CU-8693qx9yp Deid chunking - hugging face pipeline approach

medcat/cat.py

@@ -334,6 +334,7 @@ def attempt_unpack(cls, zip_path: str) -> str:
     def load_model_pack(cls,
                         zip_path: str,
                         meta_cat_config_dict: Optional[Dict] = None,
+                        ner_config_dict: Optional[Dict] = None,
                         load_meta_models: bool = True,
                         load_addl_ner: bool = True) -> "CAT":
         """Load everything within the 'model pack', i.e. the CDB, config, vocab and any MetaCAT models
@@ -346,6 +347,10 @@ def load_model_pack(cls,
                 A config dict that will overwrite existing configs in meta_cat.
                 e.g. meta_cat_config_dict = {'general': {'device': 'cpu'}}.
                 Defaults to None.
+            ner_config_dict (Optional[Dict]):
+                A config dict that will overwrite existing configs in transformers ner.
+                e.g. ner_config_dict = {'general': {'chunking_overlap_window': 6}.
+                Defaults to None.
             load_meta_models (bool):
                 Whether to load MetaCAT models if present (Default value True).
             load_addl_ner (bool):
@@ -381,15 +386,15 @@ def load_model_pack(cls,
         else:
             vocab = None
 
-        # Find meta models in the model_pack
+        # Find ner models in the model_pack
         trf_paths = [os.path.join(model_pack_path, path) for path in os.listdir(model_pack_path) if path.startswith('trf_')] if load_addl_ner else []
         addl_ner = []
         for trf_path in trf_paths:
-            trf = TransformersNER.load(save_dir_path=trf_path)
+            trf = TransformersNER.load(save_dir_path=trf_path,config_dict=ner_config_dict)
             trf.cdb = cdb # Set the cat.cdb to be the CDB of the TRF model
             addl_ner.append(trf)
 
-        # Find meta models in the model_pack
+        # Find metacat models in the model_pack
         meta_paths = [os.path.join(model_pack_path, path) for path in os.listdir(model_pack_path) if path.startswith('meta_')] if load_meta_models else []
         meta_cats = []
         for meta_path in meta_paths:

medcat/config_transformers_ner.py

@@ -13,6 +13,8 @@ class General(MixingConfig, BaseModel):
     """How many characters are piped at once into the meta_cat class"""
     ner_aggregation_strategy: str = 'simple'
     """Agg strategy for HF pipeline for NER"""
+    chunking_overlap_window: Optional[int] = 5
+    """Size of the overlap window used for chunking"""
     test_size: float = 0.2
     last_train_on: Optional[int] = None
     verbose_metrics: bool = False

medcat/ner/transformers_ner.py

@@ -76,9 +76,11 @@ def __init__(self, cdb, config: Optional[ConfigTransformersNER] = None,
         else:
             self.training_arguments = training_arguments
 
-
     def create_eval_pipeline(self):
-        self.ner_pipe = pipeline(model=self.model, task="ner", tokenizer=self.tokenizer.hf_tokenizer)
+
+        if self.config.general['chunking_overlap_window'] is None:
+            logger.warning("Chunking overlap window attribute in the config is set to None, hence chunking is disabled. Be cautious, PII data MAY BE REVEALED. To enable chunking, set the value to 0 or above.")
+        self.ner_pipe = pipeline(model=self.model, task="ner", tokenizer=self.tokenizer.hf_tokenizer,stride=self.config.general['chunking_overlap_window'])
         if not hasattr(self.ner_pipe.tokenizer, '_in_target_context_manager'):
             # NOTE: this will fix the DeID model(s) created before medcat 1.9.3
             #       though this fix may very well be unstable

medcat/utils/ner/deid.py

@@ -34,14 +34,18 @@
 - config
 - cdb
 """
-from typing import Union, Tuple, Any, List, Iterable, Optional
+from typing import Union, Tuple, Any, List, Iterable, Optional, Dict
+import logging
 
 from medcat.cat import CAT
 from medcat.utils.ner.model import NerModel
 
 from medcat.utils.ner.helpers import _deid_text as deid_text, replace_entities_in_text
 
 
+logger = logging.getLogger(__name__)
+
+
 class DeIdModel(NerModel):
     """The DeID model.
 
