Recent advances in NLP have been sustained by the availability of large amounts of data and standardized benchmarks, which are not available for many languages. As a small step towards addressing this we propose LiRo, a platform for benchmarking models on the Romanian language on nine standard tasks: text classification, named entity recognition, machine translation, sentiment analysis, POS tagging, dependency parsing, language modelling, question-answering, and semantic textual similarity. We also include a less standard task of embedding debiasing, to address the growing concerns related to gender bias in language models. The platform exposes per-task leaderboards populated with baseline results for each task. In addition, we create three new datasets: one from Romanian Wikipedia and two by translating the Semantic Textual Similarity (STS) benchmark and the Cross-lingual Question Answering Dataset (XQuAD) into Romanian. We believe LiRo will not only add to the growing body of benchmarks covering various languages, but can also enable multi-lingual research by augmenting parallel corpora, and hence is of interest for the wider NLP community. LiRo is available at https://lirobenchmark.github.io/.