datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sec-material-contracts-qa800+ EDGAR contracts with PDF images and key information extracted by the OpenAI GPT-4o model.
The key information is defined as follows:
class KeyInformation(BaseModel):
agreement_date : str = Field(description="Agreement signing date of the contract. (date)")
effective_date : str = Field(description="Effective date of the contract. (date)")
expiration_date : str = Field(description="Service end date or expiration date of the contract. (date)")
party_address : str =… See the full description on the dataset page: https://huggingface.co/datasets/chenghao/sec-material-contracts-qa.hle_material_science
HLE Material Science: A Specialized Benchmark for Materials Science
A Materials Science Subset of Humanity's Last Exam (HLE)
Overview
HLE Material Science is a carefully curated materials science subset derived from the Humanity's Last Exam (HLE) dataset, containing 106 high-quality expert-level questions covering 25+ materials science subfields, with 97% of questions rated as high confidence.
This dataset is designed to evaluate large language models'… See the full description on the dataset page: https://huggingface.co/datasets/TalentZHOU/hle_material_science.chinese-materials-science-open-intelligence
🔬 Chinese Materials Science & Metallurgy Open Intelligence Dataset
Curated open intelligence dataset providing English research briefs, authoritative DOIs, executive summaries, and high-resolution micrographs of breakthrough Chinese scientific research in Materials Science, Metallurgy, Advanced Alloys, and Mining Engineering.
[!IMPORTANT]
Data Completeness & Research Authenticity Notice:
Included in this Hugging Face Open Dataset: English structured abstracts, core… See the full description on the dataset page: https://huggingface.co/datasets/simpleG2023/chinese-materials-science-open-intelligence.sec-material-contracts-qa-splittedMixed and filtered version of chenghao/sec-material-contracts-qa and jordyvl/DUDE_subset_100val.
hle_material_science
HLE Material Science: A Specialized Benchmark for Materials Science
A Materials Science Subset of Humanity's Last Exam (HLE)
Overview
HLE Material Science is a carefully curated materials science subset derived from the Humanity's Last Exam (HLE) dataset, containing 106 high-quality expert-level questions covering 25+ materials science subfields, with 97% of questions rated as high confidence.
This dataset is designed to evaluate large language models'… See the full description on the dataset page: https://huggingface.co/datasets/stonelight/hle_material_science.materials-figure-qa
Materials Figure QA
This repository contains 250 figure-grounded multimodal question-answer pairs from recent materials-related arXiv papers. Each item requires interpreting a rendered figure and applying materials-science reasoning. Figures with aspect ratio greater than 4:1 were removed; retained figures were downsampled so their longest dimension is at most 2048 pixels.
The filtered split preserves the original deterministic split:
validation: 128 examples
test: 122 examples… See the full description on the dataset page: https://huggingface.co/datasets/gneubig/materials-figure-qa.synthetic-superconductor-materials-dataset
synthetic-superconductor-materials-dataset
Synthetic Q&A dataset on Superconductor Materials, generated with SDGS (Synthetic Dataset Generation Suite).
Dataset Details
Metric
Value
Topic
Superconductor Materials
Total Q&A Pairs
2649
Valid Pairs
2649
Provider/Model
ollama/gpt-oss:120b
Sources
This dataset was generated from 170 scholarly papers:
#
Title
Authors
Year
Source
QA Pairs
1
Observation of a large-gap… See the full description on the dataset page: https://huggingface.co/datasets/Kylan12/synthetic-superconductor-materials-dataset.gemma4-materials-mechanism-prompts
Gemma 4 Materials-Mechanism Prompt Corpus
This dataset collects the exact scientific prompts and registered prompt metadata used in “Reading and Steering Materials Science-Mechanism Representations in an Open-Weight Language Model” by Markus J. Buehler. It is organized as 21 Hugging Face configurations so that historical development prompts, frozen evaluations, falsification tests, and exploratory follow-ups are not pooled into one ambiguous table.
The release is a prompt and… See the full description on the dataset page: https://huggingface.co/datasets/lamm-mit/gemma4-materials-mechanism-prompts.quantum-simulation-chemistry-materials
Neura Parse — Quantum Simulation of Chemistry & Materials: Encodings, VQE/QPE & Dynamics
An application-deep, code-backed vertical on simulating quantum matter: electronic-structure problems, fermion-to-qubit encodings, Hamiltonian factorizations, ground/excited-state and real-time-dynamics algorithms, and analog simulation, with end-to-end resource estimates and honest classical-competitor accounting. Built with Qiskit Nature, OpenFermion, PennyLane-QChem, and PySCF — far… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-simulation-chemistry-materials.hw-mnlp-2026
Dataset for Multilingual Natural Language Processing (MNLP) Homeworks
This dataset serves for both Homework 1 and Homework 2 of the Multilingual Natural Language Processing (MNLP) course.
Homework 1 - Semantic Search
In the first homework, you are asked to build semantic search systems. You must only use the following variables:
query: A single question in natural language.
query_id: The question (query) identifier.
candidate_chunks: List of candidate answers (only one… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp-course-materials/hw-mnlp-2026.Material-mechanics
Material-mechanics
Dataset Overview
Material-mechanics is a Chinese-language instruction dataset for introductory materials mechanics and engineering mechanics. It is designed for educational question answering, concept review, and the development of domain-focused language-model assistants.
Dataset Details
Maintainer: CYHcyh66
Language: Chinese
License: Apache-2.0
Format: JSON
Split: train
Size: 238 examples
Task: Instruction-following question… See the full description on the dataset page: https://huggingface.co/datasets/CYHcyh66/Material-mechanics.Material_Selection_EvalA benchmark designed to facilitate evaluation and modify the behavior of a foundation model through different existing techniques in the context of material selection for conceptual design.
The data is collected by conducting a survey of experts in the field of material selection. The same questions mentioned in keyquestions.csv are asked to experts.
This can be used to evaluate a Language model performance and its spread compared to a human evaluation.
To get into a more detailed explanation… See the full description on the dataset page: https://huggingface.co/datasets/cmudrc/Material_Selection_Eval.Material-mechanics-merge
Material-mechanics-merge
Dataset Overview
Material-mechanics-merge is an expanded Chinese-language instruction dataset for materials mechanics and engineering mechanics. It provides question--answer examples for building and evaluating domain-focused educational language models.
Dataset Details
Maintainer: CYHcyh66
Language: Chinese
License: Apache-2.0
Format: JSON
Split: train
Size: 774 examples
Task: Instruction-following question answering… See the full description on the dataset page: https://huggingface.co/datasets/CYHcyh66/Material-mechanics-merge.Material_Selection_EvalA benchmark designed to facilitate evaluation and modify the behavior of a foundation model through different existing techniques in the context of material selection for conceptual design.
The data is collected by conducting a survey of experts in the field of material selection. The same questions mentioned in keyquestions.csv are asked to experts.
This can be used to evaluate a Language model performance and its spread compared to a human evaluation.
To get into a more detailed explanation… See the full description on the dataset page: https://huggingface.co/datasets/Frederick001/Material_Selection_Eval.