mit
Datasets
All datasets matching “mit”pile-val-backupThis is a backup for the pile val dataset downloaded from here: https://the-eye.eu/public/AI/pile/val.jsonl.zst
Please respect the original license of the dataset.
MIT-Indoor-ScenesOHLCV-1m
📈 OHLCV-1m: US Stock Market Minute-Level Candlestick Data (1992–2026)
This dataset provides minute-level OHLCV (Open, High, Low, Close, Volume) candlestick data for thousands of U.S. stocks across multiple decades (1992 to 2026). The data was originally sourced from Finnhub.io, a real-time market data provider.
It has been aggregated and reformatted from monthly .tar archives into clean and unified Parquet files — one per month — and uploaded to the Hugging Face Hub for easy… See the full description on the dataset page: https://huggingface.co/datasets/mito0o852/OHLCV-1m.MIT_environmental_impulse_responsesMIT Environmental Impulse Response Dataset
The audio recordings in this dataset are originally created by the Computational Audition Lab at MIT. The source of the data can be found at: https://mcdermottlab.mit.edu/Reverb/IR_Survey.html.
The audio files in the dataset have been resampled to a sampling rate of 16 kHz. This resampling was done to reduce the size of the dataset while making it more suitable for various tasks, including data augmentation.
The dataset consists of 271 audio files… See the full description on the dataset page: https://huggingface.co/datasets/davidscripka/MIT_environmental_impulse_responses.Inf-Stream-Train
Inf-Stream-Eval
Inf-Stream-Eval is a benchmark for evaluating vision-language models (VLMs) on near-infinite video streams. It consists of videos averaging over two hours in length that require dense, per-second alignment between video frames and text.
This dataset was introduced in the paper StreamingVLM: Real-Time Understanding for Infinite Video Streams.
Project Page: https://streamingvlm.hanlab.ai
GitHub Repository: https://github.com/mit-han-lab/streaming-vlm… See the full description on the dataset page: https://huggingface.co/datasets/mit-han-lab/Inf-Stream-Train.CHB-MIT