a bit of cleaning

configs/arxiv.py

@@ -0,0 +1,35 @@
+from configs.fs import FSConfig
+
+
+class ArxivConfig(FSConfig):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    @property
+    def id_key(self):
+        return "article_id"
+
+    @property
+    def source_key(self):
+        return "article_text"
+
+    @property
+    def target_key(self):
+        return "abstract_text"
+
+    @property
+    def train_file(self):
+        return "train.txt"
+
+    @property
+    def validation_file(self):
+        return "val.txt"
+
+    @property
+    def test_file(self):
+        return "test.txt"
+
+    def process(self, example):
+        example[self.source_key] = " ".join(example[self.source_key])
+        example[self.target_key] = " ".join(example[self.target_key]).replace("<S>", "").replace("</S>", "")
+        del example["labels"]

configs/fs.py

@@ -0,0 +1,59 @@
+from abc import abstractmethod
+
+import datasets
+
+
+class FSConfig(datasets.BuilderConfig):
+    """BuilderConfig for FS."""
+
+    def __init__(self, additional_features, data_url, citation, url, **kwargs):
+        """BuilderConfig for FS.
+        Args:
+          additional_features: `list[string]`, list of the features that will appear in the feature dict
+           additionally to the self.id_key, self.source_key and self.target_key. Should not include "label".
+          data_url: `string`, url to download the zip file from.
+          citation: `string`, citation for the data set.
+          url: `string`, url for information about the data set.
+          label_classes: `list[string]`, the list of classes for the label if the
+            label is present as a string. Non-string labels will be cast to either
+            'False' or 'True'.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(FSConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.features = [self.id_key, self.source_key, self.target_key] + additional_features
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+
+    @property
+    @abstractmethod
+    def id_key(self):
+        pass
+
+    @property
+    @abstractmethod
+    def train_file(self):
+        pass
+
+    @property
+    @abstractmethod
+    def validation_file(self):
+        pass
+
+    @property
+    @abstractmethod
+    def test_file(self):
+        pass
+
+    @property
+    @abstractmethod
+    def source_key(self):
+        pass
+
+    @property
+    @abstractmethod
+    def target_key(self):
+        pass
+
+    def process(self, example):
+        pass

configs/scrolls.py

@@ -0,0 +1,30 @@
+from configs.fs import FSConfig
+
+
+class ScrollsConfig(FSConfig):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    @property
+    def source_key(self):
+        return "input"
+
+    @property
+    def target_key(self):
+        return "output"
+
+    @property
+    def train_file(self):
+        return "train.jsonl"
+
+    @property
+    def validation_file(self):
+        return "validation.jsonl"
+
+    @property
+    def test_file(self):
+        return "test.jsonl"
+
+    @property
+    def id_key(self):
+        return "pid"

fs.py

@@ -1,10 +1,5 @@
-# coding=utf-8
-# Lint as: python3
-"""The SCROLLS benchmark."""
-
 import json
 import os
-from abc import abstractmethod
 
 import datasets
 from citations_and_descriptions import (
@@ -13,131 +8,11 @@ from citations_and_descriptions import (
     _ARXIV_CITATION, _ARXIV_DESCRIPTION,
     _FS_DESCRIPTION, _FS_CITATION,
 )
+from configs.arxiv import ArxivConfig
+from configs.scrolls import ScrollsConfig
 
 
-class FSConfig(datasets.BuilderConfig):
-    """BuilderConfig for FS."""
-
-    def __init__(self, additional_features, data_url, citation, url, **kwargs):
-        """BuilderConfig for FS.
-        Args:
-          additional_features: `list[string]`, list of the features that will appear in the feature dict
-           additionally to the self.id_key, self.source_key and self.target_key. Should not include "label".
-          data_url: `string`, url to download the zip file from.
-          citation: `string`, citation for the data set.
-          url: `string`, url for information about the data set.
-          label_classes: `list[string]`, the list of classes for the label if the
-            label is present as a string. Non-string labels will be cast to either
-            'False' or 'True'.
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(FSConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
-        self.features = [self.id_key, self.source_key, self.target_key] + additional_features
-        self.data_url = data_url
-        self.citation = citation
-        self.url = url
-
-    @property
-    @abstractmethod
-    def id_key(self):
-        pass
-
-    @property
-    @abstractmethod
-    def train_file(self):
-        pass
-
-    @property
-    @abstractmethod
-    def validation_file(self):
-        pass
-
-    @property
-    @abstractmethod
-    def test_file(self):
-        pass
-
-    @property
-    @abstractmethod
-    def source_key(self):
-        pass
-
-    @property
-    @abstractmethod
-    def target_key(self):
-        pass
-
-    def process(self, example):
-        example[self.source_key] = example[self.source_key].strip()
-        example[self.target_key] = example[self.target_key].strip() if example[
-            self.target_key] else None
-
-
-class ScrollsConfig(FSConfig):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    @property
-    def source_key(self):
-        return "input"
-
-    @property
-    def target_key(self):
-        return "output"
-
-    @property
-    def train_file(self):
-        return "train.jsonl"
-
-    @property
-    def validation_file(self):
-        return "validation.jsonl"
-
-    @property
-    def test_file(self):
-        return "test.jsonl"
-
-    @property
-    def id_key(self):
-        return "pid"
-
-
-class ArxivConfig(FSConfig):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    @property
-    def id_key(self):
-        return "article_id"
-
-    @property
-    def source_key(self):
-        return "article_text"
-
-    @property
-    def target_key(self):
-        return "abstract_text"
-
-    @property
-    def train_file(self):
-        return "train.txt"
-
-    @property
-    def validation_file(self):
-        return "val.txt"
-
-    @property
-    def test_file(self):
-        return "test.txt"
-
-    def process(self, example):
-        example[self.source_key] = " ".join(example[self.source_key])
-        example[self.target_key] = " ".join(example[self.target_key]).replace("<S>", "").replace("</S>", "")
-        del example["labels"]
-        super().process(example)
-
-
-class Fs(datasets.GeneratorBasedBuilder):
+class FS(datasets.GeneratorBasedBuilder):
     """The SCROLLS benchmark."""
     DEFAULT_WRITER_BATCH_SIZE = 1000  # because Narrative QA is a rather large dataset
     BUILDER_CONFIGS = [
@@ -201,8 +76,7 @@ class Fs(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "data_file": os.path.join(dl_dir, self.config.test_file) if data_files is None else data_files[
-                        "test"],
+                    "data_file": os.path.join(dl_dir, self.config.test_file),
                 },
             ),
         ]