datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ASCEND
Dataset Card for ASCEND
Dataset Summary
ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set.… See the full description on the dataset page: https://huggingface.co/datasets/CAiRE/ASCEND.ascender-rl-artifactsAscend-COT-v2-json
AscendKernelGen/Ascend-COT-v2-json
AscendKernelGen/Ascend-CoT-v2-json contains a subset of the full Ascend-CoT dataset, which will be released in stages. The Ascend-CoT Dataset is a high-quality, domain-specific dataset that incorporates Chain-of-Thought (CoT) reasoning derived from real-world kernel implementations. It combines three types of reasoning: documentation-based reasoning, code-centric reasoning extracted from actual NPU kernel code, and general reasoning chains that… See the full description on the dataset page: https://huggingface.co/datasets/AscendKernelGen/Ascend-COT-v2-json.Ascend-COT-v2-packed
AscendKernelGen/Ascend-COT-v2-packed
AscendKernelGen/Ascend-CoT-v2-packed contains a subset of the full Ascend-CoT dataset, which will be released in stages. The Ascend-CoT Dataset is a high-quality, domain-specific dataset that incorporates Chain-of-Thought (CoT) reasoning derived from real-world kernel implementations. It combines three types of reasoning: documentation-based reasoning, code-centric reasoning extracted from actual NPU kernel code, and general reasoning chains… See the full description on the dataset page: https://huggingface.co/datasets/AscendKernelGen/Ascend-COT-v2-packed.Ascend-COT-v1
AscendKernelGen/Ascend-COT-v1
AscendKernelGen/Ascend-CoT-v1 contains a small subset of the full Ascend-CoT dataset, which will be released in stages. The Ascend-CoT Dataset is a high-quality, domain-specific dataset that incorporates Chain-of-Thought (CoT) reasoning derived from real-world kernel implementations. It combines three types of reasoning: documentation-based reasoning, code-centric reasoning extracted from actual NPU kernel code, and general reasoning chains that… See the full description on the dataset page: https://huggingface.co/datasets/AscendKernelGen/Ascend-COT-v1.ascend_MIXED_cleaned_vadascend_ZH_cleaned_vadascend_EN_cleaned_vadAscend-CoT-v3-json
Ascend-CoT-v3-json
Ascend-CoT-v3-json is an Ascend C / CANN supervised fine-tuning dataset for custom operator development. It contains cleaned CoT-style samples for Ascend C kernel implementation, tiling logic, CANN API usage, debugging, and operator-development reasoning.
The release is organized into two final SFT subsets in one dataset repository.
Related Artifacts
Paper: AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural… See the full description on the dataset page: https://huggingface.co/datasets/AscendKernelGen/Ascend-CoT-v3-json.ASCEND_CLEAN
Dataset Card for Dataset Name
This dataset is derived from CAiRE/ASCEND. More information is available at https://huggingface.co/datasets/CAiRE/ASCEND.
Removed 嗯 呃 um uh
Resolved [UNK]'s using whisper-medium
Usage
Default utterances with cleaned transcripts
from datasets import load_dataset
data = load_dataset("georgechang8/ASCEND_CLEAN") # add split="train" for train set, etc.
Concatenated 30s utterances with cleaned transcripts… See the full description on the dataset page: https://huggingface.co/datasets/georgechang8/ASCEND_CLEAN.nanochat-ascend-dataset
nanochat-ascend-dataset
Unified training and evaluation data bundle for nanochat-ascend.
This repository is designed to make the nanochat-ascend training procedure easy to reproduce. Instead of asking users to collect multiple task and evaluation datasets and manually reconstruct the expected directory structure, this repository preserves the local filesystem layout expected by the training code.
The intended usage is simple:
place this repository at .cache/dataset
download… See the full description on the dataset page: https://huggingface.co/datasets/leideng/nanochat-ascend-dataset.ascend
Dataset Card for ASCEND
Dataset Summary
ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set.… See the full description on the dataset page: https://huggingface.co/datasets/filwsyl/ascend.qwen38-27b-triton-ascend-rl-trajectories
Qwen3.8-27B Triton-Ascend RL Trajectories
This dataset contains 1,000 multi-turn trajectories for Triton-Ascend kernel generation. Every included trajectory passed compilation and correctness validation on one official npu-kernelbench workload. Qwen3.8-27B generated an initial solution and received evaluator feedback for up to five calls.
Dataset Viewer subsets
trajectories (default): one row per sample with only messages. The initial system and task user… See the full description on the dataset page: https://huggingface.co/datasets/Yukki1011/qwen38-27b-triton-ascend-rl-trajectories.mindX-ascend-weightsAscendC_CC_AgentCodeSwitchingSpeechIdentification_ASCENDASCEND-phoneme
Dataset Summary
This dataset is a modified version of the ASCEND dataset which consists of spontaneous Mandarin-English code-switched speech. The ASCEND dataset was published by Lovenia et al. (2022) (Check here for the dataset and here for the paper).
This dataset adds a phonetic transcription column to the dataset using the eSpeak backend from the phonemizer library created by Bernard et al. (2021) (Check it out here).
the following documentation is a modified version of… See the full description on the dataset page: https://huggingface.co/datasets/katyayego/ASCEND-phoneme.vllm-ascend-310PAscendingHarmonicReasoningInstruction
Ahri: Ascending Harmonic Reasoning Instruction (v2.0)
A controlled evaluation framework for Time-Series Language Models.
26 synthetic tasks across 5 tiers, parametric signals on discrete grids at
fs=200 Hz, N=1024.
Layout
data/
{task_id}/
manifest.json
train.parquet
val.parquet
test.parquet
train and val exclude each task's held-out parameter region (paper
Appendix C); test covers the full range, and the per-example held flag
marks which test examples… See the full description on the dataset page: https://huggingface.co/datasets/TonyChen06/AscendingHarmonicReasoningInstruction.nanochat-ascend-eval
Introduction
This dataset contains the evaluation dataset of nanochat-asecnd. It includes the following subsets:
commonsense_reasoning
language_understanding
programming
reading_comprehension
safety
symbolic_problem_solving
world_knowledge
For Hugging Face dataset viewer compatibility, language_understanding, reading_comprehension, symbolic_problem_solving, and world_knowledge are exposed as schema-specific sub-configs in the datacard metadata. Each sub-config only groups… See the full description on the dataset page: https://huggingface.co/datasets/leideng/nanochat-ascend-eval._ASCEND_ZH_cleanedecu_tuningAscendDataascendRLASCENDASCEND-mixed-to-chinese-translationASCEND_MIXED_ASCEND_MIXED_cleanednanochat-ascend-task
Introduction
This dataset contains the task dataset of nanochat-asecnd, excluding identity_conversations.jsonl and words_alpha.txt. It includes the following subsets:
ai2_arc
gsm8k
humaneval
mmlu
smol-smoltalk
Layout Tree
├── ai2_arc
│ ├── ARC-Challenge
│ │ ├── test-00000-of-00001.parquet
│ │ ├── train-00000-of-00001.parquet
│ │ └── validation-00000-of-00001.parquet
│ ├── ARC-Easy
│ │ ├── test-00000-of-00001.parquet
│ │ ├──… See the full description on the dataset page: https://huggingface.co/datasets/leideng/nanochat-ascend-task.ASCEND_EN