Upload bigbiohub.py

bigbiohub.py

@@ -24,8 +24,6 @@ class BigBioConfig(datasets.BuilderConfig):
     subset_id: str = None
 
 
-
-
 # shamelessly compied from:
 # https://github.com/huggingface/datasets/blob/master/src/datasets/utils/metadata.py
 langs_json = json.load(open("languages.json", "r"))
@@ -51,12 +49,38 @@ class Tasks(Enum):
     QUESTION_ANSWERING = "QA"
     TEXTUAL_ENTAILMENT = "TE"
     SEMANTIC_SIMILARITY = "STS"
+    TEXT_PAIRS_CLASSIFICATION = "TXT2CLASS"
     PARAPHRASING = "PARA"
     TRANSLATION = "TRANSL"
     SUMMARIZATION = "SUM"
     TEXT_CLASSIFICATION = "TXTCLASS"
 
 
+TASK_TO_SCHEMA = {
+    Tasks.NAMED_ENTITY_RECOGNITION: "KB",
+    Tasks.NAMED_ENTITY_DISAMBIGUATION: "KB",
+    Tasks.EVENT_EXTRACTION: "KB",
+    Tasks.RELATION_EXTRACTION: "KB",
+    Tasks.COREFERENCE_RESOLUTION: "KB",
+    Tasks.QUESTION_ANSWERING: "QA",
+    Tasks.TEXTUAL_ENTAILMENT: "TE",
+    Tasks.SEMANTIC_SIMILARITY: "PAIRS",
+    Tasks.TEXT_PAIRS_CLASSIFICATION: "PAIRS",
+    Tasks.PARAPHRASING: "T2T",
+    Tasks.TRANSLATION: "T2T",
+    Tasks.SUMMARIZATION: "T2T",
+    Tasks.TEXT_CLASSIFICATION: "TEXT",
+}
+
+SCHEMA_TO_TASKS = defaultdict(set)
+for task, schema in TASK_TO_SCHEMA.items():
+    SCHEMA_TO_TASKS[schema].add(task)
+SCHEMA_TO_TASKS = dict(SCHEMA_TO_TASKS)
+
+VALID_TASKS = set(TASK_TO_SCHEMA.keys())
+VALID_SCHEMAS = set(TASK_TO_SCHEMA.values())
+
+
 entailment_features = datasets.Features(
     {
         "id": datasets.Value("string"),
@@ -65,3 +89,121 @@ entailment_features = datasets.Features(
         "label": datasets.Value("string"),
     }
 )
+
+pairs_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "text_1": datasets.Value("string"),
+        "text_2": datasets.Value("string"),
+        "label": datasets.Value("string"),
+    }
+)
+
+qa_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "question_id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "question": datasets.Value("string"),
+        "type": datasets.Value("string"),
+        "choices": [datasets.Value("string")],
+        "context": datasets.Value("string"),
+        "answer": datasets.Sequence(datasets.Value("string")),
+    }
+)
+
+text_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "text": datasets.Value("string"),
+        "labels": [datasets.Value("string")],
+    }
+)
+
+text2text_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "text_1": datasets.Value("string"),
+        "text_2": datasets.Value("string"),
+        "text_1_name": datasets.Value("string"),
+        "text_2_name": datasets.Value("string"),
+    }
+)
+
+kb_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "passages": [
+            {
+                "id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "text": datasets.Sequence(datasets.Value("string")),
+                "offsets": datasets.Sequence([datasets.Value("int32")]),
+            }
+        ],
+        "entities": [
+            {
+                "id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "text": datasets.Sequence(datasets.Value("string")),
+                "offsets": datasets.Sequence([datasets.Value("int32")]),
+                "normalized": [
+                    {
+                        "db_name": datasets.Value("string"),
+                        "db_id": datasets.Value("string"),
+                    }
+                ],
+            }
+        ],
+        "events": [
+            {
+                "id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                # refers to the text_bound_annotation of the trigger
+                "trigger": {
+                    "text": datasets.Sequence(datasets.Value("string")),
+                    "offsets": datasets.Sequence([datasets.Value("int32")]),
+                },
+                "arguments": [
+                    {
+                        "role": datasets.Value("string"),
+                        "ref_id": datasets.Value("string"),
+                    }
+                ],
+            }
+        ],
+        "coreferences": [
+            {
+                "id": datasets.Value("string"),
+                "entity_ids": datasets.Sequence(datasets.Value("string")),
+            }
+        ],
+        "relations": [
+            {
+                "id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "arg1_id": datasets.Value("string"),
+                "arg2_id": datasets.Value("string"),
+                "normalized": [
+                    {
+                        "db_name": datasets.Value("string"),
+                        "db_id": datasets.Value("string"),
+                    }
+                ],
+            }
+        ],
+    }
+)
+
+SCHEMA_TO_FEATURES = {
+    "KB": kb_features,
+    "QA": qa_features,
+    "TE": entailment_features,
+    "T2T": text2text_features,
+    "TEXT": text_features,
+    "PAIRS": pairs_features,
+}