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In the language domain, as in other domains, neural explainability takes anever more important role, with feature attribution methods on the forefront.Many such methods require considerable computational resources and expertknowledge about implementation details and parameter choices. To facilitateresearch, we present Thermostat which consists of a large collection of modelexplanations and accompanying analysis tools. Thermostat allows easy access toover 200k explanations for the decisions of prominent state-of-the-art modelsspanning across different NLP tasks, generated with multiple explainers. Thedataset took over 10k GPU hours (> one year) to compile; compute time that thecommunity now saves. The accompanying software tools allow to analyseexplanations instance-wise but also accumulatively on corpus level. Users caninvestigate and compare models, datasets and explainers without the need toorchestrate implementation details. Thermostat is fully open source,democratizes explainability research in the language domain, circumventsredundant computations and increases comparability and replicability.

Thermostat: A Large Collection of NLP Model Explanations and Analysis Tools