machine
Datasets
All datasets matching “machine”gsm-hard
Dataset Summary
This is the harder version of gsm8k math reasoning dataset (https://huggingface.co/datasets/gsm8k).
We construct this dataset by replacing the numbers in the questions of GSM8K with larger numbers that are less common.
Supported Tasks and Leaderboards
This dataset is used to evaluate math reasoning
Languages
English - Numbers
Dataset Structure
dataset = load_dataset("reasoning-machines/gsm-hard")
DatasetDict({
train: Dataset({… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-machines/gsm-hard.mmmu-mkNLG-Machine-Translation
SEA Machine Translation
SEA Machine Translation evaluates a model's ability to translate a document from a source language into a target language coherently and fluently. It is sampled from FLORES 200 for Burmese, Chinese, English, Indonesian, Khmer, Malay, Tamil, Thai, and Vietnamese, and NusaX for Indonesian, Javanese, and Sundanese.
Supported Tasks and Leaderboards
SEA Machine Translation is designed for evaluating chat or instruction-tuned large language models… See the full description on the dataset page: https://huggingface.co/datasets/aisingapore/NLG-Machine-Translation.data-unlearning-benchDataset for the evaluation of data-unlearning techniques using KLOM (KL-divergence of Margins).
How KLOM works:
KLOM works by:
training N models (original models)
Training N fully-retrained models (oracles) on forget set F
unlearning forget set F from the original models
Comparing the outputs of the unlearned models from the retrained models on different points
(specifically, computing the KL divergence between the distribution of margins of oracle models and distribution of… See the full description on the dataset page: https://huggingface.co/datasets/machine-unlearning-bench/data-unlearning-bench.MachineLearning
Machine Learning Tier
This dataset is a collection of synthetic microlensing light curves from the Nancy Grace Roman Space Telescope Galactic Bulge Time Domain Survey. It is intended for the training and benchmarking of machine learning models for microlensing event classification, parameter estimation, and anomaly detection.
The raw distribution of event properties is not representative of what Roman will see, but should span a statistically larger set of events. More… See the full description on the dataset page: https://huggingface.co/datasets/RGES-PIT/MachineLearning.G1_WBT_Inspire_Put_Clothes_into_Washing_Machine
Data Structure
Observations
observation.state.ee_state (12)
End-effector states of the robot.
Computed via forward kinematics (FK) from the root link to the left and right end-effectors.
Includes the contribution of the waist.
Represented as concatenated poses of both end-effectors.
observation.state.hand_state (12 or 2)
Finger states for both hands. The dimensionality depends on the hand type.
Inspire Hand (range: 0.0 – 1.0, open → close)… See the full description on the dataset page: https://huggingface.co/datasets/unitreerobotics/G1_WBT_Inspire_Put_Clothes_into_Washing_Machine.