Upd 4

docs/source/benchmarks/tabular.rst

@@ -2,7 +2,7 @@ Tabular data
 ------------
 
 Here are overall classification problem results across state-of-the-art AutoML frameworks
-using `paper <https://github.com/openml/automlbenchmark>`__ test suite:
+using `AutoMlBenchmark <https://github.com/openml/automlbenchmark>`__ test suite:
 
 .. raw:: html
    :file: amlb_res.html
@@ -11,11 +11,11 @@ using `paper <https://github.com/openml/automlbenchmark>`__ test suite:
 The results are obtained using sever based on Xeon Cascadelake (2900MHz)
 with 12 cores and 24GB memory for experiments with the local infrastructure. 1h8c configuration was used for AMLB.
 
-Despite the obtained metrics being a bit different from AMLB's paper `<https://arxiv.org/abs/2207.12560>`__
+Despite the obtained metrics being a bit different from AMLB's `paper <https://arxiv.org/abs/2207.12560>`__
 the results confirm that FEDOT is competitive with SOTA solutions.
 
 The statistical analysis was conducted using the Friedman t-test.
 The results of experiments and analysis confirm that FEDOT results are statistically indistinguishable
-from SOTA competitors H2O, AutoGluon and LAMA (see below)
+from SOTA competitors H2O, AutoGluon and LAMA (see below).
 
 .. image:: img_benchmarks/stats.png