Support 'domains, 'load_from' and redistribute dev and test while loading

qanom.py

@@ -16,7 +16,7 @@
 
 
 from dataclasses import dataclass
-from typing import Optional, Tuple
+from typing import Optional, Tuple, Union, Iterable, Set
 import datasets
 from pathlib import Path
 import pandas as pd
@@ -76,13 +76,17 @@ _URLs = {
 
 SpanFeatureType = datasets.Sequence(datasets.Value("int32"), length=2)
 
+SUPPOERTED_DOMAINS = {"wikinews", "wikipedia"}
+
 @dataclass
 class QANomBuilderConfig(datasets.BuilderConfig):
     """ Allow the loader to re-distribute the original dev and test splits between train, dev and test. """
     redistribute_dev: Tuple[float, float, float] = (0., 1., 0.)
     redistribute_test: Tuple[float, float, float] = (0., 0., 1.)
     load_from: str = "jsonl" # "csv" or "jsonl"
-    
+    domains: Union[str, Iterable[str]] = "all" # can provide also a subset of acceptable domains.
+    # Acceptable domains are {"wikipedia", "wikinews"} for dev and test (qasrl-2020)
+    # and {"wikipedia", "wikinews", "TQA"} for train (qasrl-2018)    
 
 # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
 class Qanom(datasets.GeneratorBasedBuilder):
@@ -93,7 +97,7 @@ class Qanom(datasets.GeneratorBasedBuilder):
     are for canidate nominalization which are judged to be non-predicates ("is_verbal"==False) or predicates with no QAs.
     In these cases, the qa fields (question, answers, answer_ranges) would be empty lists. """
 
-    VERSION = datasets.Version("1.1.0")
+    VERSION = datasets.Version("1.2.0")
     
     BUILDER_CONFIG_CLASS = QANomBuilderConfig
 
@@ -154,7 +158,22 @@ class Qanom(datasets.GeneratorBasedBuilder):
         """Returns SplitGenerators."""            
          
         assert self.config.load_from in ("csv", "jsonl")
-        
+
+        # Handle domain selection
+        domains: Set[str] = [] 
+        if self.config.domains == "all":
+            domains = SUPPOERTED_DOMAINS
+        elif isinstance(self.config.domains, str):
+            if self.config.domains in SUPPOERTED_DOMAINS:
+                domains = {self.config.domains}
+            else:
+                raise ValueError(f"Unrecognized domain '{self.config.domains}'; only {SUPPOERTED_DOMAINS} are supported")
+        else:
+            domains = set(self.config.domains) & SUPPOERTED_DOMAINS
+            if len(domains) == 0:
+                raise ValueError(f"Unrecognized domains '{self.config.domains}'; only {SUPPOERTED_DOMAINS} are supported")
+        self.config.domains = domains
+                
         self.corpus_base_path = Path(dl_manager.download_and_extract(_URLs[f"qanom_{self.config.load_from}"]))        
         if self.config.load_from == "csv":
             # prepare wiktionary for verb inflections inside 'self.verb_inflections'
@@ -239,6 +258,11 @@ class Qanom(datasets.GeneratorBasedBuilder):
             sent_obj = json.loads(line.strip())
             tokens = sent_obj['sentenceTokens']
             sentence = ' '.join(tokens)
+            sent_id = sent_obj['sentenceId']
+            # consider only selected domains
+            sent_domain = sent_id.split(":")[1]
+            if sent_domain not in self.config.domains:
+                continue
             for predicate_idx, verb_obj in sent_obj['verbEntries'].items():
                 verb_forms = verb_obj['verbInflectedForms']
                 predicate = tokens[int(predicate_idx)]
@@ -268,7 +292,7 @@ class Qanom(datasets.GeneratorBasedBuilder):
                     
                     yield qa_counter, {
                         "sentence": sentence,
-                        "sent_id": sent_obj['sentenceId'],
+                        "sent_id": sent_id,
                         "predicate_idx": predicate_idx,
                         "predicate": predicate,
                         "is_verbal": True,
@@ -313,6 +337,11 @@ class Qanom(datasets.GeneratorBasedBuilder):
         for counter, row in df.iterrows():
             # Each record (row) in csv is a QA or is stating a predicate/non-predicate with no QAs
             
+            # consider only selected domains
+            sent_domain = row.qasrl_id.split(":")[1]
+            if sent_domain not in self.config.domains:
+                continue
+            
             # Prepare question (slots)
             na_to_underscore = lambda s: "_" if pd.isna(s) else str(s)
             question = [] if pd.isna(row.question) else list(map(na_to_underscore, [