datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
assay-receipts
Assay Receipt Corpus
Signed internal receipts from an inference provider that is sometimes cheating, together
with the verdict an auditor reached on each one and the ground truth of which model actually
served the request.
Each row is a real receipt, not a summary statistic: it carries the prompt and output token
ids, the JL-projected sketch of the provider's hidden_states, the sign/rank invariants, and
an HMAC signature. With the gpt2 weights you can recompute the sketch… See the full description on the dataset page: https://huggingface.co/datasets/NagaYu/assay-receipts.Assay-aware-BindingDB
Assay-aware BindingDB
Assay-aware BindingDB is a collection of protein–ligand binding
records organized by experimental assay type. Each row represents a BindingDB
reactant set and includes its measured affinity, source publication, original
experimental context, and an assay-specific structured description.
The complete dataset remains available as the full split. Four assay
configurations provide direct access to ITC, SPR, FPA, or RBA records, and 40
training-compatible… See the full description on the dataset page: https://huggingface.co/datasets/anonymousapple/Assay-aware-BindingDB.assay-qi
ASSAY-QI v2.0 — Quantum-Augmented BFSI Attack Suite
ASSAY-QI (Adversarial Safety Suite for AI — Quantum-Inspired) is a 1,273-prompt adversarial corpus for BFSI AI safety evaluation, generated using quantum circuit Born machine (QCBM) sampling and simulated annealing to target the decision boundary of BFSI safety classifiers.
Published by Zytra · Part of FinProof v1 · License: CC BY 4.0
What makes ASSAY-QI different
Standard adversarial datasets use template… See the full description on the dataset page: https://huggingface.co/datasets/Zytra/assay-qi.