| # RoBERTa Explainability | |
| In this demo, we use the RoBERTa language model (optimized for masked language modelling and finetuned for sentiment analysis). | |
| The model predicts for a given sentences whether it expresses a positive, negative or neutral sentiment. | |
| But how does it arrive at its classification? This is, surprisingly perhaps, very difficult to determine. | |
| A range of so-called "attribution methods" have been developed that attempt to determine the importance of the words in the input for the final prediction; | |
| they provide a very limited form of "explanation" -- and often disagree -- but sometimes provide good initial hypotheses nevertheless that can be further explored with other methods. | |
| Abnar & Zuidema (2020) proposed a method for Transformers called "Attention Rollout", which was further refined by Chefer et al. (2021) into Gradient-weighted Rollout. | |
| Here we compare it to another popular method called Integrated Gradient. | |
| * Gradient-weighted attention rollout, as defined by [Hila Chefer](https://github.com/hila-chefer) | |
| [(Transformer-MM_explainability)](https://github.com/hila-chefer/Transformer-MM-Explainability/), with rollout recursion upto selected layer | |
| * Layer IG, as implemented in [Captum](https://captum.ai/)(LayerIntegratedGradients), based on gradient w.r.t. selected layer. | |