--- a
+++ b/medacy/pipelines/base/base_pipeline.py
@@ -0,0 +1,144 @@
+import inspect
+import time
+from abc import ABC, abstractmethod
+
+import spacy
+
+import medacy
+from medacy.pipeline_components.feature_overlayers.gold_annotator_component import GoldAnnotatorOverlayer
+
+
+class BasePipeline(ABC):
+    """
+    An abstract wrapper for a Medical NER Pipeline
+    """
+
+    def __init__(self, entities, spacy_pipeline, **kwargs):
+        """
+        Initializes a pipeline
+        :param entities: a list of entities, or an empty list (or None) if the pipeline is for a model that
+        has already been fitted
+        :param spacy_pipeline: the corresponding spacy pipeline (language) to utilize.
+        :param cuda_device: the GPU to use, if any (defaults to -1 for using the CPU)
+        """
+        self.entities = entities or []
+        self.spacy_pipeline = spacy_pipeline
+        self.overlayers = []  # Stores feature overlayers
+        self._kwargs = kwargs
+
+        # Set tokenizer, if something other than the spaCy pipeline's tokenizer is specified in get_tokenizer()
+        tokenizer = self.get_tokenizer()
+        if tokenizer:
+            # 'tokenizer' is a class with an attribute named 'tokenizer'
+            self.spacy_pipeline.tokenizer = tokenizer.tokenizer
+
+        self.add_component(GoldAnnotatorOverlayer, entities)
+
+        # The following code was causing GPU errors because you cannot specify which GPU spaCy will use;
+        # You may uncomment this code if you know you have access to the GPU that spaCy will use.
+
+        # if cuda_device >= 0:
+        #     spacy.require_gpu()
+
+
+    @abstractmethod
+    def get_tokenizer(self):
+        """Returns an instance of a tokenizer"""
+        pass
+
+    @abstractmethod
+    def get_learner(self):
+        """Returns an instance of a sci-kit learn compatible learning algorithm."""
+        pass
+
+    @abstractmethod
+    def get_feature_extractor(self):
+        """Returns an instant of FeatureExtractor with all configs set."""
+        pass
+
+    def add_component(self, component, *argv, **kwargs):
+        """
+        Adds a given component to pipeline
+        :param component: a subclass of BaseOverlayer (the class itself; not an instance)
+        :param args, kwargs: arguments to pass to the constructor of the component
+        """
+
+        current_components = [component_name for component_name, proc in self.spacy_pipeline.pipeline]
+
+        assert component.name not in current_components, "%s is already in the pipeline." % component.name
+
+        for dependent in component.dependencies:
+            assert dependent in current_components, "%s depends on %s but it hasn't been added to the pipeline" % (component, dependent)
+
+        new_component = component(self.spacy_pipeline, *argv, **kwargs)
+        self.spacy_pipeline.add_pipe(new_component)
+
+        if component.name != "gold_annotator":
+            self.overlayers.append(new_component)
+
+    def get_component_names(self):
+        """
+        Retrieves a listing of all components currently in the pipeline.
+        :return: a list of components inside the pipeline.
+        """
+        return [component_name for component_name, _ in self.spacy_pipeline.pipeline if component_name != 'ner']
+
+    def __call__(self, doc, predict=False):
+        """
+        Passes a single document through the pipeline.
+        All relevant document attributes should be set prior to this call.
+        :param self:
+        :param doc: the document to annotate over
+        :return: the annotated document
+        """
+
+        for component_name, proc in self.spacy_pipeline.pipeline:
+            if predict and component_name == "gold_annotator":
+                continue
+            doc = proc(doc)
+            if component_name == 'ner':
+                # remove labeled default entities
+                doc.ents = []
+
+        return doc
+
+    def get_report(self):
+        """
+        Generates a report about the pipeline class's configuration
+        :return: str
+        """
+
+        # Get data about these components
+        learner_name, learner = self.get_learner()
+        tokenizer = self.get_tokenizer()
+        feature_extractor = self.get_feature_extractor()
+        spacy_metadata = self.spacy_pipeline.meta
+
+        # Start the report with the name of the class and the docstring
+        report = f"{type(self).__name__}\n{self.__doc__}\n\n"
+
+        report += f"Report created at {time.asctime()}\n\n"
+        report += f"MedaCy Version: {medacy.__version__}\nSpaCy Version: {spacy.__version__}\n"
+        report += f"SpaCy Model: {spacy_metadata['name']}, version {spacy_metadata['version']}\n"
+        report += f"Entities: {self.entities}\n"
+        report += f"Constructor arguments: {self._kwargs}\n\n"
+
+        # Print data about the feature overlayers
+        if self.overlayers:
+            report += "Feature Overlayers:\n\n"
+            report += "\n\n".join(o.get_report() for o in self.overlayers) + '\n\n'
+
+        # Print data about the feature extractor
+        report += f"Feature Extractor: {type(feature_extractor).__name__} at {inspect.getfile(type(feature_extractor))}\n"
+        report += f"\tWindow Size: {feature_extractor.window_size}\n"
+        report += f"\tSpaCy Features: {feature_extractor.spacy_features}\n"
+
+        # Print the name and location of the remaining components
+        report += f"Learner: {learner_name} at {inspect.getfile(type(learner))}\n"
+
+        if self.get_tokenizer():
+            report += f"Tokenizer: {type(tokenizer).__name__} at {inspect.getfile(type(tokenizer))}\n"
+        else:
+            report += f"Tokenizer: spaCy pipeline default\n"
+
+        return report