openml
Datasets
All datasets matching “openml”OpenMLE-Tasks
OpenMLE Tasks
📄 Paper
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🌐 Project
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💻 Code
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🤗 Models
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📚 SFT Traces
OpenMLE Tasks provides machine-learning Task environments. The public SFT trajectories are released separately in OpenMLE-SFT-Traces. These resources accompany the paper Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering and the OpenRSI code release.
Release form
What is included… See the full description on the dataset page: https://huggingface.co/datasets/FrontisAI/OpenMLE-Tasks.CoopHumanEvalOpenMLE-SFT-Traces
OpenMLE-SFT-Traces
📄 Paper
•
🌐 Project
•
💻 Code
•
🤗 Models
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🧩 Tasks
OpenMLE-SFT-Traces contains the supervised fine-tuning trajectories used in the
OpenMLE project. It accompanies the paper Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering and the
OpenRSI code release.
Dataset summary
Item
Value
Trajectories
26,259
Task… See the full description on the dataset page: https://huggingface.co/datasets/FrontisAI/OpenMLE-SFT-Traces.BFCL-V4-Parallel-Native
BFCL V4 Parallel Native
Native BFCL v4 single-turn parallel function-calling rows for decentralized multi-agent collaboration.
Source data comes from the official Berkeley Function Calling Leaderboard v4 data and possible-answer files.
Fields
id
official_category
task_type
user_prompt
function
ground_truth
Categories
live_parallel
live_parallel_multiple
parallel
parallel_multiple
Counts
train: 352 rows
eval: 88 rows
total: 440… See the full description on the dataset page: https://huggingface.co/datasets/OpenMLRL/BFCL-V4-Parallel-Native.BFCL-V4-Parallel-Multi-Turn
BFCL V4 Parallel Multi-Turn
Flattened current-turn rows from BFCL v4 multi-turn trajectories for decentralized multi-agent function-calling experiments.
Source data comes from the official Berkeley Function Calling Leaderboard v4 data and possible-answer files.
Fields
id
official_category
task_type
user_prompt
function
ground_truth
turn_index
Categories
multi_turn_base_step
multi_turn_long_context_step
multi_turn_miss_func_step… See the full description on the dataset page: https://huggingface.co/datasets/OpenMLRL/BFCL-V4-Parallel-Multi-Turn.OpenMLPerf
Preparing OpenMLPerf dataset
To process the semi-raw MLPerf data into the OpenMLPerf dataset, run the following command:
# Untar raw files
bzip2 -d semi-raw-mlperf-data.tar.bz2
tar xvf semi-raw-mlperf-data.tar
# Create a virtual environment
python -m venv .venv
# Activate the virtual environment
source .venv/bin/activate
# Install the required packages
pip install -r requirements.txt
# Run the processing script
python process.py
The processed dataset will be saved both as… See the full description on the dataset page: https://huggingface.co/datasets/ctuning/OpenMLPerf.