The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 127, in _split_generators
self.info.features = datasets.Features.from_arrow_schema(pq.read_schema(f))
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 2392, in read_schema
file = ParquetFile(
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 328, in __init__
self.reader.open(
File "pyarrow/_parquet.pyx", line 1656, in pyarrow._parquet.ParquetReader.open
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
LingoQA Datasets README
Overview
The LingoQA datasets comprise a collection of complementary datasets designed for training and evaluating machine learning models on video understanding and question-answering tasks. These datasets are categorized into three main types: action, scenery, and evaluation, each containing video segments, questions, and answers, along with associated images to aid in visual understanding tasks.
Directory Structure
The datasets are organized as follows:
LingoQA/
├── action/
│ ├── train.parquet # Training data for action-related questions
│ └── images.zip # Zipped directory of images related to action segments
├── scenery/
│ ├── train.parquet # Training data for scenery-related questions
│ └── images.zip # Zipped directory of images related to scenery segments
└── evaluation/
├── val.parquet # Validation data for evaluating models
└── images.zip # Zipped directory of images related to evaluation segments
Data Format
Each .parquet file within the datasets contains the following columns:
segment_id: An md5 hash that uniquely identifies each video segment.question_id: A unique identifier for each question associated with a video segment.images: An array of relative paths pointing to images that correspond to the video segment. Example format: ['images/train/hash_segment_1/0.jpg', ...].question: The question text related to the video segment.answer: The answer text related to the question posed.
How to Use
Preparing the Data
- Extract Images: First, unzip the images.zip files in their respective directories to access the images associated with each dataset.
unzip action/images.zip -d action/
unzip scenery/images.zip -d scenery/
unzip evaluation/images.zip -d evaluation/
- Load Datasets: You can load the
.parquetfiles using Python with libraries such as pandas or pyarrow. Here's an example of how to load a dataset:
import pandas as pd
# Load an example dataset
action_train = pd.read_parquet('action/train.parquet')
Using the Data
Model Training: Use the train.parquet files from the action and scenery datasets to train your machine learning models. These datasets provide a rich set of questions and answers along with images to facilitate training models capable of understanding and responding to queries about video content.
Model Evaluation: The evaluation/val.parquet file is intended for validating the performance of your models. It offers a separate set of questions and answers to test your model's ability to generalize to new data.
License
Please ensure to review the license agreement associated with the LingoQA datasets before using them for your projects.
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