Adding training sets

opusparcus.py

@@ -45,7 +45,7 @@ _LICENSE = ""
 
 _URLs = {
 
-    "train": None,
+    "train": None,      # actual value set in the `_split_generators` method
     "validation": "validation.jsonl",
     "test": "test.jsonl"
 
@@ -135,20 +135,15 @@ class Opusparcus(datasets.GeneratorBasedBuilder):
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
 
-        _URLs["train"] = "train_{0}.jsonl.bz2".format(self.config.lang)
+        if self.config.quality > 95:
+            # No training data matches this quality criterion
+            del _URLS["train"]
+        else:
+            _URLs["train"] = "train_{0}.jsonl.bz2".format(self.config.lang)
 
         data_dir = dl_manager.download_and_extract(_URLs)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "lang": self.config.lang,
-                    "quality": self.config.quality,
-                    "filepath": data_dir["train"],
-                    "split": "train",
-                },
-            ),
+
+        splits = [
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
@@ -168,8 +163,23 @@ class Opusparcus(datasets.GeneratorBasedBuilder):
                     "filepath": data_dir["validation"],
                     "split": "validation",
                 },
-            ),
-        ]
+            )
+        ]            
+
+        if self.config.quality <= 95:
+            # We do have training data as well
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "lang": self.config.lang,
+                        "quality": self.config.quality,
+                        "filepath": data_dir["train"],
+                        "split": "train",
+                    },
+                )
+            )
 
     def _generate_examples(
             self, lang, quality, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`