Create mtop.py

mtop.py

@@ -0,0 +1,97 @@
+"""
+domain in {'alarm',
+ 'calling',
+ 'event',
+ 'messaging',
+ 'music',
+ 'news',
+ 'people',
+ 'recipes',
+ 'reminder',
+ 'timer',
+ 'weather'}
+"""
+
+_URL = "https://fb.me/mtop_dataset"
+
+_CITATION = """@article{li2020mtop,
+  title={MTOP: A comprehensive multilingual task-oriented semantic parsing benchmark},
+  author={Li, Haoran and Arora, Abhinav and Chen, Shuohui and Gupta, Anchit and Gupta, Sonal and Mehdad, Yashar},
+  journal={arXiv preprint arXiv:2008.09335},
+  year={2020}
+}"""
+
+_DESCRIPTION = """ """
+
+
+
+class MtopConfig(datasets.BuilderConfig):
+    """BuilderConfig for Mtop."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Mtop.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(MtopConfig, self).__init__(**kwargs)
+
+
+class Mtop(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        MtopConfig(
+            name="mtop",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "idx": datasets.Value("string"),
+                    "intent": datasets.Value("string"),
+                    "spans": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "domain": datasets.Value("string"),
+                    "lang": datasets.Value("string"),
+                    "logical_form": datasets.Value("string"),
+                    "tokenized_question": datasets.Value("string"),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="",
+            citation=_CITATION,
+            
+        )
+
+    def _split_generators(self, dl_manager):
+        filepath = dl_manager.download_and_extract(_URL)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": filepath,"split":"train"}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": filepath,"split":"eval"}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": filepath,"split":"test"}),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """This function returns the examples in the raw (text) form."""
+        key = 0
+        with open(f"{filepath}/mtop/en/{split}.txt", encoding="utf-8") as f:
+            for example in f:
+                example = example.split("\t")
+                dict_example = dict(idx=example[0],
+                                    intent=example[1],
+                                    spans=example[2],
+                                    question=example[3],
+                                    domain=example[4],
+                                    lang=example[5],
+                                    logical_form=example[6],
+                                    tokenized_question=example[7])
+                yield key, dict_example
+                key += 1