Within the NLP field for training word embeddings, an arbitrary dimension is often chosen. For the word embeddings, setting a higher number of dimensions usually results in higher-quality embeddings up until a saturation point . We have observed this from our property prediction tasks. The performance tends to improve with dimension size because a higher number of dimensions can capture more complex relationships between the co-occurring pairs. However, the effectiveness of increasing dimensions is ultimately constrained by the available data size, which limits the ability to learn meaningful patterns.