@@ -93,6 +97,25 @@ def deid_multi_texts(self,
         Returns:
             List[str]: List of deidentified documents.
         """
+        # NOTE: we assume we're using the 1st (and generally only)
+        #       additional NER model.
+        #       the same assumption is made in the `train` method
+        chunking_overlap_window = self.cat._addl_ner[0].config.general.chunking_overlap_window
+        if chunking_overlap_window is not None:
+            logger.warning("Chunking overlap window has been set to %s. "
+                           "This may cause multiprocessing to stall in certain"
+                           "environments and/or situations and has not been"
+                           "fully tested.",
+                           chunking_overlap_window)
+            logger.warning("If the following hangs forever (i.e doesn't finish) "
+                           "but you still wish to run on multiple processes you can set "
+                           "`cat._addl_ner[0].config.general.chunking_overlap_window = None` "
+                           "and then either a) save the model on disk and load it back up, or "
+                           " b) call `cat._addl_ner[0].create_eval_pipeline()` to recreate the pipe. "
+                           "However, this will remove chunking from the input text, which means "
+                           "only the first 512 tokens will be recognised and thus only the "
+                           "first part of longer documents (those with more than 512) tokens"
+                           "will be deidentified. ")
         entities = self.cat.get_entities_multi_texts(texts, addl_info=addl_info,
                                                      n_process=n_process, batch_size=batch_size)
         out = []
@@ -110,7 +133,7 @@ def deid_multi_texts(self,
         return out
 
     @classmethod
-    def load_model_pack(cls, model_pack_path: str) -> 'DeIdModel':
+    def load_model_pack(cls, model_pack_path: str, config: Optional[Dict] = None) -> 'DeIdModel':
         """Load DeId model from model pack.
 
         The method first loads the CAT instance.
@@ -119,6 +142,7 @@ def load_model_pack(cls, model_pack_path: str) -> 'DeIdModel':
         valid DeId model.
 
         Args:
+            config: Config for DeId model pack (primarily for stride of overlap window)
             model_pack_path (str): The model pack path.
 
         Raises:
@@ -127,7 +151,7 @@ def load_model_pack(cls, model_pack_path: str) -> 'DeIdModel':
         Returns:
             DeIdModel: The resulting DeI model.
         """
-        ner_model = NerModel.load_model_pack(model_pack_path)
+        ner_model = NerModel.load_model_pack(model_pack_path,config=config)
         cat = ner_model.cat
         if not cls._is_deid_model(cat):
             raise ValueError(

medcat/utils/ner/model.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Tuple, Union, Optional
+from typing import Any, List, Tuple, Union, Optional, Dict
 
 from spacy.tokens import Doc
 
@@ -94,16 +94,17 @@ def create(cls, ner: Union[TransformersNER, List[TransformersNER]]) -> 'NerModel
         return cls(cat)
 
     @classmethod
-    def load_model_pack(cls, model_pack_path: str) -> 'NerModel':
+    def load_model_pack(cls, model_pack_path: str,config: Optional[Dict] = None) -> 'NerModel':
         """Load NER model from model pack.
 
         The method first wraps the loaded CAT instance.
 
         Args:
+            config: Config for DeId model pack (primarily for stride of overlap window)
             model_pack_path (str): The model pack path.
 
         Returns:
             NerModel: The resulting DeI model.
         """
-        cat = CAT.load_model_pack(model_pack_path)
+        cat = CAT.load_model_pack(model_pack_path,ner_config_dict=config)
         return cls(cat)

tests/utils/ner/test_deid.py

@@ -41,11 +41,11 @@ def test_can_create_model(self):
         deid_model = deid.DeIdModel.create(ner)
         self.assertIsNotNone(deid_model)
 
-
 def _add_model(cls):
     cdb = make_or_update_cdb(TRAIN_DATA)
     config = transformers_ner.ConfigTransformersNER()
     config.general['test_size'] = 0.1  # Usually set this to 0.1-0.2
+    config.general['chunking_overlap_window'] = None
     cls.ner = transformers_ner.TransformersNER(cdb=cdb, config=config)
     cls.ner.training_arguments.num_train_epochs = 1  # Use 5-10 normally
     # As we are NOT training on a GPU that can, we'll set it to 1