datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
xlam-function-calling-60k-parsed
[PARSED] APIGen Function-Calling Datasets (xLAM)
This dataset contains the full data from the original Salesforce/xlam-function-calling-60k
Subset name
multi-turn
parallel
multiple definition
Last turn type
number of dataset
xlam-function-calling-60k
no
yes
yes
tool_calls
60000
This is a re-parsing formatting dataset for the xLAM official dataset.
Load the dataset
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/xlam-function-calling-60k-parsed.arxiv_s2orc_parsed
Dataset Card for "ArtifactAI/arxiv_s2orc_parsed"
Dataset Description
https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_s2orc_parsed
Dataset Summary
AlgorithmicResearchGroup/arxiv_s2orc_parsed is a subset of the AllenAI S2ORC dataset, a general-purpose corpus for NLP and text mining research over scientific papers,
The dataset is filtered strictly for ArXiv papers, including the full text for each paper. Github links have been extracted… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_s2orc_parsed.bfcl-v1-non-live-ast-parsed
[PARSED] BFCL V1 AST (non-live python)
The data in this dataset is a subset of the original gorilla-llm/Berkeley-Function-Calling-Leaderboard
Subset name
multi-turn
parallel
multiple definition
Last turn type
number of dataset
simple
no
no
no
tool_calls
400
multiple
no
no
yes
tool_calls
200
parallel
no
yes
no
tool_calls
200
parallel_multiple
no
yes
yes
tool_calls
200
This is a re-parsing formatting dataset for Python AST parts from V1 of the official dataset of… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/bfcl-v1-non-live-ast-parsed.toolace-parsed
[PARSED] ToolACE
The data in this dataset is a subset of the original Team-ACE/ToolACE
Subset name
multi-turn
parallel
multiple definition
Last turn type
number of dataset
toolace
yes
yes
yes
complex
11k
This is a re-parsing formatting dataset for the ToolACE official dataset.
Load the dataset
from datasets import load_dataset
ds = load_dataset("minpeter/toolace-parsed")
print(ds)
# DatasetDict({
# train: Dataset({
# features:… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/toolace-parsed.hermes-function-calling-v1-parsed
[PARSED] Hermes Function-Calling V1
The data in this dataset is a subset of the original NousResearch/hermes-function-calling-v1
Subset name
multi-turn
parallel
multiple definition
Last turn type
number of dataset
func-calling
yes
yes
yes
complex
1.8k
func-calling-singleturn
no
yes
yes
tool_calls
1.8k
glaive-function-calling-5k
yes
?
yes
complex
5k
func-calling-singleturn: Single turn function calls
func-calling: Multi-turn conversation function calls… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/hermes-function-calling-v1-parsed.agenda-parser-tool-traces
Agenda Parser — tool-calling reasoning traces
ReAct tool-calling traces for the Agenda Parser
agents: each row is one agent step — a {system, user, assistant} chat example
where the assistant emits a single JSON action {"thought", "tool", "args"}.
Two agents are covered (tagged by meta.domain):
agenda — the uploaded-packet research agent, over real public-meeting agenda
packets (tools: list/read items, semantic + exact search, summarize, report).
Each agenda row's meta.unit_id… See the full description on the dataset page: https://huggingface.co/datasets/build-small-hackathon/agenda-parser-tool-traces.rosettacode-parsed
Data Origins
Original dataset: https://huggingface.co/datasets/jondurbin/rosettacode-raw/
Cleaner code: https://github.com/the-crypt-keeper/rosettacode-parser
Data Fields
Field
Type
Description
title
string
problem title
task
string
problem description
language
string
solution language/variant
soulution
string
solution source code
Languages
One .jsonl is provided per language group, the sublanguage field in the data denotes the… See the full description on the dataset page: https://huggingface.co/datasets/mike-ravkine/rosettacode-parsed.apigen-mt-5k-parsed
[PARSED] APIGen-MT-5k
The data in this dataset is a full of the original Salesforce/APIGen-MT-5k
Subset name
multi-turn
parallel
multiple definition
Last turn type
number of dataset
apigen-mt-5k
yes
no
yes
complex
5k
This is a re-parsing formatting dataset for the APIGen-MT-5k official dataset.
Load the dataset
from datasets import load_dataset
ds = load_dataset("minpeter/apigen-mt-5k-parsed")
print(ds)
# DatasetDict({
# train: Dataset({
#… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/apigen-mt-5k-parsed.glaive-function-calling-v2-ru-parsed
Glaive Function Calling V2 Parsed (RU)
This dataset is a fork of an original mizinovmv/glaive-function-calling-v2-ru.
