#162 rename model

example_annotate.py

@@ -51,7 +51,7 @@
 
     # get the predictions
     crf = PyCRFSuite()
-    crf.tag(dataset, pkg_resources.resource_filename('nalaf.data', 'default_model'), class_id=MUT_CLASS_ID)
+    crf.tag(dataset, pkg_resources.resource_filename('nalaf.data', 'example_entity_model'), class_id=MUT_CLASS_ID)
 
     GNormPlusGeneTagger().tag(dataset, uniprot=True)
     StubSameSentenceRelationExtractor().tag(dataset)

nalaf/learning/crfsuite.py

@@ -54,7 +54,7 @@ def __init__(self, directory, minify=False):
         warnings.warn('Depricated. Please use PyCRFSuite instead', DeprecationWarning)
         self.directory = os.path.abspath(directory)
         """the directory where the CRFSuite executable is located"""
-        self.model_filename = 'default_model'
+        self.model_filename = 'example_entity_model'
         """name to be used for saving the model"""
         if sys.platform.startswith('linux'):
             self.crf_suite_call = './crfsuite'
@@ -133,7 +133,7 @@ def read_predictions(self, dataset, prediction_file='output.txt', class_id = MUT
 
         For example first we would call:
             * crf.create_input_file(dataset=test, mode='test')
-            * crf.test(options='-m default_model -i test > output.txt')
+            * crf.test(options='-m example_entity_model -i test > output.txt')
         Then we would call:
             * crf.read_predictions(dataset=test)
         """

nalaf/learning/taggers.py

@@ -105,7 +105,7 @@ class CRFSuiteTagger(Tagger):
     :type crf_suite: nalaf.learning.crfsuite.CRFSuite
     """
 
-    def __init__(self, predicts_classes, crf_suite, model_file='default_model'):
+    def __init__(self, predicts_classes, crf_suite, model_file='example_entity_model'):
         super().__init__(predicts_classes)
         self.crf_suite = crf_suite
         """an instance of CRFSuite used to actually generate predictions"""