datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
glaive_function_calling_v1_standardizedarxiv_deep_learning_python_research_code_functions_summaries
Dataset Card for "AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code_functions_summaries"
Dataset Description
https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code_functions_summaries
Dataset Summary
AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code_functions_summaries contains summaries for every python function and class extracted from source code files referenced in ArXiv papers. The… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code_functions_summaries.functional-cds
marin-dna/functional-cds
Human-anchored 255 bp vertebrate sequences from the Zoonomia 447-mammal Cactus alignment and official version-matched UCSC hg38-to-target liftOver chains.
This draft covers the cds region cohort with all species scope and preserves source FASTA/2bit letter case.
Non-human rows project only the central human nucleotide and extract the 255 bp target window centered on its unique mapped locus.
For the 28 non-mammalian targets, the stable ucsc_multiz100way… See the full description on the dataset page: https://huggingface.co/datasets/marin-dna/functional-cds.functional-enhancer
marin-dna/functional-enhancer
Human-anchored 255 bp vertebrate sequences from the Zoonomia 447-mammal Cactus alignment and official version-matched UCSC hg38-to-target liftOver chains.
This draft covers the enhancer region cohort with all species scope and preserves source FASTA/2bit letter case.
Non-human rows project only the central human nucleotide and extract the 255 bp target window centered on its unique mapped locus.
For the 28 non-mammalian targets, the stable… See the full description on the dataset page: https://huggingface.co/datasets/marin-dna/functional-enhancer.python-audio-copilot-training-using-function-knowledge-graphs
Python Copilot Audio Training using Global Functions with Knowledge Graphs
This dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details
Each global function has a question and answer mp3 where one voice reads the question and another voice reads the answer. Both mp3s are stored in the parquet dbytes column and the associated source code file_path… See the full description on the dataset page: https://huggingface.co/datasets/matlok/python-audio-copilot-training-using-function-knowledge-graphs.python-image-copilot-training-using-function-knowledge-graphs
Python Copilot Image Training using Function Knowledge Graphs
This dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details
Each row contains a png file in the dbytes column.
Rows: 134357
Size: 130.5 GB
Data type: png
Format: Knowledge graph using NetworkX with alpaca text box
Schema
The png is in the dbytes column:
{
"dbytes": "binary"… See the full description on the dataset page: https://huggingface.co/datasets/matlok/python-image-copilot-training-using-function-knowledge-graphs.functional-utr3
marin-dna/functional-utr3
Human-anchored 255 bp vertebrate sequences from the Zoonomia 447-mammal Cactus alignment and official version-matched UCSC hg38-to-target liftOver chains.
This draft covers the utr3 region cohort with all species scope and preserves source FASTA/2bit letter case.
Non-human rows project only the central human nucleotide and extract the 255 bp target window centered on its unique mapped locus.
For the 28 non-mammalian targets, the stable ucsc_multiz100way… See the full description on the dataset page: https://huggingface.co/datasets/marin-dna/functional-utr3.functional-ncrna
marin-dna/functional-ncrna
Human-anchored 255 bp vertebrate sequences from the Zoonomia 447-mammal Cactus alignment and official version-matched UCSC hg38-to-target liftOver chains.
This draft covers the ncrna region cohort with all species scope and preserves source FASTA/2bit letter case.
Non-human rows project only the central human nucleotide and extract the 255 bp target window centered on its unique mapped locus.
For the 28 non-mammalian targets, the stable… See the full description on the dataset page: https://huggingface.co/datasets/marin-dna/functional-ncrna.functional-tss
marin-dna/functional-tss
Human-anchored 255 bp vertebrate sequences from the Zoonomia 447-mammal Cactus alignment and official version-matched UCSC hg38-to-target liftOver chains.
This draft covers the tss_region region cohort with all species scope and preserves source FASTA/2bit letter case.
Non-human rows project only the central human nucleotide and extract the 255 bp target window centered on its unique mapped locus.
For the 28 non-mammalian targets, the stable… See the full description on the dataset page: https://huggingface.co/datasets/marin-dna/functional-tss.deprecated-github-code-haskell-function
Dataset Card for "github-code-haskell-function"
Rows: 3.26M
Download Size: 1.17GB
This dataset is extracted from github-code-haskell-file.
Each row has 3 flavors of the same function:
uncommented_code: Includes the function and its closest signature.
function_only_code: Includes the function only.
full_code: Includes the function and its closest signature and comment.
