diff --git a/quacc/dataset.py b/quacc/dataset.py
index 3f6e179..c11b0c9 100644
--- a/quacc/dataset.py
+++ b/quacc/dataset.py
@@ -39,14 +39,13 @@ class Dataset:
         self._target = target
 
         self.prevs = None
+        self.n_prevs = n_prevalences
         if prevs is not None:
             prevs = np.unique([p for p in prevs if p > 0.0 and p < 1.0])
             if prevs.shape[0] > 0:
                 self.prevs = np.sort(prevs)
                 self.n_prevs = self.prevs.shape[0]
 
-        self.n_prevs = n_prevalences
-
     def __spambase(self):
         return qp.datasets.fetch_UCIDataset("spambase", verbose=False).train_test
 
@@ -88,7 +87,7 @@ class Dataset:
         return DatasetSample(train, val, test)
 
     def get(self) -> List[DatasetSample]:
-        all_train, test = {
+        (all_train, test) = {
             "spambase": self.__spambase,
             "imdb": self.__imdb,
             "rcv1": self.__rcv1,
@@ -108,7 +107,7 @@ class Dataset:
 
         at_size = min(math.floor(len(all_train) * 0.5 / p) for p in prevs)
         datasets = []
-        for p in prevs:
+        for p in 1.0 - prevs:
             all_train_sampled = all_train.sampling(at_size, p, random_state=0)
             train, validation = all_train_sampled.split_stratified(
                 train_prop=TRAIN_VAL_PROP, random_state=0
@@ -122,10 +121,11 @@ class Dataset:
 
     @property
     def name(self):
-        if self._name == "rcv1":
-            return f"{self._name}_{self._target}"
-        else:
-            return self._name
+        return (
+            f"{self._name}_{self._target}_{self.n_prevs}prevs"
+            if self._name == "rcv1"
+            else f"{self._name}_{self.n_prevs}prevs"
+        )
 
 
 # >>> fetch_rcv1().target_names