diff --git a/.gitignore b/.gitignore
index 5417be0f..89dda433 100644
--- a/.gitignore
+++ b/.gitignore
@@ -58,7 +58,4 @@ run_sonar.py
 setup_ucloud.sh
 
 # paper
-src/scripts/paper/figure.r
-src/scripts/paper/speed_filtered.csv
-src/scripts/paper/speed.csv
-src/scripts/paper/tmp.csv
+src/scripts/paper/*
diff --git a/src/seb/cache/sentence-transformers__all-MiniLM-L6-v2/LCC.json b/src/seb/cache/sentence-transformers__all-MiniLM-L6-v2/LCC.json
index db25875d..79c2c00b 100644
--- a/src/seb/cache/sentence-transformers__all-MiniLM-L6-v2/LCC.json
+++ b/src/seb/cache/sentence-transformers__all-MiniLM-L6-v2/LCC.json
@@ -1 +1 @@
-{"task_name":"LCC","task_description":"The leipzig corpora collection, annotated for sentiment","task_version":"1.1.1","time_of_run":"2024-02-11T14:09:11.351629","scores":{"da":{"accuracy":0.3846666666666666,"f1":0.3650136884557438,"accuracy_stderr":0.03664241622309678,"f1_stderr":0.03540233062350939,"main_score":0.3846666666666666}},"main_score":"accuracy"}
\ No newline at end of file
+{"task_name":"LCC","task_description":"The leipzig corpora collection, annotated for sentiment","task_version":"1.1.1","time_of_run":"2024-02-15T10:45:54.680745","scores":{"da":{"accuracy":0.3846666666666666,"f1":0.3650136884557438,"accuracy_stderr":0.03664241622309678,"f1_stderr":0.03540233062350939,"main_score":0.3846666666666666}},"main_score":"accuracy"}
\ No newline at end of file
diff --git a/src/seb/interfaces/mteb_task.py b/src/seb/interfaces/mteb_task.py
index e30b3ffe..6df486cb 100644
--- a/src/seb/interfaces/mteb_task.py
+++ b/src/seb/interfaces/mteb_task.py
@@ -48,7 +48,7 @@ def load_data(self) -> DatasetDict:
 
         return DatasetDict(ds)
 
-    def get_descriptive_stats(self) -> DescriptiveDatasetStats:
+    def get_documents(self) -> list[str]:
         ds: DatasetDict = self.load_data()
         texts = []
         splits = self.mteb_task.description["eval_splits"]
@@ -67,6 +67,10 @@ def get_descriptive_stats(self) -> DescriptiveDatasetStats:
                 for text_column in self._text_columns:
                     texts += ds[split][text_column]
 
+        return texts
+
+    def get_descriptive_stats(self) -> DescriptiveDatasetStats:
+        texts = self.get_documents()
         document_lengths = np.array([len(text) for text in texts])
 
         mean = float(np.mean(document_lengths))
diff --git a/src/seb/interfaces/task.py b/src/seb/interfaces/task.py
index a5de49e9..4b16cab7 100644
--- a/src/seb/interfaces/task.py
+++ b/src/seb/interfaces/task.py
@@ -1,6 +1,6 @@
 from typing import Literal, Protocol, TypedDict, runtime_checkable
 
-from attr import dataclass
+import nummy as np
 
 from seb.interfaces.language import Language
 
@@ -70,9 +70,27 @@ def evaluate(self, model: Encoder) -> TaskResult:
         """
         ...
 
-    def get_descriptive_stats(self) -> DescriptiveDatasetStats:
+    def get_documents(self) -> list[str]:
+        """
+        Get the documents for the task.
+
+        Returns:
+            A list of strings.
+        """
         ...
 
+    def get_descriptive_stats(self) -> DescriptiveDatasetStats:
+        texts = self.get_documents()
+        document_lengths = np.array([len(text) for text in texts])
+
+        mean = float(np.mean(document_lengths))
+        std = float(np.std(document_lengths))
+        return DescriptiveDatasetStats(
+            mean_document_length=mean,
+            std_document_length=std,
+            num_documents=len(document_lengths),
+        )
+
     def name_to_path(self) -> str:
         """
         Convert a name to a path.
diff --git a/src/seb/registered_tasks/multilingual.py b/src/seb/registered_tasks/multilingual.py
index 72d0a2e4..b08d1531 100644
--- a/src/seb/registered_tasks/multilingual.py
+++ b/src/seb/registered_tasks/multilingual.py
@@ -71,7 +71,7 @@ def load_data(self) -> DatasetDict:
 
             return DatasetDict(ds)
 
-        def get_descriptive_stats(self) -> DescriptiveDatasetStats:
+        def get_documents(self) -> list[str]:
             ds = self.load_data()
             texts = []
             splits = self.get_splits()
@@ -80,7 +80,10 @@ def get_descriptive_stats(self) -> DescriptiveDatasetStats:
             for split in splits:
                 for text_column in self._text_columns:
                     texts += ds[split][text_column]
+            return texts
 
+        def get_descriptive_stats(self) -> DescriptiveDatasetStats:
+            texts = self.get_documents()
             document_lengths = np.array([len(text) for text in texts])
 
             mean = np.mean(document_lengths)