license: mit
task_categories:
- video-classification
- text-to-video
tags:
- large-scale
- ncert
- math
- education
- raw-video
- zulense
pretty_name: 'Rita: 350GB Raw Educational Video Corpus'
size_categories:
- 100G<n<1T
📚 Rita: Large-Scale Raw Educational Video Corpus (348 GB)
The Foundation Layer for the Zulense Imagination Engine
This repository contains the unfiltered, raw video ingestion used for the pre-training of Zulense's educational models. It consists of approximately 348 GB of Class 8 Mathematics lectures (aligned with the Indian NCERT curriculum), aggregated from diverse open educational sources.
💾 Dataset Statistics
- Total Size: ~348 GB
- Format: Raw Video Files (
.mp4,.mkv) - Subject: Mathematics (Algebra, Geometry, Data Handling, Mensuration)
- Curriculum: Class 8 (Indian Standard)
- Diversity: Includes various lighting conditions, teaching styles (blackboard, whiteboard, digital pad), and audio qualities.
🏗️ The Data Pipeline
This dataset represents Layer 0 (Raw Ingestion) of our training pipeline. It is not meant for direct fine-tuning but for Self-Supervised Video Pre-training.
The pipeline flows as follows:
- Layer 0 (This Dataset): Massive Raw Corpus (348GB). Used to teach the model general video concepts (how a hand moves, how chalk appears, how a classroom looks).
- Layer 1 (Filtering): We filter this data into "clean" subsets (removing intros, outros, and non-teaching segments).
- Layer 2 (Classification): Data is sorted into Blackboard/Whiteboard categories (See: Rita_Black_white_mix).
- Layer 3 (Latent Encoding): Data is compressed via VAE for the final training run.
⚠️ Storage Warning & Usage
This is a massive dataset. Do not attempt to clone this repository unless you have significant disk space and bandwidth.
Recommended Usage (Streaming)
We recommend using the Hugging Face streaming=True feature or downloading specific shards if you are exploring the data.
from datasets import load_dataset
# Stream the dataset without downloading 350GB locally
dataset = load_dataset("ProgramerSalar/Rita", split="train", streaming=True)
print("Iterating through video samples...")
for sample in dataset.take(5):
print(f"Video Path: {sample['path']}")