Allow chunking model to reuse a spacy model instead of reloading

thus, reinclude the chunking model test in the global spacy model test

danlp/models/spacy_models.py

@@ -37,8 +37,8 @@ def load_spacy_model(cache_dir=DEFAULT_CACHE_DIR, verbose=False, textcat=None, v
     return nlp
 
 
-def load_spacy_chunking_model(cache_dir=DEFAULT_CACHE_DIR, verbose=False):
-    return SpacyChunking(cache_dir=cache_dir, verbose=verbose)
+def load_spacy_chunking_model(spacy_model=None,cache_dir=DEFAULT_CACHE_DIR, verbose=False):
+    return SpacyChunking(model=spacy_model, cache_dir=cache_dir, verbose=verbose)
 
 
 
@@ -47,9 +47,12 @@ class SpacyChunking:
     Spacy Chunking Model 
     """
 
-    def __init__(self, cache_dir=DEFAULT_CACHE_DIR, verbose=False):
+    def __init__(self, model=None, cache_dir=DEFAULT_CACHE_DIR, verbose=False):
 
-        self.model = load_spacy_model(cache_dir=cache_dir, verbose=verbose)
+        if model == None:
+            self.model = load_spacy_model(cache_dir=cache_dir, verbose=verbose)
+        else:
+            self.model = model
 
     def predict(self, text: Union[str, List[str]], bio=True):
         """

tests/test_spacy_model.py

@@ -3,7 +3,7 @@
 import spacy
 import operator
 from danlp.download import download_model, DEFAULT_CACHE_DIR, _unzip_process_func
-from danlp.models import load_spacy_model
+from danlp.models import load_spacy_model, load_spacy_chunking_model
 import os
 
 class TestSpacyModel(unittest.TestCase):
@@ -17,16 +17,18 @@ def test_download(self):
         self.assertListEqual(info['pipeline'], ['tagger', 'parser', 'ner'])
         self.assertEqual(info['lang'], 'da')
 
-
     def test_predictions(self):
-
         nlp = load_spacy_model()
         some_text = "Jeg gik en tur med Lars"
         doc = nlp(some_text)
         self.assertTrue(doc.is_parsed)
         self.assertTrue(doc.is_nered)
         self.assertTrue(doc.is_tagged)
 
+        chunker = load_spacy_chunking_model(spacy_model=nlp)
+        chunks = chunker.predict(some_text)
+        self.assertEqual(len(chunks), len(doc))
+
 
 class TestSpacySentimentModel(unittest.TestCase):
     def test_download(self):