diff --git a/medcat/ner/transformers_ner.py b/medcat/ner/transformers_ner.py
index 32eb2352..1cc4c5f2 100644
--- a/medcat/ner/transformers_ner.py
+++ b/medcat/ner/transformers_ner.py
@@ -4,7 +4,7 @@
 import datasets
 from spacy.tokens import Doc
 from datetime import datetime
-from typing import Iterable, Iterator, Optional, Dict, List, cast, Union, Tuple, Callable
+from typing import Iterable, Iterator, Optional, Dict, List, cast, Union, Tuple, Callable, Type
 from spacy.tokens import Span
 import inspect
 from functools import partial
@@ -87,7 +87,13 @@ def create_eval_pipeline(self):
             # NOTE: this will fix the DeID model(s) created before medcat 1.9.3
             #       though this fix may very well be unstable
             self.ner_pipe.tokenizer._in_target_context_manager = False
+        # if not hasattr(self.ner_pipe.tokenizer, 'split_special_tokens'):
+        #     # NOTE: this will fix the DeID model(s) created with transformers before 4.42
+        #     #       and allow them to run with later transforemrs
+        #     self.ner_pipe.tokenizer.split_special_tokens = False
         self.ner_pipe.device = self.model.device
+        self._consecutive_identical_failures = 0
+        self._last_exception: Optional[Tuple[str, Type[Exception]]] = None
 
     def get_hash(self) -> str:
         """A partial hash trying to catch differences between models.
@@ -390,34 +396,33 @@ def _process(self,
             #all_text_processed = self.tokenizer.encode_eval(all_text)
             # For now we will process the documents one by one, should be improved in the future to use batching
             for doc in docs:
-                try:
-                    res = self.ner_pipe(doc.text, aggregation_strategy=self.config.general['ner_aggregation_strategy'])
-                    doc.ents = []  # type: ignore
-                    for r in res:
-                        inds = []
-                        for ind, word in enumerate(doc):
-                            end_char = word.idx + len(word.text)
-                            if end_char <= r['end'] and end_char > r['start']:
-                                inds.append(ind)
-                            # To not loop through everything
-                            if end_char > r['end']:
-                                break
-                        if inds:
-                            entity = Span(doc, min(inds), max(inds) + 1, label=r['entity_group'])
-                            entity._.cui = r['entity_group']
-                            entity._.context_similarity = r['score']
-                            entity._.detected_name = r['word']
-                            entity._.id = len(doc._.ents)
-                            entity._.confidence = r['score']
-
-                            doc._.ents.append(entity)
-                    create_main_ann(self.cdb, doc)
-                    if self.cdb.config.general['make_pretty_labels'] is not None:
-                        make_pretty_labels(self.cdb, doc, LabelStyle[self.cdb.config.general['make_pretty_labels']])
-                    if self.cdb.config.general['map_cui_to_group'] is not None and self.cdb.addl_info.get('cui2group', {}):
-                        map_ents_to_groups(self.cdb, doc)
-                except Exception as e:
-                    logger.warning(e, exc_info=True)
+                res = self.ner_pipe(doc.text, aggregation_strategy=self.config.general['ner_aggregation_strategy'])
+                doc.ents = []  # type: ignore
+                for r in res:
+                    inds = []
+                    for ind, word in enumerate(doc):
+                        end_char = word.idx + len(word.text)
+                        if end_char <= r['end'] and end_char > r['start']:
+                            inds.append(ind)
+                        # To not loop through everything
+                        if end_char > r['end']:
+                            break
+                    if inds:
+                        entity = Span(doc, min(inds), max(inds) + 1, label=r['entity_group'])
+                        entity._.cui = r['entity_group']
+                        entity._.context_similarity = r['score']
+                        entity._.detected_name = r['word']
+                        entity._.id = len(doc._.ents)
+                        entity._.confidence = r['score']
+
+                        doc._.ents.append(entity)
+                create_main_ann(self.cdb, doc)
+                if self.cdb.config.general['make_pretty_labels'] is not None:
+                    make_pretty_labels(self.cdb, doc, LabelStyle[self.cdb.config.general['make_pretty_labels']])
+                if self.cdb.config.general['map_cui_to_group'] is not None and self.cdb.addl_info.get('cui2group', {}):
+                    map_ents_to_groups(self.cdb, doc)
+                self._consecutive_identical_failures = 0  # success
+                self._last_exception = None
             yield from docs
 
     # Override