Frieda Born
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first commit of ressources for levels data repository
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| README.md | 1 mēnesi atpakaļ | |
| processed_dataset.pkl | 1 mēnesi atpakaļ | |
| pruned_dataset.pkl | 1 mēnesi atpakaļ | |
| pruned_processed_dataset.pkl | 1 mēnesi atpakaļ | |
README.md
The processedLevels dataset
These files contain processed_data from the Levels dataset.
Overview
We have performed a couple of processing steps (e.g., removing duplicates, etc.) to give you the option to easily work with the data without having to process the sourcedata first. This resulted in 2 processed versions of the dataset:
pruned_processed_dataset.pkl: This file is processed and pruned, which means it only includes the experiment trials per participant and we already pruned to remove triplet responses of triplets that either have < 4 or > 5 responses.processed_dataset.pkl: This dataset is processed (but not pruned). It includes only the necessary variables for analysis, but you would still need to filter for the experiment trials (see codebook) and please note, that here also duplicate triplet information is still included.
Loading data
If you use python, you can consider loading the data like this:
with open("../your path to data /pruned_processed_dataset.pkl", "rb") as f:
pruned_processed_dataset = pickle.load(f)
| datacite.yml | |
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| Title | The Levels Dataset |
| Authors |
Muttenthaler,Lukas;Google DeepMind, Machine Learning Group, Technische Universität Berlin, BIFOLD, Berlin Institute for the Foundations of Learning and Data, Berlin, Germany;ORCID:0000-0002-0804-4687
Greff,Klaus;Google DeepMind;ORCID:0000-0001-6982-0937 Born,Frieda;Technische Universität Berlin, BIFOLD, Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Adaptive Memory and Decision Making (AMD), Max Planck Institute for Human Development,Berlin, Germany;ORCID:0009-0002-1214-4864 Spitzer,Bernhard;Adaptive Memory and Decision Making (AMD), Max Planck Institute for Human Development, Berlin, Germany;ORCID:0000-0001-9752-932X Kornblith,Simon;Anthropic;ORCID:0000-0002-9088-2443 Mozer,Michael C.;Google DeepMind;ORCID:0000-0002-9654-0575 Müller,Klaus-Robert;Google DeepMind, Machine Learning Group, Technische Universität Berlin, BIFOLD, Berlin Institute for the Foundations of Learning and Data, Berlin, Germany,Department of Artificial Intelligence, Korea University, Seoul, Max Planck Institute for Informatics, Saarbrücken, Germany;ORCID:0000-0002-3861-7685 Unterthiner,Thomas;Google DeepMind;ORCID:0000-0001-5361-3087 Lampinen,Andrew K.;Google DeepMind;ORCID:0000-0002-6988-8437 |
| Description | To validate that AligNet can indeed help to increase the alignment between models and humans, we used crowd-sourcing to collect a novel evaluation dataset of human semantic judgments across multiple levels of abstraction that we call Levels. |
| License | Open Data Commons Public Domain Dedication and License (PDDL) v1.0 (https://opendatacommons.org/licenses/pddl/1-0/) |
| References |
Muttenthaler, L., Greff, K., Born, F., Spitzer, B., Kornblith, S., Mozer, M.C., Müller, K.R., Unterthiner, T., Lampinen, A.K. : Aligning Machine and Human Visual Representations across Abstraction Levels [doi:10.48550/arXiv.2409.06509] (IsSupplementTo)
Born Frieda. (2024). Levels Collection Experiment Code (v1.0.0) [doi:10.5281/zenodo.13749102] (IsReferencedBy) |
| Funding | |
| Keywords |
AI alignment
human cognition representation learning computer vision |
| Resource Type |
Dataset |