Upload mtop_intent_classification.py with huggingface_hub

mtop_intent_classification.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.sea_datasets.mtop_intent_classification.labels import (
+    DOMAIN_LABELS, INTENT_LABELS)
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{li-etal-2021-mtop,
+  author    = {Li, Haoran and Arora, Abhinav and Chen, Shuochi and Gupta, Anchit and Gupta, Sonal and Mehdad, Yashar},
+  title     = {MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark},
+  booktitle   = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
+  publisher = {Association for Computational Linguistics},
+  year      = {2021},
+  url       = {https://aclanthology.org/2021.eacl-main.257},
+  doi       = {10.18653/v1/2021.eacl-main.257},
+  pages    = {2950-2962},
+}
+"""
+_LOCAL = False
+_LANGUAGES = ["tha"]
+_DATASETNAME = "mtop_intent_classification"
+_DESCRIPTION = """
+This dataset contains annotated utterances from 6 languages, including Thai,
+for semantic parsing. Queries corresponding to the chosen domains are crowdsourced.
+ Two subsets are included in this dataset: 'domain' (eg. 'news', 'people', 'weather')
+ and 'intent' (eg. 'GET_MESSAGE', 'STOP_MUSIC', 'END_CALL')
+"""
+
+_HOMEPAGE = "https://huggingface.co/mteb"
+_LICENSE = Licenses.CC_BY_SA_4_0.value  # Found in original dataset (not HF) linked in paper
+_URL = "https://huggingface.co/datasets/mteb/"
+
+
+_SUPPORTED_TASKS = [Tasks.INTENT_CLASSIFICATION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class MTOPIntentClassificationDataset(datasets.GeneratorBasedBuilder):
+    """Dataset of Thai sentences and their domains or intents."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SUBSETS = ["domain", "intent"]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema for {subset} subset",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in SUBSETS
+    ] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema for {subset} subset",
+            schema="seacrowd_text",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in SUBSETS
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_domain_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("int64"),
+                    "text": datasets.Value("string"),
+                    "label": datasets.Value("int32"),
+                    "label_text": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_text":
+            if self.config.subset_id == "domain":
+                labels = DOMAIN_LABELS
+            elif self.config.subset_id == "intent":
+                labels = INTENT_LABELS
+            else:
+                raise ValueError(f"Received unexpected schema name {self.config.name}")
+            features = schemas.text_features(label_names=labels)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        # dl_manager not used since dataloader uses HF `load_dataset`
+        return [datasets.SplitGenerator(name=split, gen_kwargs={"split": split._name}) for split in (datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST)]
+
+    def _load_hf_data_from_remote(self, split: str) -> datasets.DatasetDict:
+        """Load dataset from HuggingFace."""
+        if self.config.subset_id not in ("domain", "intent"):
+            raise ValueError(f"Received unexpected schema name {self.config.name}")
+        HF_REMOTE_REF = "/".join(_URL.split("/")[-2:]) + f"mtop_{self.config.subset_id}"
+        _hf_dataset_source = datasets.load_dataset(HF_REMOTE_REF, "th", split=split)
+        return _hf_dataset_source
+
+    def _generate_examples(self, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        data = self._load_hf_data_from_remote(split=split)
+        for index, row in enumerate(data):
+            if self.config.schema == "source":
+                example = row
+
+            elif self.config.schema == "seacrowd_text":
+                example = {"id": str(index), "text": row["text"], "label": row["label_text"]}
+            yield index, example