Pre-trained word embeddings (for example, BERT-like) have been successfully used in a variety of downstream tasks. However, do all embeddings, obtained from the models of the same architecture, encode information in the same way? Does the size of the model correlate to the quality of the information encoding? In this paper, we will attempt to dissect the dimensions of several BERT-like models that were trained on the French language to find where grammatical information (gender, plurality, part of speech) and semantic features might be encoded. In addition to this, we propose a framework for comparing the quality of encoding in different models.