delete useless files

ActivityNet_Captions

@@ -1,127 +0,0 @@
-# Lint as: python3
-"""TGIF: A New Dataset and Benchmark on Animated GIF Description"""
-
-import os
-import json
-import datasets
-
-_CITATION = """
-@inproceedings{krishna2017dense,
-    title={Dense-Captioning Events in Videos},
-    author={Krishna, Ranjay and Hata, Kenji and Ren, Frederic and Fei-Fei, Li and Niebles, Juan Carlos},
-    booktitle={International Conference on Computer Vision (ICCV)},
-    year={2017}
-}
-"""
-
-_DESCRIPTION = """\
-The ActivityNet Captions dataset connects videos to a series of temporally annotated sentence descriptions.
-Each sentence covers an unique segment of the video, describing multiple events that occur. These events
-may occur over very long or short periods of time and are not limited in any capacity, allowing them to 
-co-occur. On average, each of the 20k videos contains 3.65 temporally localized sentences, resulting in
-a total of 100k sentences. We find that the number of sentences per video follows a relatively normal
-distribution. Furthermore, as the video duration increases, the number of sentences also increases. 
-Each sentence has an average length of 13.48 words, which is also normally distributed. You can find more
-details of the dataset under the ActivityNet Captions Dataset section, and under supplementary materials 
-in the paper.
-"""
-
-_URL_BASE = "https://cs.stanford.edu/people/ranjaykrishna/densevid/"
-
-_DL_URL = "https://huggingface.co/datasets/Leyo/ActivityNet_Captions/resolve/main/captions.tar.gz"
-
-class ActivityNetConfig(datasets.BuilderConfig):
-    """BuilderConfig for ActivityNet Captions."""
-
-    def __init__(self, **kwargs):
-        super(ActivityNetConfig, self).__init__(
-            version=datasets.Version("2.1.0", ""), **kwargs)
-
-
-class ActivityNet(datasets.GeneratorBasedBuilder):
-
-    DEFAULT_CONFIG_NAME = "all"
-    BUILDER_CONFIGS = [
-        ActivityNetConfig(
-            name="all", description="All the ActivityNet Captions dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "video_id": datasets.Value("string"),
-                    "path": datasets.Value("string"),
-                    "duration": datasets.Value("float32"),
-                    "starts": datasets.features.Sequence(datasets.Value("float32")),
-                    "ends": datasets.features.Sequence(datasets.Value("float32")),
-                    "captions": datasets.features.Sequence(datasets.Value("string"))
-                }
-            ),
-            supervised_keys=None,
-            homepage=_URL_BASE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download(_DL_URL)
-
-        train_splits = [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "files": dl_manager.iterable(archive_path),
-                    "ids_file": os.path.join(archive_path, "train_ids.json"),
-                    "infos_file": os.path.join(archive_path, "train.json")
-                },
-            )
-        ]
-        dev_splits = [
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "files": dl_manager.iterable(archive_path),
-                    "ids_file": os.path.join(archive_path, "val_ids.json"),
-                    "infos_file": os.path.join(archive_path, "val_1.json")
-                },
-            )
-        ]
-        test_splits = [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "files": dl_manager.iterable(archive_path),
-                    "ids_file": os.path.join(archive_path, "test_ids.json"),
-                    "infos_file": os.path.join(archive_path, "val_2.json")
-                },
-            )
-        ]
-        return train_splits + dev_splits + test_splits
-
-    def _generate_examples(self, files, ids_file, infos_file):
-        """This function returns the examples."""
-
-        for path, f in files:
-            if path == infos_file:
-                with open(f, encoding="utf-8") as json_file:
-                    infos = json.load(json_file)
-        for path, f in files:
-            if path == ids_file:
-                with open(f, encoding="utf-8") as json_file:
-                    ids = json.load(json_file)
-                    for idx, id in enumerate(ids):
-                        path = "https://www.youtube.com/watch?v=" + id[2:]
-                        starts = [timestamp[0]
-                                for timestamp in infos[id]["timestamps"]]
-                        ends = [timestamp[1] for timestamp in infos[id]["timestamps"]]
-                        yield idx, {
-                            "video_id": id,
-                            "path": path,
-                            "video_id": datasets.Value("string"),
-                            "path": datasets.Value("string"),
-                            "duration": infos[id]["duration"],
-                            "starts": starts,
-                            "ends": ends,
-                            "captions": infos[id]["sentences"],
-                        }

captions.tar.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dc829f081096b8a4cd2271ca54df37d28ba44a56b94330c0dc1a9c1e7f8ba854
-size 2664968