Release: 1.18.1

Commit from https://github.com/huggingface/datasets/commit/218e496519ff14b4bc69ea559616af6f2ef89e57

lama.py

@@ -1,350 +1,350 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The LAMA Dataset"""
-
-
-import json
-from fnmatch import fnmatch
-
-import datasets
-
-
-_CITATION = """@inproceedings{petroni2019language,
-  title={Language Models as Knowledge Bases?},
-  author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
-  booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
-  year={2019}
-}
-@inproceedings{petroni2020how,
-  title={How Context Affects Language Models' Factual Predictions},
-  author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
-  booktitle={Automated Knowledge Base Construction},
-  year={2020},
-  url={https://openreview.net/forum?id=025X0zPfn}
-}
-"""
-
-
-_DESCRIPTION = """LAMA is a dataset used to probe and analyze the factual and commonsense knowledge contained in pretrained language models. See https://github.com/facebookresearch/LAMA.
-"""
-
-_HOMEPAGE = "https://github.com/facebookresearch/LAMA"
-
-_LICENSE = "The Creative Commons Attribution-Noncommercial 4.0 International License. see https://github.com/facebookresearch/LAMA/blob/master/LICENSE"
-
-_RELATIONS_URL = "https://s3.amazonaws.com/datasets.huggingface.co/lama/relations.jsonl"
-
-_DATA_URL = "https://dl.fbaipublicfiles.com/LAMA/negated_data.tar.gz"
-
-
-class Lama(datasets.GeneratorBasedBuilder):
-    """Lama Dataset"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="trex", version=VERSION, description="The TRex part of the Lama dataset"),
-        datasets.BuilderConfig(name="squad", version=VERSION, description="The Squad part of the Lama dataset"),
-        datasets.BuilderConfig(
-            name="google_re", version=VERSION, description="The Google_re part of the Lama dataset"
-        ),
-        datasets.BuilderConfig(
-            name="conceptnet", version=VERSION, description="The Conceptnet part of the Lama dataset"
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "trex"
-
-    def _info(self):
-        if self.config.name == "trex":
-            features = datasets.Features(
-                {
-                    "uuid": datasets.Value("string"),
-                    "obj_uri": datasets.Value("string"),
-                    "obj_label": datasets.Value("string"),
-                    "sub_uri": datasets.Value("string"),
-                    "sub_label": datasets.Value("string"),
-                    "predicate_id": datasets.Value("string"),
-                    "sub_surface": datasets.Value("string"),
-                    "obj_surface": datasets.Value("string"),
-                    "masked_sentence": datasets.Value("string"),
-                    "template": datasets.Value("string"),
-                    "template_negated": datasets.Value("string"),
-                    "label": datasets.Value("string"),
-                    "description": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                }
-            )
-            return datasets.DatasetInfo(
-                description=_DESCRIPTION,
-                features=features,
-                supervised_keys=None,
-                homepage=_HOMEPAGE,
-                license=_LICENSE,
-                citation=_CITATION,
-            )
-        elif self.config.name == "conceptnet":
-            features = datasets.Features(
-                {
-                    "uuid": datasets.Value("string"),
-                    "sub": datasets.Value("string"),
-                    "obj": datasets.Value("string"),
-                    "pred": datasets.Value("string"),
-                    "obj_label": datasets.Value("string"),
-                    "masked_sentence": datasets.Value("string"),
-                    "negated": datasets.Value("string"),
-                }
-            )
-            return datasets.DatasetInfo(
-                description=_DESCRIPTION,
-                features=features,
-                supervised_keys=None,
-                homepage=_HOMEPAGE,
-                license=_LICENSE,
-                citation=_CITATION,
-            )
-        elif self.config.name == "squad":
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "sub_label": datasets.Value("string"),
-                    "obj_label": datasets.Value("string"),
-                    "negated": datasets.Value("string"),
-                    "masked_sentence": datasets.Value("string"),
-                }
-            )
-            return datasets.DatasetInfo(
-                description=_DESCRIPTION,
-                features=features,
-                supervised_keys=None,
-                homepage=_HOMEPAGE,
-                license=_LICENSE,
-                citation=_CITATION,
-            )
-        elif self.config.name == "google_re":
-            features = datasets.Features(
-                {
-                    "pred": datasets.Value("string"),
-                    "sub": datasets.Value("string"),
-                    "obj": datasets.Value("string"),
-                    "evidences": datasets.Value("string"),
-                    "judgments": datasets.Value("string"),
-                    "sub_w": datasets.Value("string"),
-                    "sub_label": datasets.Value("string"),
