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README.md

@@ -1,67 +1,67 @@
 ---
 dataset_info:
   features:
-  - name: text
-    dtype: string
-  - name: tokens
-    sequence: string
-  - name: tokens_offsets
-    sequence:
-      sequence: int32
-  - name: clinical_entity_tags
-    sequence:
-      class_label:
-        names:
-          '0': O
-          '1': B-CLINENTITY
-          '2': I-CLINENTITY
-  - name: temporal_information_tags
-    sequence:
-      class_label:
-        names:
-          '0': O
-          '1': B-EVENT
-          '2': B-ACTOR
-          '3': B-BODYPART
-          '4': B-TIMEX3
-          '5': B-RML
-          '6': I-EVENT
-          '7': I-ACTOR
-          '8': I-BODYPART
-          '9': I-TIMEX3
-          '10': I-RML
+    - name: text
+      dtype: string
+    - name: tokens
+      sequence: string
+    - name: tokens_offsets
+      sequence:
+        sequence: int32
+    - name: clinical_entity_tags
+      sequence:
+        class_label:
+          names:
+            "0": O
+            "1": B-CLINENTITY
+            "2": I-CLINENTITY
+    - name: temporal_information_tags
+      sequence:
+        class_label:
+          names:
+            "0": O
+            "1": B-EVENT
+            "2": B-ACTOR
+            "3": B-BODYPART
+            "4": B-TIMEX3
+            "5": B-RML
+            "6": I-EVENT
+            "7": I-ACTOR
+            "8": I-BODYPART
+            "9": I-TIMEX3
+            "10": I-RML
   config_name: e3c
   splits:
-  - name: en.layer1
-    num_bytes: 1280534
-    num_examples: 1520
-  - name: en.layer2
-    num_bytes: 2566638
-    num_examples: 2873
-  - name: es.layer1
-    num_bytes: 1262006
-    num_examples: 1134
-  - name: es.layer2
-    num_bytes: 2524461
-    num_examples: 2347
-  - name: eu.layer1
-    num_bytes: 1537670
-    num_examples: 3126
-  - name: eu.layer2
-    num_bytes: 853766
-    num_examples: 1594
-  - name: fr.layer1
-    num_bytes: 1275362
-    num_examples: 1109
-  - name: fr.layer2
-    num_bytes: 2581993
-    num_examples: 2389
-  - name: it.layer1
-    num_bytes: 1299388
-    num_examples: 1146
-  - name: it.layer2
-    num_bytes: 2697483
-    num_examples: 2436
+    - name: en.layer1
+      num_bytes: 1280534
+      num_examples: 1520
+    - name: en.layer2
+      num_bytes: 2566638
+      num_examples: 2873
+    - name: es.layer1
+      num_bytes: 1262006
+      num_examples: 1134
+    - name: es.layer2
+      num_bytes: 2524461
+      num_examples: 2347
+    - name: eu.layer1
+      num_bytes: 1537670
+      num_examples: 3126
+    - name: eu.layer2
+      num_bytes: 853766
+      num_examples: 1594
+    - name: fr.layer1
+      num_bytes: 1275362
+      num_examples: 1109
+    - name: fr.layer2
+      num_bytes: 2581993
+      num_examples: 2389
+    - name: it.layer1
+      num_bytes: 1299388
+      num_examples: 1146
+    - name: it.layer2
+      num_bytes: 2697483
+      num_examples: 2436
   download_size: 230213492
   dataset_size: 17879301
 ---
@@ -92,4 +92,4 @@ information about clinical entities based on medical taxonomies, to be used for
     url = {https://uts.nlm.nih.gov/uts/umls/home},
     year = {2021},
 }
-```
+```

e3c.py

@@ -303,12 +303,9 @@ class E3C(datasets.GeneratorBasedBuilder):
                     "TIMEX3",
                     "RML",
                 ]:
-                    if len(content[entity_type]) != 0 and sentence[1] >= content[entity_type][0][0]:
+                    if len(content[entity_type]) != 0:
                         for entities in list(
-                            filter(
-                                lambda entity: sentence[0] <= entity[0] <= sentence[1],
-                                content[entity_type],
-                            )
+                            content[entity_type],
                         ):
                             annotated_tokens = [
                                 idx_token