Delete loading script

blended_skill_talk.py

@@ -1,146 +0,0 @@
-"""TODO(blended_skill_talk): Add a description here."""
-
-
-import json
-
-import datasets
-
-
-# TODO(blended_skill_talk): BibTeX citation
-_CITATION = """\
-@misc{smith2020evaluating,
-    title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills},
-    author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau},
-    year={2020},
-    eprint={2004.08449},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL}
-}
-"""
-
-# TODO(blended_skill_talk):
-_DESCRIPTION = """\
-A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge.
-"""
-_URL = "http://parl.ai/downloads/blended_skill_talk/blended_skill_talk.tar.gz"
-
-_TASK = ["convai2", "empathetic_dialogues", "wizard_of_wikipedia"]
-
-
-class BlendedSkillTalk(datasets.GeneratorBasedBuilder):
-    """TODO(blended_skill_talk): Short description of my dataset."""
-
-    # TODO(blended_skill_talk): Set up version.
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        # TODO(blended_skill_talk): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "personas": datasets.features.Sequence(datasets.Value("string")),
-                    "additional_context": datasets.Value("string"),
-                    "previous_utterance": datasets.features.Sequence(datasets.Value("string")),
-                    "context": datasets.Value("string"),
-                    "free_messages": datasets.features.Sequence(datasets.Value("string")),
-                    "guided_messages": datasets.features.Sequence(datasets.Value("string")),
-                    "suggestions": datasets.features.Sequence({task: datasets.Value("string") for task in _TASK}),
-                    "guided_chosen_suggestions": datasets.features.Sequence(datasets.Value("string")),
-                    "label_candidates": datasets.features.Sequence(
-                        datasets.features.Sequence(datasets.Value("string"))
-                    ),
-                    # These are the features of your dataset like images, labels ...
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://parl.ai/projects/bst/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(blended_skill_talk): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        archive = dl_manager.download(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "train.json",
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "valid.json",
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "test.json",
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, files):
-        """Yields examples."""
-        # TODO(blended_skill_talk): Yields (key, example) tuples from the dataset
-        for path, f in files:
-            if path == filepath:
-                data = json.load(f)
-                for id_, row in enumerate(data):
-                    personas = [row["personas"][1][0], row["personas"][1][1]]
-                    dialogs = [dialog[1] for dialog in row["dialog"]]
-                    free_messages = []
-                    guided_messages = []
-
-                    for i in range(len(dialogs) // 2):
-                        free_messages.append(dialogs[2 * i])
-                        guided_messages.append(dialogs[2 * i + 1])
-                    context = row["context_dataset"]
-                    add_context = row["additional_context"] if context == "wizard_of_wikipedia" else ""
-                    previous_utterance = [row["free_turker_utterance"], row["guided_turker_utterance"]]
-                    suggestions = row["suggestions"]
-                    convai_suggestions = []
-                    empathetic_suggestions = []
-                    wow_suggestions = []
-                    for i in range(len(suggestions) // 2):
-                        convai_suggestions.append(suggestions[2 * i + 1]["convai2"])
-                        empathetic_suggestions.append(suggestions[2 * i + 1]["empathetic_dialogues"])
-                        wow_suggestions.append(suggestions[2 * i + 1]["wizard_of_wikipedia"])
-                    chosen_suggestions = row["chosen_suggestions"]
-                    guided_chosen_suggestions = []
-                    for i in range(len(chosen_suggestions) // 2):
-                        guided_chosen_suggestions.append(chosen_suggestions[2 * i + 1])
-                    label_candidates = row["label_candidates"] if "label_candidates" in row else []
-                    yield id_, {
-                        "personas": personas,
-                        "additional_context": add_context,
-                        "previous_utterance": previous_utterance,
-                        "context": context,
-                        "free_messages": free_messages,
-                        "guided_messages": guided_messages,
-                        "suggestions": {
-                            "convai2": convai_suggestions,
-                            "empathetic_dialogues": empathetic_suggestions,
-                            "wizard_of_wikipedia": wow_suggestions,
-                        },
-                        "guided_chosen_suggestions": guided_chosen_suggestions,
-                        "label_candidates": label_candidates,
-                    }
-                break