Update metrics/scrolls.py

metrics/scrolls.py

@@ -1,4 +1,4 @@
-""" Scrolls benchmark metric. """
+""" SCROLLS benchmark metric. """
 
 from collections import defaultdict
 from copy import deepcopy
@@ -15,10 +15,9 @@ _CITATION = """\
 """
 
 _DESCRIPTION = """\
-Scrolls: Standardized CompaRison Over Long Language Sequences
-Recent progress in NLP has created models that can process long inputs consisting of thousands of words.
-But how well do these models understand the information in the input text?
-The Scrolls benchmark aims to measure the ability of models to semantically understand long texts.
+SCROLLS: Standardized CompaRison Over Long Language Sequences.
+A suite of natural language datasets that require reasoning over long texts.
+https://scrolls-benchmark.com/
 """
 
 _KWARGS_DESCRIPTION = """
@@ -53,14 +52,46 @@ Examples:
 """
 
 DATASET_TO_METRICS = {
-    "contract_nli": {"metrics_to_compute": ["exact_match"], "score": "exact_match"},
-    "gov_report": {"metrics_to_compute": ["rouge"], "score": "rouge/geometric_mean"},
-    "narrative_qa": {"metrics_to_compute": ["f1"], "score": "f1"},
-    "qasper": {"metrics_to_compute": ["f1"], "score": "f1"},
-    "qmsum": {"metrics_to_compute": ["rouge"], "score": "rouge/geometric_mean"},
-    "summ_screen_fd": {"metrics_to_compute": ["rouge"], "score": "rouge/geometric_mean"},
-    "quality": {"metrics_to_compute": ["exact_match"], "score": "exact_match"},
-    "quality_hard": {"metrics_to_compute": ["exact_match"], "score": "exact_match"},
+    "contract_nli": {
+        "metrics_to_compute": ["exact_match"],
+        "scrolls_score_key": "exact_match",
+        "display_keys": ["exact_match"],
+    },
+    "gov_report": {
+        "metrics_to_compute": ["rouge"],
+        "scrolls_score_key": "rouge/geometric_mean",
+        "display_keys": ["rouge/rouge1", "rouge/rouge2", "rouge/rougeL"],
+    },
+    "narrative_qa": {
+        "metrics_to_compute": ["f1"],
+        "scrolls_score_key": "f1",
+        "display_keys": ["f1"],
+    },
+    "qasper": {
+        "metrics_to_compute": ["f1"],
+        "scrolls_score_key": "f1",
+        "display_keys": ["f1"],
+    },
+    "qmsum": {
+        "metrics_to_compute": ["rouge"],
+        "scrolls_score_key": "rouge/geometric_mean",
+        "display_keys": ["rouge/rouge1", "rouge/rouge2", "rouge/rougeL"],
+    },
+    "summ_screen_fd": {
+        "metrics_to_compute": ["rouge"],
+        "scrolls_score_key": "rouge/geometric_mean",
+        "display_keys": ["rouge/rouge1", "rouge/rouge2", "rouge/rougeL"],
+    },
+    "quality": {
+        "metrics_to_compute": ["exact_match"],
+        "scrolls_score_key": "exact_match",
+        "display_keys": ["exact_match"],
+    },
+    "quality_hard": {
+        "metrics_to_compute": ["exact_match"],
+        "scrolls_score_key": None,
+        "display_keys": ["exact_match"],
+    },
 }
 
 
@@ -153,7 +184,17 @@ class Scrolls(datasets.Metric):
         metrics = {key: round(value, 4) for key, value in metrics.items()}
 
         if self.config_name in DATASET_TO_METRICS:
-            metrics["score"] = metrics[DATASET_TO_METRICS[self.config_name]["score"]]
+            scrolls_score_key = DATASET_TO_METRICS[self.config_name]["scrolls_score_key"]
+            if scrolls_score_key is not None:
+                metrics["scrolls_score"] = metrics[scrolls_score_key]
+            else:
+                metrics["scrolls_score"] = None
+
+            display_keys = DATASET_TO_METRICS[self.config_name]["display_keys"]
+            metrics["display_keys"] = display_keys
+            metrics["display"] = []
+            for display_key in display_keys:
+                metrics["display"].append(metrics[display_key])
 
         return metrics