New schema: Add chat schema

seacrowd/utils/constants.py

@@ -10,6 +10,7 @@
     pairs_features_score,
     pairs_multi_features,
     qa_features,
+    chat_features,
     image_features,
     image_multi_features,
     imqa_features,
@@ -105,6 +106,7 @@ class Tasks(Enum):
     # Multi Text Generation
     DIALOGUE_SYSTEM = "DS"
     E2E_TASK_ORIENTED_DIALOGUE = "TOD"
+    MULTI_TURN_CONVERSATION = "MTC"
 
     # Self Supervised & Unsupervised Text
     PROMPTING = "PRT"
@@ -246,6 +248,7 @@ class Licenses(Enum):
     Tasks.TOKEN_LEVEL_LANGUAGE_IDENTIFICATION: "SEQ_LABEL",
     Tasks.COMMONSENSE_REASONING: "QA",
     Tasks.QUESTION_ANSWERING: "QA",
+    Tasks.MULTI_TURN_CONVERSATION: "CHAT",
     Tasks.CONCEPT_ALIGNMENT_CLASSIFICATION: "PAIRS",
     Tasks.NEXT_SENTENCE_PREDICTION: "PAIRS",
     Tasks.TEXT_RETRIEVAL: "PAIRS",
@@ -313,6 +316,7 @@ class Licenses(Enum):
     "KB": kb_features,
     "TREE": tree_features,
     "QA": qa_features,
+    "CHAT": chat_features,
     "T2T": text2text_features,
     "TEXT": text_features(),
     "TEXT_MULTI": text_multi_features(),

seacrowd/utils/schemas/__init__.py

@@ -5,6 +5,7 @@
 from .pairs import features_with_continuous_label as pairs_features_score
 from .pairs_multilabel import features as pairs_multi_features
 from .qa import features as qa_features
+from .chat import features as chat_features
 from .image import features as image_features
 from .image import multi_features as image_multi_features
 from .imqa import features as imqa_features
@@ -28,6 +29,7 @@
     "pairs_features_score",
     "pairs_multi_features",
     "qa_features",
+    "chat_features",
     "image_features",
     "image_multi_features",
     "imqa_features",
@@ -42,4 +44,4 @@
     "text2text_features",
     "video_features",
     "tod_features",
-]
+]

seacrowd/utils/schemas/chat.py

@@ -0,0 +1,24 @@
+"""
+Conversational Chat Schema
+"""
+import datasets
+
+features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "input": datasets.Sequence({
+            "role": datasets.ClassLabel(names=["system", "user", "assistant"]),
+            "content": datasets.Value("string"),
+        }),
+        "output": datasets.Value("string"),
+
+        # the schema of 'meta' aren't specified either to allow some flexibility
+        "meta": {}
+
+        # notes on how to use this field of 'meta'
+        # you can choose two of options:
+        # 1. defining as empty dict if you don't think it's usable in `_generate_examples`, or
+        # 2. defining meta as dict of key with intended colname meta and its val with dataset.Features class
+        #    in `_info` Dataloader method then populate it with the values in `_general_examples` Dataloader method
+    }
+)