Update metrics/scrolls.py

metrics/scrolls.py

@@ -31,24 +31,44 @@ Returns: depending on the Scrolls subset, one or several of:
     "exact_match": Exact Match score
     "f1": F1 score
     "rouge": ROUGE score
+
+Use the following code to download the metric:
+```
+import os, shutil
+from huggingface_hub import hf_hub_download
+def download_metric():
+    scrolls_metric_path = hf_hub_download(repo_id="datasets/tau/scrolls", filename="metrics/scrolls.py")
+    updated_scrolls_metric_path = (
+        os.path.dirname(scrolls_metric_path) + os.path.basename(scrolls_metric_path).replace(".", "_") + ".py"
+    )
+    shutil.copy(scrolls_metric_path, updated_scrolls_metric_path)
+    return updated_scrolls_metric_path
+
+scrolls_metric_path = download_metric()
+```
+
 Examples:
     predictions = ["exact match example", "hello there", "general kenobi"]  # List[str]
     references = [["exact match example"], ["hello", "hi there"], ["commander kenobi"]]  # List[List[str]]
 
-    >>> scrolls_metric = datasets.load_metric('src/metrics/scrolls.py', 'gov_report')  # 'gov_report' or any of ["qmsum", "summ_screen_fd"]
+    >>> scrolls_metric = datasets.load_metric(scrolls_metric_path, 'gov_report')  # 'gov_report' or any of ["qmsum", "summ_screen_fd"]
     >>> results = scrolls_metric.compute(predictions=predictions, references=references)
     >>> print(results)
-    {'rouge/rouge1': 72.2222, 'rouge/rouge2': 33.3333, 'rouge/rougeL': 72.2222, 'rouge/rougeLsum': 72.2222, 'rouge/geometric_mean': 55.8136, 'num_predicted': 3, 'mean_prediction_length_characters': 14.6667}
+    {'rouge/rouge1': 72.2222, 'rouge/rouge2': 33.3333, 'rouge/rougeL': 72.2222, 'rouge/rougeLsum': 72.2222, 'rouge/geometric_mean': 55.8136, 
+    'num_predicted': 3, 'mean_prediction_length_characters': 14.6667, 'scrolls_score': 55.8136, 
+    'display_keys': ['rouge/rouge1', 'rouge/rouge2', 'rouge/rougeL'], 'display': [72.2222, 33.3333, 72.2222]}
 
-    >>> scrolls_metric = datasets.load_metric('src/metrics/scrolls.py', 'contract_nli')  # 'contract_nli' or any of ["quality", "quality_hard"]
+    >>> scrolls_metric = datasets.load_metric(scrolls_metric_path, 'contract_nli')  # 'contract_nli' or "quality"
     >>> results = scrolls_metric.compute(predictions=predictions, references=references)
     >>> print(results)
-    {'exact_match': 33.3333, 'num_predicted': 3, 'mean_prediction_length_characters': 14.6667}
+    {'exact_match': 33.3333, 'num_predicted': 3, 'mean_prediction_length_characters': 14.6667, 'scrolls_score': 33.3333, 
+    'display_keys': ['exact_match'], 'display': [33.3333]}
 
-    >>> scrolls_metric = datasets.load_metric('src/metrics/scrolls.py', 'narrative_qa')  # 'narrative_qa' or any of ["qasper"]
+    >>> scrolls_metric = datasets.load_metric(scrolls_metric_path, 'narrative_qa')  # 'narrative_qa' or "qasper"
     >>> results = scrolls_metric.compute(predictions=predictions, references=references)
     >>> print(results)
-    {'f1': 72.2222, 'num_predicted': 3, 'mean_prediction_length_characters': 14.6667}
+    {'f1': 72.2222, 'num_predicted': 3, 'mean_prediction_length_characters': 14.6667, 'scrolls_score': 72.2222, 
+    'display_keys': ['f1'], 'display': [72.2222]}
 """
 
 DATASET_TO_METRICS = {