Upload OpenSLU_Clone.py with huggingface_hub

OpenSLU_Clone.py

@@ -0,0 +1,156 @@
+import json
+import os
+
+import datasets
+
+_OPEN_SLU_CITATION = """\
+xxx"""
+
+_OPEN_SLU_DESCRIPTION = """\
+xxx"""
+
+_ATIS_CITATION = """\
+@inproceedings{hemphill1990atis,
+	title = "The {ATIS} Spoken Language Systems Pilot Corpus",
+	author = "Hemphill, Charles T.  and
+	Godfrey, John J.  and
+	Doddington, George R.",
+	booktitle = "Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, {P}ennsylvania, June 24-27,1990",
+	year = "1990",
+	url = "https://aclanthology.org/H90-1021",
+}
+"""
+
+_ATIS_DESCRIPTION = """\
+ A widely used SLU corpus for single-intent SLU.
+"""
+
+
+class OpenSLUConfig(datasets.BuilderConfig):
+    """BuilderConfig for OpenSLU."""
+
+    def __init__(self, features, data_url, citation, url, intent_label_classes=None, slot_label_classes=None, **kwargs):
+        """BuilderConfig for OpenSLU.
+        Args:
+          features: `list[string]`, list of the features that will appear in the
+            feature dict. Should not include "label".
+          data_url: `string`, url to download the zip file from.
+          citation: `string`, citation for the data set.
+          url: `string`, url for information about the data set.
+          intent_label_classes: `list[string]`, the list of classes for the intent label
+          slot_label_classes: `list[string]`, the list of classes for the slot label
+          **kwargs: keyword arguments forwarded to super.
+        """
+        # Version history:
+        # 0.0.1: Initial version.
+        super(OpenSLUConfig, self).__init__(version=datasets.Version("0.0.1"), **kwargs)
+        self.features = features
+        self.intent_label_classes = intent_label_classes
+        self.slot_label_classes = slot_label_classes
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+
+
+class OpenSLU(datasets.GeneratorBasedBuilder):
+    """The SuperGLUE benchmark."""
+
+    BUILDER_CONFIGS = [
+        OpenSLUConfig(
+            name="products",
+            description=_ATIS_DESCRIPTION,
+            features=["text"],
+            data_url="https://huggingface.co/datasets/rams901/OpenSLU_Clone/resolve/main/prods_v1.tar.gz",
+            citation=_ATIS_CITATION,
+            url="https://aclanthology.org/H90-1021",
+        ),
+    ]
+
+    def _info(self):
+        features = {feature: datasets.Sequence(datasets.Value("string")) for feature in self.config.features}
+        features["slot"] = datasets.Sequence(datasets.Value("string"))
+        features["intent"] = datasets.Value("string")
+
+        return datasets.DatasetInfo(
+            description=_OPEN_SLU_DESCRIPTION + self.config.description,
+            features=datasets.Features(features),
+            homepage=self.config.url,
+            citation=self.config.citation + "\n" + _OPEN_SLU_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        print(self.config.data_url)
+        dl_dir = dl_manager.download_and_extract(self.config.data_url) or ""
+
+        task_name = _get_task_name_from_data_url(self.config.data_url)
+        print(dl_dir)
+        print(task_name)
+        dl_dir = os.path.join(dl_dir, task_name)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "train.jsonl"),
+                    "split": datasets.Split.TRAIN,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "dev.jsonl"),
+                    "split": datasets.Split.VALIDATION,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "test.jsonl"),
+                    "split": datasets.Split.TEST,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_file, split):
+        with open(data_file, encoding="utf-8") as f:
+            for index, line in enumerate(f):
+                row = json.loads(line)
+                yield index, row
+
+
+def _cast_label(label):
+    """Converts the label into the appropriate string version."""
+    if isinstance(label, str):
+        return label
+    elif isinstance(label, bool):
+        return "True" if label else "False"
+    elif isinstance(label, int):
+        assert label in (0, 1)
+        return str(label)
+    else:
+        raise ValueError("Invalid label format.")
+
+
+def _get_record_entities(passage):
+    """Returns the unique set of entities."""
+    text = passage["text"]
+    entity_spans = list()
+    for entity in passage["entities"]:
+        entity_text = text[entity["start"]: entity["end"] + 1]
+        entity_spans.append({"text": entity_text, "start": entity["start"], "end": entity["end"] + 1})
+    entity_spans = sorted(entity_spans, key=lambda e: e["start"])  # sort by start index
+    entity_texts = set(e["text"] for e in entity_spans)  # for backward compatability
+    return entity_texts, entity_spans
+
+
+def _get_record_answers(qa):
+    """Returns the unique set of answers."""
+    if "answers" not in qa:
+        return []
+    answers = set()
+    for answer in qa["answers"]:
+        answers.add(answer["text"])
+    return sorted(answers)
+
+
+def _get_task_name_from_data_url(data_url):
+    return data_url.split("/")[-1].split(".")[0]