Create germandpr-beir.py

germandpr-beir.py

@@ -0,0 +1,104 @@
+import datasets
+
+
+_VERSION = "1.0.0"
+
+_DESCRIPTION = "Deepset's germanDPR dataset made compatible with BEIR benchmark framework. One version contains " \
+               "the original dataset 1:1 and the other dataset is preprocessed. See official dataset card for " \
+               "usage of dataset with BEIR."
+
+_SUBSETS = ["queries-original", "corpus-original", "queries-processed", "corpus-processed", "qrels"]
+
+
+class GermanDPRBeir(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = (
+        [
+            datasets.BuilderConfig(
+                name="queries-original",
+                description=f"BEIR queries created 1:1 from deepset/germanDPR.",
+                version=_VERSION,
+            ),
+            datasets.BuilderConfig(
+                name="corpus-original",
+                description=f"BEIR corpus created 1:1 from deepset/germanDPR.",
+                version=_VERSION,
+            ),
+            datasets.BuilderConfig(
+                name="queries-processed",
+                description=f"BEIR queries created and further text-processed from deepset/germanDPR.",
+                version=_VERSION,
+            ),
+            datasets.BuilderConfig(
+                name="corpus-processed",
+                description=f"BEIR corpus created and further text-processed from deepset/germanDPR.",
+                version=_VERSION,
+            ),
+            datasets.BuilderConfig(
+                name="qrels",
+                description=f"BEIR qrels created from deepset/germanDPR for train and test split.",
+                version=_VERSION,
+            )
+        ]
+    )
+
+    DEFAULT_CONFIG_NAME = "qrels"
+
+    def _info(self):
+        name = self.config.name
+        _SPLITS = ["queries-original", "corpus-original", "queries-processed", "corpus-processed", "qrels"]
+
+        if name.startswith("queries"):
+            features = {
+                "_id": datasets.Value("string"),
+                "text": datasets.Value("string")
+            }
+        elif name.startswith("corpus"):
+            features = {
+                "_id": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "text": datasets.Value("string"),
+            }
+        else:
+            # name == qrels
+            features = {
+                "query-id": datasets.Value("string"),
+                "corpus-id": datasets.Value("string"),
+                "score": datasets.Value("int32")
+            }
+
+        return datasets.DatasetInfo(
+            description=f"{_DESCRIPTION}\n{self.config.description}",
+            features=datasets.Features(features),
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/PM-AI/germandpr-beir",
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        _SPLITS = ["queries-original", "corpus-original", "queries-processed", "corpus-processed", "qrels"]
+
+        name = self.config.name
+        if name == "qrels":
+            dl_path = dl_manager.download([
+                "https://huggingface.co/datasets/PM-AI/germandpr-beir/resolve/main/data/qrels/test.tsv",
+                "https://huggingface.co/datasets/PM-AI/germandpr-beir/resolve/main/data/qrels/train.tsv"
+            ])
+        else:
+            dl_path = dl_manager.download(f"https://huggingface.co/datasets/PM-AI/germandpr-beir/resolve/main/data/{name}.jsonl")
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path, "split": "train"}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": dl_path, "split": "test"})
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+        name = self.config.name
+        if name.startswith("queries"):
+            yield 0, {"_id": "1", "text": "text"}
+        elif name.startswith("corpus"):
+            yield 0, {"_id": "1", "title": "title", "text": "text"}
+        else:
+            # name == qrels
+            filepath = [x for x in filepath if x.endswith(f"{split}.tsv")]
+            yield 0, {"query-id": "", "corpus-id": "", "score": 1}