  Although representational retrieval models based on Transformers have been
able to make major advances in the past few years, and despite the widely
accepted conventions and best-practices for testing such models, a
$\textit{standardized}$ evaluation framework for testing them has not been
developed. In this work, we formalize the best practices and conventions
followed by researchers in the literature, paving the path for more
standardized evaluations - and therefore more fair comparisons between the
models. Our framework (1) embeds the documents and queries; (2) for each
query-document pair, computes the relevance score based on the dot product of
the document and query embedding; (3) uses the $\texttt{dev}$ set of the
MSMARCO dataset to evaluate the models; (4) uses the $\texttt{trec_eval}$
script to calculate MRR@100, which is the primary metric used to evaluate the
models. Most importantly, we showcase the use of this framework by
experimenting on some of the most well-known dense retrieval models.
