datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Dynamically-Generated-Hate-Speech-Dataset
Dataset Card for dynamically generated hate speech dataset
Dataset Summary
This is a copy of the Dynamically-Generated-Hate-Speech-Dataset, presented in this paper by
Bertie Vidgen, Tristan Thrush, Zeerak Waseem and Douwe Kiela
Original README from GitHub
Dynamically-Generated-Hate-Speech-Dataset
ReadMe for v0.2 of the Dynamically Generated Hate Speech Dataset from Vidgen et al. (2021). If you use the dataset, please cite our paper in the… See the full description on the dataset page: https://huggingface.co/datasets/LennardZuendorf/Dynamically-Generated-Hate-Speech-Dataset.Dynamic-Topic-RedPajama-Data-1T-100k-SubSample-max-1k-tokens
Dynamic Topic Modeling Dataset: RedPajama-1T SubSample (100k samples, 1k tokens)
📝Check out the Blog Post
This dataset represents a curated subset of the RedPajama-1T Sample dataset, specifically processed for dynamic topic modeling applications. It contains 100,000
samples from the original dataset, with each document limited to the first 1,024 tokens for consistent processing.
Dataset Overview
Name:… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/Dynamic-Topic-RedPajama-Data-1T-100k-SubSample-max-1k-tokens.alignment_recovery_dynamics_v01Clarus Alignment Recovery Dynamics v0.1
This dataset measures recovery after an alignment flip.
Focus
Not only whether a system flips
But whether it can recover
And whether it relapses under renewed pressure
Design
One row per step
Steps form a trajectory grouped by case_id
A recovery window defines how quickly recovery must occur
Columns
flip_signal_expected
none, early_warning, flip, cascade
first_flip_step_expected
First step where a flip is expected, or -1
recovery_expected
true if… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/alignment_recovery_dynamics_v01.clinical_alignment_recovery_dynamics_v0.1Clinical Alignment Recovery Dynamics
Measures whether a model corrects earlier clinical errors when new signals appear.
Output JSON
recovered
recovery_type
correct_action
Runpython scorer.py --predictions predictions.jsonl --test_csv data/test.csv