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

Thermostat是一个大型的模型解释数据集和分析工具，拥有超过200k种来自不同NLP任务的杰出state-of-the-art模型决策解释，该数据集的编译时间超过了1年，但现在可以使社区节省计算时间，降低了冗余计算以及增加可比性和可复制性。

Thermostat：大规模 NLP 模型解释和分析工具集合