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.scm-sql
SCM-SQL — a supply-chain natural-language-to-SQL evaluation set
500 (question, gold SQL) pairs authored against the live Odoo 17 supply-chain
schema, spanning 6 explicit complexity levels including multi-turn dialogues.
Built for the dissertation Domain-Aware Multi-Agent Natural-Language-to-SQL for
Enterprise Supply Chain Intelligence by Aniruddha Prakash Kawarase (BITS Pilani
WILP, 2026). Released as a public evaluation benchmark so other researchers can
compare domain-aware… See the full description on the dataset page: https://huggingface.co/datasets/AniruddhaAI/scm-sql.sc-matlab-validated
SC MATLAB Validated
Validated MATLAB/Octave code–pseudocode pairs for program comprehension and synthesis research.
Each sample was filtered from semran1/yulan-code-MNBVC-matlab,
converted to pseudocode with Gemini, regenerated back to MATLAB, and kept only when Octave execution output matched the original.
Fields
Column
Description
sample_id
Numeric sample index
code
Original MATLAB/Octave source
pseudocode
LLM-generated pseudocode from the… See the full description on the dataset page: https://huggingface.co/datasets/philip120/sc-matlab-validated.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.