-                    "sub_aliases": datasets.Value("string"),
-                    "obj_w": datasets.Value("string"),
-                    "obj_label": datasets.Value("string"),
-                    "obj_aliases": datasets.Value("string"),
-                    "uuid": datasets.Value("string"),
-                    "masked_sentence": datasets.Value("string"),
-                    "template": datasets.Value("string"),
-                    "template_negated": datasets.Value("string"),
-                }
-            )
-            return datasets.DatasetInfo(
-                description=_DESCRIPTION,
-                features=features,
-                supervised_keys=None,
-                homepage=_HOMEPAGE,
-                license=_LICENSE,
-                citation=_CITATION,
-            )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        archive = dl_manager.download(_DATA_URL)
-        if self.config.name == "trex":
-            relations_path = dl_manager.download(_RELATIONS_URL)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepaths": ["TREx/*"],
-                        "files": dl_manager.iter_archive(archive),
-                        "relations_path": relations_path,
-                    },
-                ),
-            ]
-        elif self.config.name == "google_re":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepaths": [
-                            "Google_RE/date_of_birth_test.jsonl",
-                            "Google_RE/place_of_birth_test.jsonl",
-                            "Google_RE/place_of_death_test.jsonl",
-                        ],
-                        "files": dl_manager.iter_archive(archive),
-                    },
-                ),
-            ]
-        elif self.config.name == "conceptnet":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepaths": ["ConceptNet/test.jsonl"],
-                        "files": dl_manager.iter_archive(archive),
-                    },
-                ),
-            ]
-        elif self.config.name == "squad":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepaths": ["Squad/test.jsonl"],
-                        "files": dl_manager.iter_archive(archive),
-                    },
-                ),
-            ]
-
-    def _generate_examples(self, filepaths, files, relations_path=None):
-        """Yields examples from the LAMA dataset."""
-        filepaths = list(filepaths)
-        if self.config.name == "trex":
-            all_rels = {}
-            with open(relations_path, encoding="utf-8") as f:
-                for row in f:
-                    data = json.loads(row)
-                    all_rels[data["relation"]] = data
-            id_ = -1
-            inside_trec_directory = False
-            for path, f in files:
-                if any(fnmatch(path, pattern) for pattern in filepaths):
-                    inside_trec_directory = True
-                    for row in f:
-                        data = json.loads(row)
-                        pred = all_rels.get(data["predicate_id"], {})
-                        for evidences in data["evidences"]:
-                            id_ += 1
-                            yield id_, {
-                                "uuid": str(data["uuid"]),
-                                "obj_uri": str(data["obj_uri"]),
-                                "obj_label": str(data["obj_label"]),
-                                "sub_uri": str(data["sub_uri"]),
-                                "sub_label": str(data["sub_label"]),
-                                "predicate_id": str(data["predicate_id"]),
-                                "sub_surface": str(evidences["sub_surface"]),
-                                "obj_surface": str(evidences["obj_surface"]),
-                                "masked_sentence": str(evidences["masked_sentence"]),
-                                "template": str(pred.get("template", "")),
-                                "template_negated": str(pred.get("template_negated", "")),
-                                "label": str(pred.get("label", "")),
-                                "description": str(pred.get("description", "")),
-                                "type": str(pred.get("type", "")),
-                            }
-                elif inside_trec_directory:
-                    break
-        elif self.config.name == "conceptnet":
-            id_ = -1
-            for path, f in files:
-                if not filepaths:
-                    break
-                if path in list(filepaths):
-                    for row in f:
-                        data = json.loads(row)
-                        if data.get("negated") is not None:
-                            for masked_sentence, negated in zip(data["masked_sentences"], data["negated"]):
-                                id_ += 1
-                                yield id_, {
-                                    "uuid": str(data["uuid"]),
-                                    "sub": str(data.get("sub", "")),
-                                    "obj": str(data.get("obj", "")),
-                                    "pred": str(data["pred"]),
-                                    "obj_label": str(data["obj_label"]),
-                                    "masked_sentence": str(masked_sentence),
