Update PxCorpus.py

PxCorpus.py

@@ -36,8 +36,35 @@ of 55 participants (38% non-experts, 25% doctors, 36% medical practitioners), ma
 
 _URL = "https://zenodo.org/record/6524162/files/pxslu.zip?download=1"
 
+class StringIndex:
+
+    def __init__(self, vocab):
+
+        self.vocab_struct = {}
+
+        print("Start building the index!")
+        for term in vocab:
+
+            if len(term) == 0:
+                continue
+
+            # Index terms by their first letter and length
+            key = (term[0], len(term))
+
+            if key not in self.vocab_struct:
+                self.vocab_struct[key] = []
+            
+            self.vocab_struct[key].append(term)
+
+        print("Finished building the index!")
+
+    def find(self, term):
+        return term in self.vocab_struct[(term[0], len(term))]
+
 class PxCorpus(datasets.GeneratorBasedBuilder):
 
+    VOCAB = StringIndex(vocab=open("./vocabulary_nachos_lowercased.txt","r").read().split("\n"))
+
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(name=f"default", version="1.0.0", description=f"PxCorpus data"),
     ]
@@ -59,6 +86,11 @@ class PxCorpus(datasets.GeneratorBasedBuilder):
                         names=['O', 'B-A', 'B-cma_event', 'B-d_dos_form', 'B-d_dos_form_ext', 'B-d_dos_up', 'B-d_dos_val', 'B-dos_cond', 'B-dos_uf', 'B-dos_val', 'B-drug', 'B-dur_ut', 'B-dur_val', 'B-fasting', 'B-freq_days', 'B-freq_int_v1', 'B-freq_int_v1_ut', 'B-freq_int_v2', 'B-freq_int_v2_ut', 'B-freq_startday', 'B-freq_ut', 'B-freq_val', 'B-inn', 'B-max_unit_uf', 'B-max_unit_ut', 'B-max_unit_val', 'B-min_gap_ut', 'B-min_gap_val', 'B-qsp_ut', 'B-qsp_val', 'B-re_ut', 'B-re_val', 'B-rhythm_hour', 'B-rhythm_perday', 'B-rhythm_rec_ut', 'B-rhythm_rec_val', 'B-rhythm_tdte', 'B-roa', 'I-cma_event', 'I-d_dos_form', 'I-d_dos_form_ext', 'I-d_dos_up', 'I-d_dos_val', 'I-dos_cond', 'I-dos_uf', 'I-dos_val', 'I-drug', 'I-fasting', 'I-freq_startday', 'I-inn', 'I-rhythm_tdte', 'I-roa'],
                     ),
                 ),
+                "is_oov": datasets.Sequence(
+                    datasets.features.ClassLabel(
+                        names=['is_not_oov', 'is_oov'],
+                    ),
+                ),
             }
         )
         
@@ -109,6 +141,7 @@ class PxCorpus(datasets.GeneratorBasedBuilder):
 
         tokens = []
         ner_tags = []
+        is_oov = []
 
         for pair in document.split("\n"):
 
@@ -118,8 +151,9 @@ class PxCorpus(datasets.GeneratorBasedBuilder):
             text, label = pair.split("\t")
             tokens.append(text)
             ner_tags.append(label)
+            is_oov.append(VOCAB.find(text))
 
-        return tokens, ner_tags
+        return tokens, ner_tags, is_oov
 
     def _generate_examples(self, filepath_1, filepath_2, filepath_3, split):
 
@@ -143,7 +177,7 @@ class PxCorpus(datasets.GeneratorBasedBuilder):
             text = seq_in[idx]
             label = seq_label[idx]
 
-            tokens, ner_tags = self.getTokenTags(docs[idx])
+            tokens, ner_tags, is_oov = self.getTokenTags(docs[idx])
 
             if len(text) <= 0 or len(label) <= 0:
                 continue
@@ -154,6 +188,7 @@ class PxCorpus(datasets.GeneratorBasedBuilder):
                 "label": label,
                 "tokens": tokens,
                 "ner_tags": ner_tags,
+                "is_oov": is_oov,
             })
             
             key += 1