datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
iclr-wm-backup-public
ICLR Watermark Benchmark — backup overflow (public part)
Companion to the private repo Aak975/iclr-wm-backup, which reached its
storage quota. Together the two repos form ONE backup — every file exists in
exactly one of them, with the same layout:
archives/<sub>/part-0000 ... part-NNNN, MANIFEST.json
restore one archive: cat part-* | zstd -d | tar -x
MANIFEST.json = {"parts": N, "sha256": <whole-stream>, "total_bytes": M}
This public part holds only shareable image data… See the full description on the dataset page: https://huggingface.co/datasets/Aak975/iclr-wm-backup-public.cdr_bigbio_processedSindhi-Intelligence-Core-SFT
🧠 Sindhi Intelligence Core SFT
This is a premium, high-density instruction dataset designed for training Large Language Models (LLMs) to master the Sindhi language. With 361,225 rows, it provides a robust foundation for grammar, factual knowledge, and logical reasoning.
📊 Dataset Summary
This dataset was created by consolidating multiple high-quality Sindhi corpora into a unified ChatML format. It is specifically optimized for Supervised Fine-Tuning (SFT).
📁… See the full description on the dataset page: https://huggingface.co/datasets/aakashMeghwar01/Sindhi-Intelligence-Core-SFT.hi-en-noisy-vad-benchmark
Hindi-English Noisy VAD Benchmark
Version 0.1.0 is a deterministic, evaluation-only benchmark with 78
mono PCM16 WAV files at 16 kHz: six clean speech controls and 72 mixtures spanning
six speech sources, three real noise categories, and four SNRs (20, 10, 5, 0 dB).
Intended use
Use this dataset to compare voice-activity detectors under matched Hindi/English
noise conditions and to tune thresholds. It is too small and insufficiently diverse
for model training… See the full description on the dataset page: https://huggingface.co/datasets/Aakash22134/hi-en-noisy-vad-benchmark.pico_bigbio_processedresearch-paper-agent-reasoning-traces-unverified