-                                    "negated": str(negated),
-                                }
-                        else:
-                            for masked_sentence in data["masked_sentences"]:
-                                id_ += 1
-                                yield id_, {
-                                    "uuid": str(data["uuid"]),
-                                    "sub": str(data.get("sub", "")),
-                                    "obj": str(data.get("obj", "")),
-                                    "pred": str(data["pred"]),
-                                    "obj_label": str(data["obj_label"]),
-                                    "masked_sentence": str(masked_sentence),
-                                    "negated": str(""),
-                                }
-                    filepaths.remove(path)
-        elif self.config.name == "squad":
-            id_ = -1
-            for path, f in files:
-                if not filepaths:
-                    break
-                if path in filepaths:
-                    for row in f:
-                        data = json.loads(row)
-                        for masked_sentence in data["masked_sentences"]:
-                            id_ += 1
-                            yield id_, {
-                                "id": str(data["id"]),
-                                "sub_label": str(data["sub_label"]),
-                                "obj_label": str(data["obj_label"]),
-                                "negated": str(data.get("negated", "")),
-                                "masked_sentence": str(masked_sentence),
-                            }
-                    filepaths.remove(path)
-        elif self.config.name == "google_re":
-            id_ = -1
-            for path, f in files:
-                if path in filepaths:
-                    if not filepaths:
-                        break
-                    if path in filepaths:
-                        # from https://github.com/facebookresearch/LAMA/blob/master/scripts/run_experiments.py
-                        if "place_of_birth" in path:
-                            pred = {
-                                "relation": "place_of_birth",
-                                "template": "[X] was born in [Y] .",
-                                "template_negated": "[X] was not born in [Y] .",
-                            }
-                        elif "date_of_birth" in path:
-                            pred = {
-                                "relation": "date_of_birth",
-                                "template": "[X] (born [Y]).",
-                                "template_negated": "[X] (not born [Y]).",
-                            }
-                        else:
-                            pred = {
-                                "relation": "place_of_death",
-                                "template": "[X] died in [Y] .",
-                                "template_negated": "[X] did not die in [Y] .",
-                            }
-                        for row in f:
-                            data = json.loads(row)
-                            for masked_sentence in data["masked_sentences"]:
-                                id_ += 1
-                                yield id_, {
-                                    "pred": str(data["pred"]),
-                                    "sub": str(data["sub"]),
-                                    "obj": str(data["obj"]),
-                                    "evidences": str(data["evidences"]),
-                                    "judgments": str(data["judgments"]),
-                                    "sub_w": str(data["sub_w"]),
-                                    "sub_label": str(data["sub_label"]),
-                                    "sub_aliases": str(data["sub_aliases"]),
-                                    "obj_w": str(data["obj_w"]),
-                                    "obj_label": str(data["obj_label"]),
-                                    "obj_aliases": str(data["obj_aliases"]),
-                                    "uuid": str(data["uuid"]),
-                                    "masked_sentence": str(masked_sentence),
-                                    "template": str(pred["template"]),
-                                    "template_negated": str(pred["template_negated"]),
-                                }
-                        filepaths.remove(path)
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""The LAMA Dataset"""
+
+
+import json
+from fnmatch import fnmatch
+
+import datasets
+
+
+_CITATION = """@inproceedings{petroni2019language,
+  title={Language Models as Knowledge Bases?},
+  author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
+  booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
+  year={2019}
+}
+@inproceedings{petroni2020how,
+  title={How Context Affects Language Models' Factual Predictions},
+  author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
+  booktitle={Automated Knowledge Base Construction},
+  year={2020},
+  url={https://openreview.net/forum?id=025X0zPfn}
+}
+"""
+
+
+_DESCRIPTION = """LAMA is a dataset used to probe and analyze the factual and commonsense knowledge contained in pretrained language models. See https://github.com/facebookresearch/LAMA.
