Update DEFT2021.py

DEFT2021.py

@@ -585,57 +585,73 @@ class DEFT2021(datasets.GeneratorBasedBuilder):
 
         elif self.config.name.find("ner") != -1:
 
-          all_res = []
-      
-          key = 0
-      
-          with open(os.path.join(data_dir, 'distribution-corpus.txt')) as f_dist:
-      
-              distribution = [line.strip() for line in f_dist.readlines()]
-      
-              random.seed(4)
-              train = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'train 2021']
-              random.shuffle(train)
-              random.shuffle(train)
-              random.shuffle(train)
-              train, validation = np.split(train, [int(len(train)*0.73)])
-              test = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'test 2021']
-      
-              ann_path = Path(data_dir) / "DEFT-cas-cliniques"
-      
-              for guid, txt_file in enumerate(sorted(ann_path.glob("*.txt"))):
-                  brat_example = self.parse_brat_file(txt_file, parse_notes=True)
-      
-                  source_example = self._to_source_example(brat_example)
-      
-                  prod_format = self.convert_to_prodigy(source_example, _LABELS_BASE)
-      
-                  hf_format = self.convert_to_hf_format(prod_format)
-      
-                  hf_split = self.split_sentences(hf_format)
-      
-                  for h in hf_split:
-      
-                      all_res.append({
-                          "id": str(key),
-                          "document_id": h['document_id'],
-                          "tokens": h['tokens'],
-                          "ner_tags": h['ner_tags'],
-                      })
-      
-                      key += 1
-      
-              if split == "train":
-                  allowed_ids = list(train)
-              elif split == "validation":
-                  allowed_ids = list(validation)
-              elif split == "test":
-                  allowed_ids = list(test)
-      
-              print("train", len(train))
-              print("validation", len(validation))
-              print("test", len(test))
-      
-              for r in all_res:
-                  if r["document_id"]+'.txt' in allowed_ids:
-                      yield r["id"], r
+            print("Inside NER")
+
+            all_res = []
+
+            key = 0
+
+            with open(os.path.join(data_dir, 'distribution-corpus.txt')) as f_dist:
+
+                print("T1")
+
+                distribution = [line.strip() for line in f_dist.readlines()]
+
+                random.seed(4)
+                train = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'train 2021']
+                random.shuffle(train)
+                random.shuffle(train)
+                random.shuffle(train)
+                train, validation = np.split(train, [int(len(train)*0.73)])
+                test = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'test 2021']
+
+                print("T2")
+                ann_path = Path(data_dir) / "DEFT-cas-cliniques"
+
+                for guid, txt_file in enumerate(sorted(ann_path.glob("*.txt"))):
+
+                    print("T3")
+
+                    brat_example = self.parse_brat_file(txt_file, parse_notes=True)
+
+                    source_example = self._to_source_example(brat_example)
+
+                    print("T4")
+
+                    prod_format = self.convert_to_prodigy(source_example, _LABELS_BASE)
+
+                    print("T5")
+
+                    hf_format = self.convert_to_hf_format(prod_format)
+
+                    print("T6")
+
+                    hf_split = self.split_sentences(hf_format)
+
+                    print("T7")
+
+                    for h in hf_split:
+
+                        all_res.append({
+                            "id": str(key),
+                            "document_id": h['document_id'],
+                            "tokens": h['tokens'],
+                            "ner_tags": h['ner_tags'],
+                        })
+
+                        key += 1
+
+                if split == "train":
+                    allowed_ids = list(train)
+                elif split == "validation":
+                    allowed_ids = list(validation)
+                elif split == "test":
+                    allowed_ids = list(test)
+
+                print("train", len(train))
+                print("validation", len(validation))
+                print("test", len(test))
+
+                for r in all_res:
+                    if r["document_id"]+'.txt' in allowed_ids:
+                        yield r["id"], r