Add `topics_with_context`

trec-cast-2019-multi-turn.py

@@ -48,6 +48,7 @@ _LICENSE = ""
 _URL = "https://huggingface.co/datasets/uva-irlab/trec-cast-2019-multi-turn/resolve/main/"
 _URLs = {
     'topics': _URL+"cast2019_test_annotated.tsv",
+    'topics_with_context': _URL+"cast2019_test_annotated_with_context.tsv",
     'qrels': _URL+"2019qrels.txt",
     'test_collection': {
         'car': "http://trec-car.cs.unh.edu/datareleases/v2.0/paragraphCorpus.v2.0.tar.xz",
@@ -76,8 +77,11 @@ class TrecCast2019MultiTurn(datasets.GeneratorBasedBuilder):
                                version=VERSION,
                                description=""),
         datasets.BuilderConfig(name="topics",
-                               version=VERSION,
+                               version="1.0.1",
                                description="The topics contain the queries, query IDs and their history."),
+        datasets.BuilderConfig(name="topics_with_context",
+                               version=VERSION,
+                               description="The topics contain the queries with relevant terms from the history, query IDs and their history."),
         datasets.BuilderConfig(name="test_collection",
                                version=VERSION,
                                description="The test collection will provide the passages of TREC CAR and MSMARCO"),
@@ -99,6 +103,12 @@ class TrecCast2019MultiTurn(datasets.GeneratorBasedBuilder):
                 "query": datasets.Value("string"),
             })
             download_size = 1138032
+        elif self.config.name == "topics_with_context":
+            features = datasets.Features({
+                "qid": datasets.Value("string"),
+                "history": datasets.features.Sequence(feature=datasets.Value('string')),
+                "query": datasets.Value("string"),
+            })
         elif self.config.name == "qrels":
             features = datasets.Features({
                 "qid": datasets.Value("string"),
@@ -143,6 +153,7 @@ class TrecCast2019MultiTurn(datasets.GeneratorBasedBuilder):
         urlkey = 'test_collection' if self.config.name == 'test_collection_sample' else self.config.name
         my_urls = _URLs[urlkey]
         downloaded_files = dl_manager.download_and_extract(my_urls)
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
@@ -172,7 +183,7 @@ class TrecCast2019MultiTurn(datasets.GeneratorBasedBuilder):
             for qid in qrels.keys():
                 yield qid, {'qid': qid, 'qrels': qrels[qid]}
 
-        elif split == 'topics':
+        elif split == 'topics' or split == 'topics_with_context':
             topics_file = csv.reader(open(file), delimiter="\t")
             topics = defaultdict(list)
             for row in topics_file: