datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
kurdish-kurmanji-grammar-error-correctionThis dataset is for developing and evaluating grammatical error correction (GEC) models,
like Grammarly, for Kurdish Kurmanji. Incorrect sentences were manually collected
from YouTube comment sections of Kurdish videos and X(Twitter) and Muzaffer Cıkay added their corrections.
The source videos are documented in the source.txt file.
Usage
from datasets import load_dataset
dataset = load_dataset("muzaffercky/kurdish-kurmanji-typo-correction", split="train")
print(dataset)
vietnamese-error-correction-corpus
Data Summary
The model is trained on a Vietnamese text error correction dataset constructed from real-world noisy inputs. The dataset contains approximately 70,000 sentence pairs and is split into training, validation, and test sets.
• Data Source: Crawled Vietnamese social media comments, reflecting informal and user-generated text.
• Annotation Method: Automatically labeled using a large language model, which generates corrected versions of noisy inputs.
• Data… See the full description on the dataset page: https://huggingface.co/datasets/yammdd/vietnamese-error-correction-corpus.quantum-error-correction-failure-v0.1
quantum-error-correction-failure-v0.1
What this dataset does
This dataset evaluates whether models can detect instability in quantum error correction regimes.
Each row represents a simplified quantum computing scenario where logical qubits are protected using error correction.
The task is to determine whether the correction mechanism remains stable or fails due to noise and correction latency.
Core stability idea
Quantum error correction works by detecting and… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/quantum-error-correction-failure-v0.1.vi-error-correction-v2vi-error-correction-2.0error-correction-viquantum_error_correction_telemetry