Inseq: An Interpretability Toolkit for Sequence Generation Models
Inseq is a Pytorch-based hackable toolkit to democratize the study of interpretability for sequence generation models. Inseq supports a wide set of models from the 🤗 Transformers library and an ever-growing set of feature attribution methods, leveraging in part the widely-used Captum library. For a quick introduction to common use cases, see the Getting started with Inseq page.
Using Inseq, feature attribution maps that can be saved, reloaded, aggregated and visualized either as HTMLs (with Jupyter notebook support) or directly in the console using rich. Besides simple attribution, Inseq also supports features like step score extraction, attribution aggregation and attributed functions customization for more advanced use cases.
- PECoRe: Plausibility Evaluation of Context Usage in Language Models
- IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation
- Characterizing Linguistic Complexity in Humans and Language Models
- Contrastive Language-Image Pre-training for the Italian Language
- Contrastive Image-Text Pretraining for Italian