datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
machinelearninglm-scm-synthetic-tabularml
MachineLearningLM Pretraining Corpus
This repository contains the pretraining corpus for MachineLearningLM, a framework designed to equip large language models (LLMs) with robust in-context machine learning (ML) capabilities. The dataset consists of ML tasks synthesized from millions of structural causal models (SCMs), spanning various shot counts up to 1,024. It is designed to enable LLMs to learn from many in-context examples on standard ML tasks purely via in-context learning… See the full description on the dataset page: https://huggingface.co/datasets/MachineLearningLM/machinelearninglm-scm-synthetic-tabularml.tokenization-multiplicity-data
Dataset: Tokenization Multiplicity Leads to Arbitrary Price Variation in LLM-as-a-service
This dataset contains the official experiment inference traces for the paper Tokenization Multiplicity Leads to Arbitrary Price Variation in LLM-as-a-service by Ivi Chatzi, Nina Corvelo Benz, Stratis Tsirtsis and Manuel Gomez-Rodriguez.
📂 Dataset Structure
The dataset is organized into folders as follows:
.\{model}\{task}\{lang}\{seed}_{10*temperature}.jsonl
where {model}… See the full description on the dataset page: https://huggingface.co/datasets/Human-Centric-Machine-Learning/tokenization-multiplicity-data.strategic-ttc-data
Dataset: Strategic Test-Time Compute (TTC)
This dataset contains the official experiment inference traces for the paper "Test-Time Compute Games" (arXiv:2601.21839).
It includes full model generations, token counts, and correctness verifications for various Large Language Models (LLMs) across three major reasoning benchmarks: GSM8K, AIME, and GPQA.
This data allows researchers to analyze the relationship between test-time compute and model performance without needing to re-run… See the full description on the dataset page: https://huggingface.co/datasets/Human-Centric-Machine-Learning/strategic-ttc-data.quantum-machine-learning-theory
Neura Parse — Quantum Machine Learning Theory: Trainability, Generalization & Learning From Quantum Data
A research-depth, proof-oriented vertical on the learning theory of quantum models and quantum data. Covers why parameterized quantum circuits train or don't (barren plateaus), what they can represent, when they generalize or provably beat classical models, and — for quantum data — how to predict properties of unknown states/channels with few measurements (classical… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-machine-learning-theory.quantum-machine-learning-models
Neura Parse — Quantum Machine Learning Models: Encodings, Kernels, QNNs & Generative/Deep Architectures
A hands-on, code-first vertical on quantum models that learn from data. Spans data encodings/feature maps, variational classifiers, quantum kernels/QSVMs, and quantum neural networks through modern generative and deep architectures (quantum GANs, circuit Born machines, quantum Boltzmann machines, QCNNs, quantum autoencoders, quantum RL, and quantum… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-machine-learning-models.machine-learning-glossary-ai
📚 Machine Learning & AI Technical Glossary Dataset
Curated benchmark dataset covering core terminology, mathematical formulations, and engineering principles across Deep Learning, Transformers, and MLOps.
Maintained and documented by AheadMint.
📌 Dataset Overview
Category
Key Concepts
Reference Documentation
Neural Networks
Backpropagation, Attention, Loss Functions
AheadMint Deep Learning
Generative AI
RAG Architectures, Vector Embeddings… See the full description on the dataset page: https://huggingface.co/datasets/aheadmint/machine-learning-glossary-ai.task718_mmmlu_answer_generation_machine_learning
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task718_mmmlu_answer_generation_machine_learning
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task718_mmmlu_answer_generation_machine_learning.machinelearninglm-scm-synthetic-tabularml
MachineLearningLM Pretraining Corpus
This repository contains the pretraining corpus for MachineLearningLM, a framework designed to equip large language models (LLMs) with robust in-context machine learning (ML) capabilities. The dataset consists of ML tasks synthesized from millions of structural causal models (SCMs), spanning various shot counts up to 1,024. It is designed to enable LLMs to learn from many in-context examples on standard ML tasks purely via in-context learning… See the full description on the dataset page: https://huggingface.co/datasets/ESHMO-AI-2047/machinelearninglm-scm-synthetic-tabularml.