Previous literature inform us that corpus size and posterior quality do not follow linear correlation for some learning tasks (e.g. semantic measures) <cite>[5]</cite> . In our semantic tests, on average 85% of the quality can be obtained by training on a random ∼ 4% subset of the original corpus (e.g. as in Fig. 1 , 5 random million lines yield 64.14% instead of 75.14%). Our claims are that i) such evaluation posteriors are Normally distributed (Tab. I), and that ii) the variance is inversely proportional to the subset size (Tab. II).