diff --git a/examples/pipeline/wikidata_entity_linking.py b/examples/pipeline/wikidata_entity_linking.py
index b57d9f541..17c2976dd 100644
--- a/examples/pipeline/wikidata_entity_linking.py
+++ b/examples/pipeline/wikidata_entity_linking.py
@@ -154,8 +154,8 @@ def run_pipeline():
             optimizer.L2 = L2
 
         # define the size (nr of entities) of training and dev set
-        train_limit = 5
-        dev_limit = 5
+        train_limit = 5000
+        dev_limit = 5000
 
         train_data = training_set_creator.read_training(nlp=nlp_2,
                                                         training_dir=TRAINING_DIR,
@@ -198,13 +198,12 @@ def run_pipeline():
                             print("Error updating batch:", e)
 
                 if batchnr > 0:
-                    with el_pipe.model.use_params(optimizer.averages):
-                        el_pipe.context_weight = 1
-                        el_pipe.prior_weight = 1
-                        dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
-                        losses['entity_linker'] = losses['entity_linker'] / batchnr
-                        print("Epoch, train loss", itn, round(losses['entity_linker'], 2),
-                              " / dev acc avg", round(dev_acc_context, 3))
+                    el_pipe.cfg["context_weight"] = 1
+                    el_pipe.cfg["prior_weight"] = 1
+                    dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
+                    losses['entity_linker'] = losses['entity_linker'] / batchnr
+                    print("Epoch, train loss", itn, round(losses['entity_linker'], 2),
+                          " / dev acc avg", round(dev_acc_context, 3))
 
         # STEP 7: measure the performance of our trained pipe on an independent dev set
         if len(dev_data) and measure_performance:
@@ -218,24 +217,19 @@ def run_pipeline():
             print("dev acc random:", round(acc_r, 3), [(x, round(y, 3)) for x, y in acc_r_label.items()])
             print("dev acc prior:", round(acc_p, 3), [(x, round(y, 3)) for x, y in acc_p_label.items()])
 
-            with el_pipe.model.use_params(optimizer.averages):
-                # measuring combined accuracy (prior + context)
-                el_pipe.context_weight = 1
-                el_pipe.prior_weight = 1
-                dev_acc_combo, dev_acc_combo_dict = _measure_accuracy(dev_data, el_pipe, error_analysis=False)
-                print("dev acc combo avg:", round(dev_acc_combo, 3),
-                      [(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()])
+            # using only context
+            el_pipe.cfg["context_weight"] = 1
+            el_pipe.cfg["prior_weight"] = 0
+            dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
+            print("dev acc context avg:", round(dev_acc_context, 3),
+                  [(x, round(y, 3)) for x, y in dev_acc_context_dict.items()])
 
-                # using only context
-                el_pipe.context_weight = 1
-                el_pipe.prior_weight = 0
-                dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
-                print("dev acc context avg:", round(dev_acc_context, 3),
-                      [(x, round(y, 3)) for x, y in dev_acc_context_dict.items()])
-
-            # reset for follow-up tests
-            el_pipe.context_weight = 1
-            el_pipe.prior_weight = 1
+            # measuring combined accuracy (prior + context)
+            el_pipe.cfg["context_weight"] = 1
+            el_pipe.cfg["prior_weight"] = 1
+            dev_acc_combo, dev_acc_combo_dict = _measure_accuracy(dev_data, el_pipe, error_analysis=False)
+            print("dev acc combo avg:", round(dev_acc_combo, 3),
+                  [(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()])
 
         # STEP 8: apply the EL pipe on a toy example
         if to_test_pipeline:
@@ -250,8 +244,7 @@ def run_pipeline():
             print("STEP 9: testing NLP IO", datetime.datetime.now())
             print()
             print("writing to", NLP_2_DIR)
-            with el_pipe.model.use_params(optimizer.averages) and el_pipe.model.tok2vec.use_params(el_pipe.sgd_context.averages):
-                nlp_2.to_disk(NLP_2_DIR)
+            nlp_2.to_disk(NLP_2_DIR)
             print()
 
     # verify that the IO has gone correctly