update

app.py

@@ -18,7 +18,13 @@ def main(file, ext_threshold, article_type):
     ExtModel = load_ExtModel('model/LGB_model_F10_S.pkl')
     ext = extractive_method(sentJson, sentFeat, ExtModel, threshold=ext_threshold, TGB=False)
 
-    TOKENIZER, ABSTRMODEL = load_AbstrModel('fuhsiao/BioBART-PMC-EXT-Section')
+    abstr_model_path = ''
+    if article_type == 'non-specialized field':
+        abstr_model_path = 'model/BART-PMC-EXT-Section'
+    elif article_type == 'biomedical field':
+        abstr_model_path = 'model/BioBART-PMC-EXT-Section'
+        
+    TOKENIZER, ABSTRMODEL = load_AbstrModel(abstr_model_path)
     abstr = abstractive_method(ext, tokenizer=TOKENIZER, model=ABSTRMODEL)
 
     result = ''

model/BART-PMC-EXT-Section

@@ -0,0 +1 @@
+Subproject commit 952a510b0609c33faf7575d646a93a830f761ce7

model/BioBART-PMC-EXT-Section

@@ -0,0 +1 @@
+Subproject commit 8d32d050b4287ff94760b526e4d734b8c07d8929

model/all-MiniLM-L6-v2

@@ -0,0 +1 @@
+Subproject commit 7dbbc90392e2f80f3d3c277d6e90027e55de9125

utils/preprocess.py

@@ -143,7 +143,7 @@ def similarity(lst, ptm):
 def feature_extraction(title, section, sents): 
     lst = sent_lst(sents)
     tfisf = Tfisf(lst)
-    cosine = similarity(lst, "sentence-transformers/all-MiniLM-L6-v2")
+    cosine = similarity(lst, "model/all-MiniLM-L6-v2")
     
     # Number of sentences
     ns = len(sents)