The heuristic for finding the closest signature and comment follows: If the immediate previous neighbor of the… See the full description on the dataset page: https://huggingface.co/datasets/blastwind/deprecated-github-code-haskell-function.python_functions_filtered
Dataset Card for "python_functions_filtered"
Python functions extracted from starcoder base. Only functions with minimal external dependencies were chosen. They were filtered manually, and also based on learning value and quality.
pulmonary-disease-airway-lung-function-dataset
Acoustic Waveform Airway and Respiratory Examination (AWARE/PTEase) Dataset
Guidelines
AWARE/PTEase is a smartphone-based sensing system that examines human airway's internal physiological conditions, developed by the Intelligent Systems Laboratory at University of Pittsburgh. AWARE/PTEase probes the airway with acoustic pulses through mouth, and collect the airway's reflections for analysis. Please refer to our paper and github repo for more details.
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/ericyxy98/pulmonary-disease-airway-lung-function-dataset.prosite_functional_motif_scaffolding_benchmark
PROSITE-derived Functional Motif Benchmark
This archive contains an anonymized dataset artifact for a systematically derived benchmark of structurally conserved functional motif-scaffolding cases from PROSITE-linked experimental protein structures.
The benchmark is intended for static motif-scaffolding evaluation with standard MotifBench-style pipelines. Cases are derived from PROSITE motif-pattern entries, mapped to experimentally resolved PDB structures, filtered for recurrent… See the full description on the dataset page: https://huggingface.co/datasets/anonymous-motif-scaffolding/prosite_functional_motif_scaffolding_benchmark.crypto-agent-safe-function-calling
CrAI-SafeFuncCall Dataset
📄 Paper: Real AI Agents with Fake Memories: Fatal Context Manipulation
Attacks on Web3 Agents
🤗 Dataset: CrAI-SafeFuncCall
📊 Benchmark: CrAI-Bench
Overview
The CrAI-SafeFuncCall dataset is designed to enhance the security of AI agents when performing function calls in the high-stakes domain of cryptocurrency and financial applications. It focuses on the critical challenge of detecting and mitigating memory injection attacks. Derived from the… See the full description on the dataset page: https://huggingface.co/datasets/SentientAGI/crypto-agent-safe-function-calling.Function_calling_SFTfunctional-multiclass-gamba
GAMBA Functional Region Multiclass
This representation benchmark asks whether frozen sequence embeddings
separate genomic functional categories. Each row is one annotated region;
label == category.
Loading
from datasets import load_dataset
full_bidi = load_dataset(
"Taykhoom/functional-multiclass-gamba",
"full-bidi",
split="all",
)
paper_test = full_bidi.filter(
lambda row: row["split"] == "test"
and row["category"] != "noncoding_regions"
)… See the full description on the dataset page: https://huggingface.co/datasets/Taykhoom/functional-multiclass-gamba.functional-random-gamba
GAMBA Functional Regions: Feature vs Category-Matched Random
This paired binary representation benchmark asks whether a model can
distinguish an annotated functional region from a chromosome- and
length-matched random control.
For this dataset, a random control avoids retained anchors from the same
functional category. It may overlap annotations from other categories.
Use the annotation-free random dataset if controls must avoid every retained
annotation category.
Each… See the full description on the dataset page: https://huggingface.co/datasets/Taykhoom/functional-random-gamba.functional-upstream-gamba
GAMBA Functional Regions: Feature vs Upstream
This paired binary representation benchmark asks whether a model can
distinguish an annotated functional region from a strand-aware, equal-length
control located 2 kb upstream.
Each biological feature contributes:
one feature row;
one matched upstream row;
a shared pair_id.
Loading
from datasets import load_dataset
bidi = load_dataset(
"Taykhoom/functional-upstream-gamba",
"bidi",
split="all",
)… See the full description on the dataset page: https://huggingface.co/datasets/Taykhoom/functional-upstream-gamba.gpt-4o-function-calling-traces
Dataset Card for Dataset Name
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]… See the full description on the dataset page: https://huggingface.co/datasets/cfahlgren1/gpt-4o-function-calling-traces.vulnerable-functions-and-commits_cvefixes-2022
vulnerable-functions-and-commits_cvefixes-2022
Contains vulnerable functions and commits from the CVEFixes SQLite database.
