebanks with both gold-standard and automatically generated POS tags."""
+
+    BUILDER_CONFIGS = [
+        # source
+        config_constructor(subset_id="auto_pos_stack", schema="source", version=_SOURCE_VERSION),
+        config_constructor(subset_id="auto_pos_multiview", schema="source", version=_SOURCE_VERSION),
+        config_constructor(subset_id="en_ud_autopos", schema="source", version=_SOURCE_VERSION),
+        config_constructor(subset_id="gold_pos", schema="source", version=_SOURCE_VERSION),
+        # seq_label
+        config_constructor(subset_id="auto_pos_stack", schema="seacrowd_seq_label", version=_SEACROWD_VERSION),
+        config_constructor(subset_id="auto_pos_multiview", schema="seacrowd_seq_label", version=_SEACROWD_VERSION),
+        config_constructor(subset_id="en_ud_autopos", schema="seacrowd_seq_label", version=_SEACROWD_VERSION),
+        config_constructor(subset_id="gold_pos", schema="seacrowd_seq_label", version=_SEACROWD_VERSION),
+        # dependency parsing
+        config_constructor(subset_id="auto_pos_stack", schema="seacrowd_kb", version=_SEACROWD_VERSION),
+        config_constructor(subset_id="auto_pos_multiview", schema="seacrowd_kb", version=_SEACROWD_VERSION),
+        config_constructor(subset_id="en_ud_autopos", schema="seacrowd_kb", version=_SEACROWD_VERSION),
+        config_constructor(subset_id="gold_pos", schema="seacrowd_kb", version=_SEACROWD_VERSION),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_gold_pos_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    # metadata
+                    "sent_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "text_en": datasets.Value("string"),
+                    # tokens
+                    "id": [datasets.Value("string")],
+                    "form": [datasets.Value("string")],
+                    "lemma": [datasets.Value("string")],
+                    "upos": [datasets.Value("string")],
+                    "xpos": [datasets.Value("string")],
+                    "feats": [datasets.Value("string")],
+                    "head": [datasets.Value("string")],
+                    "deprel": [datasets.Value("string")],
+                    "deps": [datasets.Value("string")],
+                    "misc": [datasets.Value("string")],
+                }
+            )
+        elif self.config.schema == "seacrowd_seq_label":
+            features = schemas.seq_label_features(label_names=_POSTAGS)
+        elif self.config.schema == "seacrowd_kb":
+            features = schemas.kb_features
+        else:
+            raise ValueError(f"Invalid config: {self.config.schema}")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """ "return splitGenerators"""
+        urls = _URLS[self.config.subset_id]
+        downloaded_files = dl_manager.download_and_extract(urls)
+        splits = []
+        if "train" in downloaded_files:
+            splits.append(datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}))
+        if "validation" in downloaded_files:
+            splits.append(datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation"]}))
+        if "test" in downloaded_files:
+            splits.append(datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}))
+        return splits
+
+    def _generate_examples(self, filepath):
+        def process_buffer(TextIO):
+            BOM = "\ufeff"
+            buffer = io.StringIO()
+            for line in TextIO:
+                line = line.replace(BOM, "") if BOM in line else line
+                buffer.write(line)
+            buffer.seek(0)
+            return buffer
+
+        with open(filepath, "r", encoding="utf-8") as data_file:
+            tokenlist = list(conllu.parse_incr(process_buffer(data_file)))
+            data_instances = []
+            for idx, sent in enumerate(tokenlist):
+                idx = sent.metadata["sent_id"] if "sent_id" in sent.metadata else idx
+                tokens = [token["form"] for token in sent]
+                txt = sent.metadata["text"] if "text" in sent.metadata else " ".join(tokens)
+                example = {
+                    # meta
+                    "sent_id": str(idx),
+                    "text": txt,
+                    "text_en": txt,
+                    # tokens
+                    "id": [token["id"] for token in sent],
+                    "form": [token["form"] for token in sent],
+                    "lemma": [token["lemma"] for token in sent],
+                    "upos": [token["upos"] for token in sent],
+                    "xpos": [token["xpos"] for token in sent],
+                    "feats": [str(token["feats"]) for token in sent],
+                    "head": [str(token["head"]) for token in sent],
+                    "deprel": [str(token["deprel"]) for token in sent],
+                    "deps": [str(token["deps"]) for token in sent],
+                    "misc": [str(token["misc"]) for token in sent]
+                }
+                data_instances.append(example)
+
+            if self.config.schema == "source":
+                pass
+            if self.config.schema == "seacrowd_seq_label":
+                data_instances = list(
+                    map(
+                        lambda d: {
+                            "id": d["sent_id"],
+                            "tokens": d["form"],
+                            "labels": d["upos"],
+                        },
+                        data_instances,
+                    )
+                )
+            if self.config.schema == "seacrowd_kb":
+                data_instances = load_ud_data_as_seacrowd_kb(filepath, data_instances)
+            for key, exam in enumerate(data_instances):
+                yield key, exam