Structure
train (102860 rows)
messages
functions
test (9681 rows)
messages
functions
Messages field contains JSON string with list of messages. Each message has role (one of: system, user, assistant, function_call, function_response) and content (message string) fields.
Functions field contains JSON string with list of functions for current chat… See the full description on the dataset page: https://huggingface.co/datasets/korotkov/glaive-function-calling-v2-ru-parsed.quantum-computing
Neura Parse — Quantum Computing
A multi-format quantum computing dataset spanning theory and hardware — from qubits, gates, and algorithms to QPUs, error correction, quantum software (Qiskit/Cirq/PennyLane), and quantum machine learning. Records come as instruction/response pairs, open and multiple-choice Q&A, runnable code tasks, encyclopedic concepts, and pretraining-style text, so the dataset supports SFT, evaluation, and continued pretraining under one schema.
Part of… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-computing.github-jupyter-parsed
GitHub Jupyter Dataset
Dataset Description
This is a parsed and preprocessed version of GitHub-Jupyter Dataset, a dataset extracted from Jupyter Notebooks on BigQuery. We only keep markdown and python cells and convert the markdown to text. Some heuristics are also applied to filter notebooks with little data and very long or very short cells.
Licenses
Each example has the license of its associated repository. There are in total 15 licenses:
[
'mit'… See the full description on the dataset page: https://huggingface.co/datasets/codeparrot/github-jupyter-parsed.fault-tolerant-quantum-computing
Neura Parse — Fault-Tolerant Quantum Computing: QEC Codes, Decoders, Magic States & Resource Estimation
A deep, Stim-informed vertical on fault tolerance — QEC code families, decoders, fault-tolerant gate constructions, and the full physical-to-logical resource-estimation pipeline. Expands the general dataset's handful of error-correction topics into research-grade coverage including the 2024-2026 milestones: surface-code below threshold, qLDPC/bivariate-bicycle memories… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/fault-tolerant-quantum-computing.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.quantum-information-and-complexity-theory
Neura Parse — Quantum Information & Complexity Theory: Channels, Entropies, Classes & the Structure of Advantage
A proof-based theoretical-foundations vertical uniting quantum information theory (channels, entropies, entanglement measures, distinguishability, capacities, Shannon theory) with quantum complexity theory and the structure of quantum advantage (classes, Hamiltonian complexity, sampling-based advantage and its verification, pseudorandomness, dequantization).… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-information-and-complexity-theory.glaive-function-calling-v2-parsed
Glaive Function Calling V2 Parsed
This dataset is a fork of an original - glaiveai/glaive-function-calling-v2.
Structure
train (100199 rows)
messages
functions
test (12553 rows)
messages
functions
Messages field contains JSON string with list of messages. Each message has role (one of: system, user, assistant, function_call, function_response) and content (message string) fields.
Functions field contains JSON string with list of functions for current chat (their… See the full description on the dataset page: https://huggingface.co/datasets/korotkov/glaive-function-calling-v2-parsed.agenda-parser-tool-traces
Agenda Parser — tool-calling reasoning traces
ReAct tool-calling traces for the Agenda Parser
agents: each row is one agent step — a {system, user, assistant} chat example
where the assistant emits a single JSON action {"thought", "tool", "args"}.
Two agents are covered (tagged by meta.domain):
agenda — the uploaded-packet research agent, over real public-meeting agenda
packets (tools: list/read items, semantic + exact search, summarize, report).