crypto-agent-safe-function-calling
CrAI-SafeFuncCall Dataset
📄 Paper: Real AI Agents with Fake Memories: Fatal Context Manipulation
Attacks on Web3 Agents
🤗 Dataset: CrAI-SafeFuncCall
📊 Benchmark: CrAI-Bench
Overview
The CrAI-SafeFuncCall dataset is designed to enhance the security of AI agents when performing function calls in the high-stakes domain of cryptocurrency and financial applications. It focuses on the critical challenge of detecting and mitigating memory injection attacks. Derived from the… See the full description on the dataset page: https://huggingface.co/datasets/peiyao-sentient/crypto-agent-safe-function-calling.koch_test_stop_functionThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.0",
"robot_type": "koch",
"total_episodes": 5,
"total_frames": 1410,
"total_tasks": 1,
"total_videos": 10,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:5"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/RobotisSW/koch_test_stop_function.restructured-glaive-function-calling-v2
Glaive Function Calling V2 (Structured)
This dataset is a cleaned and structured version of the originalGlaive Function Calling V2.
The goal of this dataset is to make the conversations easier to use for training tool-calling / function-calling language models, such as:
Llama
Qwen
Mistral
DeepSeek
other OpenAI-compatible tool calling models
The original dataset stores conversations as raw text.This version converts them into a structured message format suitable for modern LLM… See the full description on the dataset page: https://huggingface.co/datasets/muhammadravi251001/restructured-glaive-function-calling-v2.Nemotron-RL-Agentic-Function-Calling-Pivot-v1-prompt-only
Nemotron-RL-Agentic-Function-Calling-Pivot-v1-prompt-only
Prompt-only extraction from nvidia/Nemotron-RL-Agentic-Function-Calling-Pivot-v1.
Files:
prompts.csv: one prompt extraction record per source row. Records include
prompt, separated system_prompt, and structured tools when the source row
defines available tools. Nested values are JSON-encoded inside CSV cells.
summary.md: source row counts, extracted row counts, count deltas, and failed prompt counts.… See the full description on the dataset page: https://huggingface.co/datasets/jamesdborin/Nemotron-RL-Agentic-Function-Calling-Pivot-v1-prompt-only.multi_class_solidity_function_vulnerabilty
Dataset Card for "multi_class_solidity_function_vulnerabilty"
More Information needed
africa-egypt-capmas-state-final-account-functional-classification-f2f66f33
State Final Account (Functional Classification) | Africa (CAPMAS Egypt Open Data)
1,410 rows - 1 Africa country/area - 2011-2023 - 14 indicators - Engineered by Electric Sheep Africa
TL;DR
This dataset contains 1,410 rows from CAPMAS Egypt Open Data, covering State Final Account (Functional Classification). It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.
What… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-egypt-capmas-state-final-account-functional-classification-f2f66f33.glaive-function-calling-v2-ko-refined
glaive-function-calling-v2-ko-refined
Created by: Seungwoo Ryu
Overview
This dataset is an enhanced version of the heegyu/glaive-function-calling-v2-ko dataset, specifically optimized for Qwen models and addressing JSON parsing issues in the original dataset. The dataset contains Korean conversations with function calling annotations, standardized for easier use in tool-augmented language models, with particular attention to Qwen's function calling implementation.… See the full description on the dataset page: https://huggingface.co/datasets/tryumanshow/glaive-function-calling-v2-ko-refined.details_uukuguy__speechless-zephyr-code-functionary-7b
Dataset Card for Evaluation run of uukuguy/speechless-zephyr-code-functionary-7b
Dataset automatically created during the evaluation run of model uukuguy/speechless-zephyr-code-functionary-7b.
The dataset is composed of 136 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always… See the full description on the dataset page: https://huggingface.co/datasets/OALL/details_uukuguy__speechless-zephyr-code-functionary-7b.test_feedback_function_Jan14_temae_demoThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "so101_follower",
"total_episodes": 6,
"total_frames": 2023,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 30,
"splits": {
"train": "0:6"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/shuhei25/test_feedback_function_Jan14_temae_demo.audio-function-calling
Audio Function Calling Dataset
A synthetic multi-turn conversation dataset for audio-based tool/function calling.
Each sample contains a system prompt with tool definitions, alternating user and assistant turns,
where user turns are designed for audio (natural spoken language with filler words) and assistant
turns may include tool calls.
Note: This is the first batch (137 audio samples, 153 text-only samples). We are actively improving the generation pipeline and will be adding… See the full description on the dataset page: https://huggingface.co/datasets/mlech26l/audio-function-calling.