datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ane-rooflines
ANE Rooflines
Cross-Apple-Silicon performance and fp16-correctness measurements for the Apple
Neural Engine (ANE), collected with ANEForge.
Each row is one machine (grouped by hardware hash; identical silicon in different
chassis stays distinct by model identifier).
See it charted: the ANE leaderboard
ranks these machines by peak GEMM, perf-per-watt, and decode throughput.
These are community-contributed submissions mirrored from the public
bench/results/rooflines/
in the repo.… See the full description on the dataset page: https://huggingface.co/datasets/aneforge/ane-rooflines.AnesBench
AnesBench
Paper | GitHub
Dataset Description
AnesBench is designed to assess anesthesiology-related reasoning capabilities of Large Language Models (LLMs). It provides bilingual (English and Chinese) anesthesiology questions across two separate files. Each question is labeled with a three-level categorization of cognitive demands based on dual-process theory (System 1, System 1.x, and System 2), enabling evaluation of LLMs' knowledge, application, and clinical reasoning… See the full description on the dataset page: https://huggingface.co/datasets/MiliLab/AnesBench.assistant-bot-ner-dataset
NER Dataset for Contact Management Assistant Bot
This dataset is used to train Named Entity Recognition (NER) models for extracting contact information from natural language text.
Supported Entity Types
This dataset extracts the following entity types:
NAME: Person's full name
PHONE: Phone numbers in various formats
EMAIL: Email addresses
ADDRESS: Full street addresses (including building numbers, street names, apartments, cities, states, ZIP codes)
BIRTHDAY: Dates of… See the full description on the dataset page: https://huggingface.co/datasets/aneesa3131/assistant-bot-ner-dataset.aultra-unified-training-data
AUltra Unified Training Data
This dataset package contains the reconstructed chat-format training data used for the AUltra Unified defensive cybersecurity and code-assistant fine-tune.
The dataset was reconstructed from the original preparation scripts, deterministic seeds, local Hugging Face cache, and the same public upstream dataset. The reconstructed split sizes match the documented training run.
Transparency Notice
This dataset is an experimental, partially… See the full description on the dataset page: https://huggingface.co/datasets/Anes-03/aultra-unified-training-data.smollm3-3b-base-blind-spots
SmolLM3-3B-Base Blind Spots
Title & Overview
A curated set of failure cases for HuggingFaceTB/SmolLM3-3B-Base, showcasing blind spots discovered while probing the 3B-parameter base pre-training checkpoint released in July 2025. Each entry captures a prompt, the expected aligned behaviour, and the model's actual output. The dataset illustrates common failure patterns observed when probing the base model without any instruction tuning, RLHF, or safety fine-tuning applied.… See the full description on the dataset page: https://huggingface.co/datasets/aneeshadas02/smollm3-3b-base-blind-spots.ComfyUI_Sonic_dataset_sonicCloudanet_sampled_sftcleaned-mimic-1to5k-90k-part2AnesBench
Dataset Description
AnesBench is designed to assess anesthesiology-related reasoning capabilities of Large Language Models (LLMs). It provides bilingual (English and Chinese) anesthesiology questions across two separate files. Each question is labeled with a three-level categorization of cognitive demands based on dual-process theory (System 1, System 1.x, and System 2), enabling evaluation of LLMs' knowledge, application, and clinical reasoning abilities across diverse linguistic… See the full description on the dataset page: https://huggingface.co/datasets/priby045/AnesBench.MixEval_and_RACE_combined_mcq_dataregdataSakalti__Anemoi-3B-details
Dataset Card for Evaluation run of Sakalti/Anemoi-3B
Dataset automatically created during the evaluation run of model Sakalti/Anemoi-3B
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Sakalti__Anemoi-3B-details.anet_ret_train_global_rzen_sftanet_sampled_sft_validanet_ret_val_1000_sftappointments-training-datasetDatabricks-dolly-15k-without-nulldatalake_shuffle.jsonlanet_caption_concat_sfttrainset 앞에서부터 1000개 골라와서
gpt 로 만들고 필터링한 데이터셋