This project is part of the final year NLP course at ENSAE. We have written a paper on this subject and here is the abstract.

Deep learning models allow today to train large datasets in order to have very good performance in all problems that call for Natural Language Processing (NLP). Nevertheless, these models are very vulnerable to the change in distribution between the training dataset and the application data. This is why it has become essential to develop methods to detect data that are not of the same distribution. In this paper, we give a quick background of the out-of-distribution problem. Then, we present different detectors, starting with their mathematical basis, and we change the DistilBERT model in order to have different aggregations. Finally, we propose to apply the detectors to several datasets and aggregation in order to compare them.

To create the environment to work on, on a terminal, use:

$ conda env create -f environment.yml

or to install packages, use:

$ pip install -r requirements.txt

You can create a folder on you drive to keep intermediate data to easily use it.

The models_scripts folder contains the modification files of the transformers package to access the outputs of the intermediate layers of the DistilBERT model, in order to use the created classes, you will have to use them to replace the initial corresponding files of your transformers package.

You can find several examples of use in the Notebook folder. For instance for IMDB as in-distribution, you will have to run the get_data_train_model_imdb.ipynbto download the data and trained the model. And then, you can use the get_metric_ood_imdb.ipynbnotebook to compute the OOD detections.