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README.md

@@ -8,58 +8,64 @@ dataset_info:
   - name: tokens_offsets
     sequence:
       sequence: int32
-  - name: ner_tags
+  - name: clinical_entity_tags
     sequence:
       class_label:
         names:
           '0': O
           '1': B-CLINENTITY
-          '2': B-EVENT
-          '3': B-ACTOR
-          '4': B-BODYPART
-          '5': B-TIMEX3
-          '6': B-RML
-          '7': I-CLINENTITY
-          '8': I-EVENT
-          '9': I-ACTOR
-          '10': I-BODYPART
-          '11': I-TIMEX3
-          '12': I-RML
+          '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: 1039582
+    num_bytes: 1280534
     num_examples: 1520
   - name: en.layer2
-    num_bytes: 2083098
+    num_bytes: 2566638
     num_examples: 2873
   - name: es.layer1
-    num_bytes: 1026950
+    num_bytes: 1262006
     num_examples: 1134
   - name: es.layer2
-    num_bytes: 2052073
+    num_bytes: 2524461
     num_examples: 2347
   - name: eu.layer1
-    num_bytes: 1252750
+    num_bytes: 1537670
     num_examples: 3126
   - name: eu.layer2
-    num_bytes: 697118
+    num_bytes: 853766
     num_examples: 1594
   - name: fr.layer1
-    num_bytes: 1036878
+    num_bytes: 1275362
     num_examples: 1109
   - name: fr.layer2
-    num_bytes: 2104253
+    num_bytes: 2581993
     num_examples: 2389
   - name: it.layer1
-    num_bytes: 1055588
+    num_bytes: 1299388
     num_examples: 1146
   - name: it.layer2
-    num_bytes: 2191803
+    num_bytes: 2697483
     num_examples: 2436
   download_size: 230213492
-  dataset_size: 14540093
+  dataset_size: 17879301
 ---
+
 # Dataset Card for E3C
 
 ## Dataset Description

e3c.py

@@ -57,17 +57,24 @@ class E3C(datasets.GeneratorBasedBuilder):
                 "text": datasets.Value("string"),
                 "tokens": datasets.Sequence(datasets.Value("string")),
                 "tokens_offsets": datasets.Sequence(datasets.Sequence(datasets.Value("int32"))),
-                "ner_tags": datasets.Sequence(
+                "clinical_entity_tags": datasets.Sequence(
                     datasets.features.ClassLabel(
                         names=[
                             "O",
                             "B-CLINENTITY",
+                            "I-CLINENTITY",
+                        ],
+                    ),
+                ),
+                "temporal_information_tags": datasets.Sequence(
+                    datasets.features.ClassLabel(
+                        names=[
+                            "O",
                             "B-EVENT",
                             "B-ACTOR",
                             "B-BODYPART",
                             "B-TIMEX3",
                             "B-RML",
-                            "I-CLINENTITY",
                             "I-EVENT",
                             "I-ACTOR",
                             "I-BODYPART",
@@ -286,7 +293,8 @@ class E3C(datasets.GeneratorBasedBuilder):
                 tokens_offsets = [
                     [token[0] - sentence[0], token[1] - sentence[0]] for token in filtered_tokens
                 ]
-                labels = ["O"] * len(filtered_tokens)
+                clinical_labels = ["O"] * len(filtered_tokens)
+                temporal_information_labels = ["O"] * len(filtered_tokens)
                 for entity_type in [
                     "CLINENTITY",
                     "EVENT",
@@ -308,14 +316,21 @@ class E3C(datasets.GeneratorBasedBuilder):
                                 if token[0] >= entities[0] and token[1] <= entities[1]
                             ]
                             for idx_token in annotated_tokens:
-                                if idx_token == annotated_tokens[0]:
-                                    labels[idx_token] = f"B-{entity_type}"
+                                if entity_type == "CLINENTITY":
+                                    if idx_token == annotated_tokens[0]:
+                                        clinical_labels[idx_token] = f"B-{entity_type}"
+                                    else:
+                                        clinical_labels[idx_token] = f"I-{entity_type}"
                                 else:
-                                    labels[idx_token] = f"I-{entity_type}"
+                                    if idx_token == annotated_tokens[0]:
+                                        temporal_information_labels[idx_token] = f"B-{entity_type}"
+                                    else:
+                                        temporal_information_labels[idx_token] = f"I-{entity_type}"
                 yield guid, {
                     "text": sentence[-1],
                     "tokens": list(map(lambda tokens: tokens[2], filtered_tokens)),
-                    "ner_tags": labels,
+                    "clinical_entity_tags": clinical_labels,
+                    "temporal_information_tags": temporal_information_labels,
                     "tokens_offsets": tokens_offsets,
                 }
-                guid += 1
+                guid += 1