+"""
+
+_HOMEPAGE = "https://github.com/facebookresearch/LAMA"
+
+_LICENSE = "The Creative Commons Attribution-Noncommercial 4.0 International License. see https://github.com/facebookresearch/LAMA/blob/master/LICENSE"
+
+_RELATIONS_URL = "https://s3.amazonaws.com/datasets.huggingface.co/lama/relations.jsonl"
+
+_DATA_URL = "https://dl.fbaipublicfiles.com/LAMA/negated_data.tar.gz"
+
+
+class Lama(datasets.GeneratorBasedBuilder):
+    """Lama Dataset"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="trex", version=VERSION, description="The TRex part of the Lama dataset"),
+        datasets.BuilderConfig(name="squad", version=VERSION, description="The Squad part of the Lama dataset"),
+        datasets.BuilderConfig(
+            name="google_re", version=VERSION, description="The Google_re part of the Lama dataset"
+        ),
+        datasets.BuilderConfig(
+            name="conceptnet", version=VERSION, description="The Conceptnet part of the Lama dataset"
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "trex"
+
+    def _info(self):
+        if self.config.name == "trex":
+            features = datasets.Features(
+                {
+                    "uuid": datasets.Value("string"),
+                    "obj_uri": datasets.Value("string"),
+                    "obj_label": datasets.Value("string"),
+                    "sub_uri": datasets.Value("string"),
+                    "sub_label": datasets.Value("string"),
+                    "predicate_id": datasets.Value("string"),
+                    "sub_surface": datasets.Value("string"),
+                    "obj_surface": datasets.Value("string"),
+                    "masked_sentence": datasets.Value("string"),
+                    "template": datasets.Value("string"),
+                    "template_negated": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+                    "description": datasets.Value("string"),
+                    "type": datasets.Value("string"),
+                }
+            )
+            return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=features,
+                supervised_keys=None,
+                homepage=_HOMEPAGE,
+                license=_LICENSE,
+                citation=_CITATION,
+            )
+        elif self.config.name == "conceptnet":
+            features = datasets.Features(
+                {
+                    "uuid": datasets.Value("string"),
+                    "sub": datasets.Value("string"),
+                    "obj": datasets.Value("string"),
+                    "pred": datasets.Value("string"),
+                    "obj_label": datasets.Value("string"),
+                    "masked_sentence": datasets.Value("string"),
+                    "negated": datasets.Value("string"),
+                }
+            )
+            return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=features,
+                supervised_keys=None,
+                homepage=_HOMEPAGE,
+                license=_LICENSE,
+                citation=_CITATION,
+            )
+        elif self.config.name == "squad":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "sub_label": datasets.Value("string"),
+                    "obj_label": datasets.Value("string"),
+                    "negated": datasets.Value("string"),
+                    "masked_sentence": datasets.Value("string"),
+                }
+            )
+            return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=features,
+                supervised_keys=None,
+                homepage=_HOMEPAGE,
+                license=_LICENSE,
+                citation=_CITATION,
+            )
+        elif self.config.name == "google_re":
+            features = datasets.Features(
+                {
+                    "pred": datasets.Value("string"),
+                    "sub": datasets.Value("string"),
+                    "obj": datasets.Value("string"),
+                    "evidences": datasets.Value("string"),
+                    "judgments": datasets.Value("string"),
+                    "sub_w": datasets.Value("string"),
+                    "sub_label": datasets.Value("string"),
+                    "sub_aliases": datasets.Value("string"),
+                    "obj_w": datasets.Value("string"),
+                    "obj_label": datasets.Value("string"),
+                    "obj_aliases": datasets.Value("string"),
+                    "uuid": datasets.Value("string"),
+                    "masked_sentence": datasets.Value("string"),
+                    "template": datasets.Value("string"),
+                    "template_negated": datasets.Value("string"),
+                }
+            )
+            return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=features,
+                supervised_keys=None,
+                homepage=_HOMEPAGE,
+                license=_LICENSE,
+                citation=_CITATION,
+            )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        archive = dl_manager.download(_DATA_URL)
+        if self.config.name == "trex":
+            relations_path = dl_manager.download(_RELATIONS_URL)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepaths": ["TREx/*"],
+                        "files": dl_manager.iter_archive(archive),
+                        "relations_path": relations_path,
+                    },
+                ),
+            ]
+        elif self.config.name == "google_re":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepaths": [
+                            "Google_RE/date_of_birth_test.jsonl",
+                            "Google_RE/place_of_birth_test.jsonl",
+                            "Google_RE/place_of_death_test.jsonl",
+                        ],
+                        "files": dl_manager.iter_archive(archive),
+                    },
+                ),
+            ]
+        elif self.config.name == "conceptnet":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepaths": ["ConceptNet/test.jsonl"],
+                        "files": dl_manager.iter_archive(archive),
+                    },
+                ),
+            ]
+        elif self.config.name == "squad":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepaths": ["Squad/test.jsonl"],
+                        "files": dl_manager.iter_archive(archive),
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, filepaths, files, relations_path=None):
+        """Yields examples from the LAMA dataset."""
