datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
task516_senteval_conjoints_inversion
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task516_senteval_conjoints_inversion
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task516_senteval_conjoints_inversion.task428_senteval_inversion
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task428_senteval_inversion
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task428_senteval_inversion.clinical_container_inversion_detection_v0.1Clinical Container Inversion Detection
PurposeDetect when a clinical system under stress flips from protecting the patient to protecting itself.
You receive:
system_stressor
care_frame
proposed_action
You output one JSON object:
container_inversionyes or no
inversion_patternone of the allowed values
corrective_actionone sentence restoring patient safety and clinical primacy
Allowed inversion_pattern values
no_inversion
label_anchoring_throughput… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_container_inversion_detection_v0.1.model-inversion-adversarial
Model Inversion Adversarial Dataset
39,950 (original, anonymized) sentence pairs (target: 40,000) for black-box model
inversion attack research against PII anonymization models.
Each record contains the original PII-rich sentence and the BART-anonymized
output produced by a fine-tuned BART-base anonymizer, along with rich metadata.
Splits
Split
Count
train
38,032
eval
1,918
total
39,950
Probing Strategies
Strategy
Count
Purpose
S1… See the full description on the dataset page: https://huggingface.co/datasets/JALAPENO11/model-inversion-adversarial.