Added test data results to README
@@ -10,19 +10,25 @@ is also supported in different ways, e.g. grid search, pseudo-random search
or Bayesian optimization. These are some of the state-of-the-art results
obtained with libKGE (mean reciprocal rank):
-<center>
-
-| | FB15K-237 | WNRR |
-|----------|-----------|------|
-| [RESCAL](http://www.icml-2011.org/papers/438_icmlpaper.pdf) | 0.36 | 0.46 |
-| [TransE](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data) | 0.31 | 0.42 |
-| [DistMult](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ICLR2015_updated.pdf) | 0.35 | 0.45 |
-| [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf) | 0.35 | 0.47 |
-| [ConvE](https://arxiv.org/abs/1707.01476) | 0.34 | 0.44 |
-
-</center>
-
-
+## FB15K-237
+
+| | MRR | Hits@10 |
+|----------|-----------|---------|
+| [RESCAL](http://www.icml-2011.org/papers/438_icmlpaper.pdf) | 0.356 | 0.542 |
+| [TransE](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data) | 0.310 | 0.493 |
+| [DistMult](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ICLR2015_updated.pdf) | 0.344 | 0.531 |
+| [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf) | 0.348 | 0.536 |
+| [ConvE](https://arxiv.org/abs/1707.01476) | 0.338 | 0.520 |
+
+## WNRR
+
+| | MRR | Hits@10 |
+|----------|-----------|---------|
+| [RESCAL](http://www.icml-2011.org/papers/438_icmlpaper.pdf) | 0.467 | 0.517 |
+| [TransE](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data) | 0.228 | 0.519 |
+| [DistMult](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ICLR2015_updated.pdf) | 0.454 | 0.535 |
+| [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf) | 0.479 | 0.552 |
+| [ConvE](https://arxiv.org/abs/1707.01476) | 0.442 | 0.505 |
# Quick start
