Update parquet files

.gitattributes

@@ -1,37 +0,0 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zstandard filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
-*.mp3 filter=lfs diff=lfs merge=lfs -text
-*.ogg filter=lfs diff=lfs merge=lfs -text
-*.wav filter=lfs diff=lfs merge=lfs -text

ActivityNet_Captions.py

@@ -1,115 +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/"
-
-
-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"),
-                    "video_path": datasets.Value("string"),
-                    "duration": datasets.Value("float32"),
-                    "captions_starts": datasets.features.Sequence(datasets.Value("float32")),
-                    "captions_ends": datasets.features.Sequence(datasets.Value("float32")),
-                    "en_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_and_extract(
-            _URL_BASE + "captions.zip")
-
-        train_splits = [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "infos_file": os.path.join(archive_path, "train.json")
-                },
-            )
-        ]
-        dev_splits = [
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "infos_file": os.path.join(archive_path, "val_1.json")
-                },
-            )
-        ]
-        test_splits = [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "infos_file": os.path.join(archive_path, "val_2.json")
-                },
-            )
-        ]
-        return train_splits + dev_splits + test_splits
-
-    def _generate_examples(self, infos_file):
-        """This function returns the examples."""
-
-        with open(infos_file, encoding="utf-8") as json_file:
-            infos = json.load(json_file)
-            for idx, id in enumerate(infos):
-                path = "https://www.youtube.com/watch?v=" + id[2:]
-                starts = [float(timestamp[0])
-                          for timestamp in infos[id]["timestamps"]]
-                ends = [float(timestamp[1])
-                        for timestamp in infos[id]["timestamps"]]
-                captions = [str(caption) for caption in infos[id]["sentences"]]
-                yield idx, {
-                    "video_id": id,
-                    "video_path": path,
-                    "duration": float(infos[id]["duration"]),
-                    "captions_starts": starts,
-                    "captions_ends": ends,
-                    "en_captions": captions,
-                }

README.md

@@ -1,97 +0,0 @@
----
-annotations_creators:
-- expert-generated
-language_creators:
-- crowdsourced
-language:
-- en
-license:
-- other
-multilinguality:
-- monolingual
-pretty_name: ActivityNet Captions
-size_categories:
-- 10k<n<100K
-source_datasets:
-- original
-task_categories:
-- video-captionning
-task_ids:
-- closed-domain-qa
----
-
-
-# Dataset Card for ActivityNet Captions
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-## Dataset Description
-- **Homepage:** https://cs.stanford.edu/people/ranjaykrishna/densevid/
-- **Paper:** https://arxiv.org/abs/1705.00754
-
-### Dataset Summary
-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.
-### Languages
-The captions in the dataset are in English.
-## Dataset Structure
-### Data Fields
-- `video_id` : `str` unique identifier for the video
-- `video_path`: `str` Path to the video file
--`duration`: `float32` Duration of the video
-- `captions_starts`: `List_float32` List of timestamps denoting the time at which each caption starts 
-- `captions_ends`: `List_float32` List of timestamps denoting the time at which each caption ends 
-- `en_captions`: `list_str` List of english captions describing parts of the video
-
-### Data Splits
-|             |train  |validation| test  | Overall |
-|-------------|------:|---------:|------:|------:|
-|# of videos|10,009	|4,917     |4,885 |19,811 |
-### Annotations
-Quoting [ActivityNet Captions' paper](https://arxiv.org/abs/1705.00754): \
-"Each annotation task was divided into two steps: (1)
-Writing a paragraph describing all major events happening
-in the videos in a paragraph, with each sentence of the paragraph describing one event, and (2) Labeling the
-start and end time in the video in which each sentence in the
-paragraph event occurred."
-### Who annotated the dataset?
-Amazon Mechnical Turk annotators
-### Personal and Sensitive Information
-Nothing specifically mentioned in the paper.
-## Considerations for Using the Data
-### Social Impact of Dataset
-[More Information Needed]
-### Discussion of Biases
-[More Information Needed]
-### Other Known Limitations
-[More Information Needed]
-## Additional Information
-### Licensing Information
-[More Information Needed]
-### Citation Information
-```bibtex
-@InProceedings{tgif-cvpr2016,
-@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}
-}
-```
-### Contributions
-Thanks to [@leot13](https://github.com/leot13) for adding this dataset.

all/activity_net_captions-test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3dd10bd12864a3d8902b17591e3a9a0e25dc3abbcc348beb62efe4cbb51bd58
+size 971393

all/activity_net_captions-train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf9d9e63c457382fbf29badb7b03d77f402d04f14166ad0bbd5235175cdf2a4c
+size 2282807

all/activity_net_captions-validation.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e59c1e5235418248579a8275413f424e49e9b18a1c3ef1607751887c9216b72
+size 1072766