Calculate metric using evaluate instead of datasets

metrics/scrolls.py

@@ -3,6 +3,7 @@
 from collections import defaultdict
 from copy import deepcopy
 import datasets
+import evaluate
 
 # fmt: off
 from .rouge import compute_rouge, postprocess_text as rouge_postprocess_text  # From: https://huggingface.co/datasets/tau/scrolls/raw/main/metrics/rouge.py
@@ -58,20 +59,20 @@ 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(scrolls_metric_path, 'gov_report')  # 'gov_report' or any of ["qmsum", "summ_screen_fd"]
+    >>> scrolls_metric = evaluate.load(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, 'scrolls_score': 55.8136, 
     'display_keys': ['rouge/rouge1', 'rouge/rouge2', 'rouge/rougeL'], 'display': [72.2222, 33.3333, 72.2222]}
 
-    >>> scrolls_metric = datasets.load_metric(scrolls_metric_path, 'contract_nli')  # 'contract_nli' or "quality"
+    >>> scrolls_metric = evaluate.load(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, 'scrolls_score': 33.3333, 
     'display_keys': ['exact_match'], 'display': [33.3333]}
 
-    >>> scrolls_metric = datasets.load_metric(scrolls_metric_path, 'narrative_qa')  # 'narrative_qa' or "qasper"
+    >>> scrolls_metric = evaluate.load(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, 'scrolls_score': 72.2222, 
@@ -123,7 +124,7 @@ DATASET_TO_METRICS = {
 
 
 @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class Scrolls(datasets.Metric):
+class Scrolls(evaluate.Metric):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
@@ -173,7 +174,7 @@ class Scrolls(datasets.Metric):
             self._metrics_to_compute = DATASET_TO_METRICS[self.config_name]["metrics_to_compute"]
 
     def _info(self):
-        return datasets.MetricInfo(
+        return evaluate.MetricInfo(
             description=_DESCRIPTION,
             citation=_CITATION,
             inputs_description=_KWARGS_DESCRIPTION,