+        filepaths = list(filepaths)
+        if self.config.name == "trex":
+            all_rels = {}
+            with open(relations_path, encoding="utf-8") as f:
+                for row in f:
+                    data = json.loads(row)
+                    all_rels[data["relation"]] = data
+            id_ = -1
+            inside_trec_directory = False
+            for path, f in files:
+                if any(fnmatch(path, pattern) for pattern in filepaths):
+                    inside_trec_directory = True
+                    for row in f:
+                        data = json.loads(row)
+                        pred = all_rels.get(data["predicate_id"], {})
+                        for evidences in data["evidences"]:
+                            id_ += 1
+                            yield id_, {
+                                "uuid": str(data["uuid"]),
+                                "obj_uri": str(data["obj_uri"]),
+                                "obj_label": str(data["obj_label"]),
+                                "sub_uri": str(data["sub_uri"]),
+                                "sub_label": str(data["sub_label"]),
+                                "predicate_id": str(data["predicate_id"]),
+                                "sub_surface": str(evidences["sub_surface"]),
+                                "obj_surface": str(evidences["obj_surface"]),
+                                "masked_sentence": str(evidences["masked_sentence"]),
+                                "template": str(pred.get("template", "")),
+                                "template_negated": str(pred.get("template_negated", "")),
+                                "label": str(pred.get("label", "")),
+                                "description": str(pred.get("description", "")),
+                                "type": str(pred.get("type", "")),
+                            }
+                elif inside_trec_directory:
+                    break
+        elif self.config.name == "conceptnet":
+            id_ = -1
+            for path, f in files:
+                if not filepaths:
+                    break
+                if path in list(filepaths):
+                    for row in f:
+                        data = json.loads(row)
+                        if data.get("negated") is not None:
+                            for masked_sentence, negated in zip(data["masked_sentences"], data["negated"]):
+                                id_ += 1
+                                yield id_, {
+                                    "uuid": str(data["uuid"]),
+                                    "sub": str(data.get("sub", "")),
+                                    "obj": str(data.get("obj", "")),
+                                    "pred": str(data["pred"]),
+                                    "obj_label": str(data["obj_label"]),
+                                    "masked_sentence": str(masked_sentence),
+                                    "negated": str(negated),
+                                }
+                        else:
+                            for masked_sentence in data["masked_sentences"]:
+                                id_ += 1
+                                yield id_, {
+                                    "uuid": str(data["uuid"]),
+                                    "sub": str(data.get("sub", "")),
+                                    "obj": str(data.get("obj", "")),
+                                    "pred": str(data["pred"]),
+                                    "obj_label": str(data["obj_label"]),
+                                    "masked_sentence": str(masked_sentence),
+                                    "negated": str(""),
+                                }
+                    filepaths.remove(path)
+        elif self.config.name == "squad":
+            id_ = -1
+            for path, f in files:
+                if not filepaths:
+                    break
+                if path in filepaths:
+                    for row in f:
+                        data = json.loads(row)
+                        for masked_sentence in data["masked_sentences"]:
+                            id_ += 1
+                            yield id_, {
+                                "id": str(data["id"]),
+                                "sub_label": str(data["sub_label"]),
+                                "obj_label": str(data["obj_label"]),
+                                "negated": str(data.get("negated", "")),
+                                "masked_sentence": str(masked_sentence),
+                            }
+                    filepaths.remove(path)
+        elif self.config.name == "google_re":
+            id_ = -1
+            for path, f in files:
+                if path in filepaths:
+                    if not filepaths:
+                        break
+                    if path in filepaths:
+                        # from https://github.com/facebookresearch/LAMA/blob/master/scripts/run_experiments.py
+                        if "place_of_birth" in path:
+                            pred = {
+                                "relation": "place_of_birth",
+                                "template": "[X] was born in [Y] .",
+                                "template_negated": "[X] was not born in [Y] .",
+                            }
+                        elif "date_of_birth" in path:
+                            pred = {
+                                "relation": "date_of_birth",
+                                "template": "[X] (born [Y]).",
+                                "template_negated": "[X] (not born [Y]).",
+                            }
+                        else:
+                            pred = {
+                                "relation": "place_of_death",
+                                "template": "[X] died in [Y] .",
+                                "template_negated": "[X] did not die in [Y] .",
+                            }
+                        for row in f:
+                            data = json.loads(row)
+                            for masked_sentence in data["masked_sentences"]:
+                                id_ += 1
+                                yield id_, {
+                                    "pred": str(data["pred"]),
+                                    "sub": str(data["sub"]),
+                                    "obj": str(data["obj"]),
+                                    "evidences": str(data["evidences"]),
+                                    "judgments": str(data["judgments"]),
+                                    "sub_w": str(data["sub_w"]),
+                                    "sub_label": str(data["sub_label"]),
+                                    "sub_aliases": str(data["sub_aliases"]),
+                                    "obj_w": str(data["obj_w"]),
+                                    "obj_label": str(data["obj_label"]),
+                                    "obj_aliases": str(data["obj_aliases"]),
+                                    "uuid": str(data["uuid"]),
+                                    "masked_sentence": str(masked_sentence),
+                                    "template": str(pred["template"]),
+                                    "template_negated": str(pred["template_negated"]),
+                                }
+                        filepaths.remove(path)