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Materials for the Lab "Explaining Neural Language Models from Internal Representations to Model Predictions" at AILC LCL 2023 🔍

A domain-informed probe visualiser trained on wav2vec 2.0 representations.

Counting dataset for Vision & Language models. Introduced in the paper "Seeing Past Words: Testing the Cross-Modal Capabilities of Pretrained V&L Models". https://arxiv.org/abs/2012.12352

Materials for the MSc Thesis "Interpreting Neural Language Models for Linguistic Complexity Assessment" and related works.

Probing Bert for part of speech (POS) and named entity recognition (NER) to alleviate the opacity of learned representations.

Code for "The Locality and Symmetry of Positional Encodings" EMNLP Findings

Natural language inference task on SNLI and carried out POS tag probing

Understanding Morphosyntactic Representations in Pretrained Language Models.