Each agenda row's meta.unit_id… See the full description on the dataset page: https://huggingface.co/datasets/rdubwiley/agenda-parser-tool-traces.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.omnimcp_healthtech_hl7_parser_teaser
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📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_healthtech_hl7_parser_teaser.quantum-error-mitigation-and-benchmarking
Neura Parse — Quantum Error Mitigation, Characterization & Benchmarking
A pre-fault-tolerance, code-backed vertical on getting trustworthy answers from noisy hardware and rigorously measuring device quality: error-mitigation techniques, characterization/tomography protocols, and benchmarking suites. Runnable Mitiq, pyGSTi, and Qiskit Experiments pipelines with honest sampling-overhead and bias/variance accounting — the practitioner and research toolkit the general dataset… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-error-mitigation-and-benchmarking.quantum-networking-and-distributed
Neura Parse — Quantum Networking, Repeaters & Distributed Quantum Computing
A systems-frontier vertical on connecting quantum devices: entanglement distribution and distillation, quantum repeaters, quantum-internet protocol stacks, quantum memories/transduction, and modular/distributed quantum computing (nonlocal gates, circuit knitting across nodes, blind/verifiable delegated computation). Covers protocol and simulation methods used with tools such as NetSquid and SeQUeNCe… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-networking-and-distributed.quantum-compilation-and-programming
Neura Parse — Quantum Compilation & Programming
A code-heavy vertical on the quantum software/compilation stack: turning abstract quantum circuits and unitaries into device-executable programs. Covers unitary decomposition and circuit synthesis (Euler/ZYZ, KAK/Cartan, Solovay-Kitaev, Ross-Selinger gridsynth, numerical synthesis with BQSKit), gate-set/basis transpilation to native gate sets, qubit layout/mapping and routing under connectivity constraints (SABRE, VF2, SWAP… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-compilation-and-programming.ai-for-quantum
Neura Parse — AI for Quantum: ML & LLMs for Decoding, Control, Characterization & Software
The reverse quantum-AI direction — classical machine learning, RL, and LLMs/agents applied to make quantum computers work. Covers neural/transformer QEC decoders (AlphaQubit-style), RL/ML pulse and calibration control, neural-network quantum states, ML tomography and Hamiltonian/noise learning, learned circuit optimization, and LLM/agentic quantum software engineering (code generation… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/ai-for-quantum.dolphin-r1-korean-deepseek-parsed
[PARSED] dolphin R1 korean deepseek (toolcalls)
The data in this dataset is a subset of the original exp-models/dolphin-r1-korean-deepseek-toolcalls*Dropped row 1273 due to surrogates error.
Subset name
multi-turn
parallel
multiple definition
Last turn type
number of dataset
dolphin-r1-korean-deepseek
no
yes
yes
tool_calls
1757
dolphin-r1-korean-deepseek-non-reasoning
no
yes
yes
tool_calls
1757
This dataset is a re-parsed version of… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/dolphin-r1-korean-deepseek-parsed.quantum-optimization
Neura Parse — Quantum Optimization, Annealing & Finance: QAOA, Adiabatic Methods & the Advantage Question
A research-plus-practitioner vertical on quantum approaches to combinatorial and continuous optimization and their most-piloted enterprise use cases. Covers QAOA theory and variants, adiabatic/annealing methods and D-Wave, QUBO/Ising encodings, amplitude-estimation Monte Carlo for finance, and the rigorous question of whether and where quantum beats classical (including… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-optimization.advanced-quantum-algorithms
Neura Parse — Advanced Quantum Algorithms: Derivations, QSVT/Block-Encoding & Hamiltonian Simulation
A derivation- and resource-analyzed algorithms vertical spanning the canonical fault-tolerant canon (with full proofs, complexity, and worked traces) and the modern QSVT/block-encoding toolkit through Hamiltonian simulation, amplitude estimation, and quantum linear systems. Turns the general dataset's one-topic-per-algorithm summaries into line-by-line derivations, lower… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/advanced-quantum-algorithms.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.job-educational-parser-dataset-08-0-0805
Job Educational Parser Dataset
招聘领域的岗位与学历要求数据集。
输入：岗位描述 -> 输出：学历要求
Splits
train: 19w_0701.csv (约 19 万条)
test: 2w_0716.csv (约 2 万条)
validation: 4w_0708.csv (约 4 万条)
每条数据至少包含字段：
user: 职位描述
assistant: 要求的学历（如 "博士、硕士、本科"）,遵循从高到低
由 @wangzihaogithub 创建。
quantum-hardware-device-physics
Neura Parse — Quantum Hardware Device Physics: Qubit Design, Coherence, Control & Scaling
A physics- and engineering-deep vertical on how qubits are built, controlled, and scaled across superconducting, trapped-ion, neutral-atom, and spin modalities (plus emerging erasure/biased-noise qubits). Device-physics derivations, coherence-limit analyses, control-stack engineering, and 2025-2026 scaling/interconnect work, with QuTiP/scqubits simulation context — expanding the general… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-hardware-device-physics.quantum-sensing-and-metrology
Neura Parse — Quantum Sensing & Metrology: Fisher Information, the Heisenberg Limit & Entanglement-Enhanced Sensors
A physics- and estimation-theory-deep vertical on the second quantum revolution's sensing pillar: how quantum Fisher information and the Cramer-Rao bound set ultimate precision, how entanglement and squeezing push sensors from the standard quantum limit toward the Heisenberg limit, and how these ideas are realized in optical atomic clocks, NV-center… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-sensing-and-metrology.sql-parsed