Upload PET.py

PET.py

@@ -1,4 +1,13 @@
-# TO CREATE dataset_infos.json use: datasets-cli test PET --save_infos --all_configs
+# https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html
+# TO login
+# TO CREATE dataset_infos.json use:
+#       $ datasets-cli test PET --save_infos --all_configs
+#
+# DO
+#       $ huggingface-cli login
+# then.
+# in pytohn:
+#       dataset.push_to_hub(patriziobellan/PET) to set the preview on the web interface
 #
 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
 #
@@ -55,7 +64,35 @@ _URL = "https://pdi.fbk.eu/pet/PETHuggingFace/"
 # _TRAINING_FILE = "train.json"
 # _DEV_FILE = "dev.json"
 _TEST_FILE = "test.json"
-
+_TEST_FILE_RELATIONS = 'PETrelations.json'
+
+_NER = 'token-classification'
+_RELATIONS_EXTRACTION = 'relations-extraction'
+_NER_TAGS = [ "O",
+            "B-Actor",
+            "I-Actor",
+            "B-Activity",
+            "I-Activity",
+            "B-Activity Data",
+            "I-Activity Data",
+            "B-Further Specification",
+            "I-Further Specification",
+            "B-XOR Gateway",
+            "I-XOR Gateway",
+            "B-Condition Specification",
+            "I-Condition Specification",
+            "B-AND Gateway",
+            "I-AND Gateway"]
+
+_STR_PET = """\n
+ _______ _     _ _______       _____  _______ _______      ______  _______ _______ _______ _______ _______ _______
+    |    |_____| |______      |_____] |______    |         |     \ |_____|    |    |_____| |______ |______    |   
+    |    |     | |______      |       |______    |         |_____/ |     |    |    |     | ______| |______    |   
+                                                                                                                  
+\n\n\n
+
+Discover more at: [https://pdi.fbk.eu/pet-dataset/]
+"""
 
 
 class PETConfig(datasets.BuilderConfig):
@@ -70,105 +107,114 @@ class PETConfig(datasets.BuilderConfig):
 
 class PET(datasets.GeneratorBasedBuilder):
     """PET DATASET."""
-    BUILDER_CONFIGS = [
-        PETConfig(
-            name="PET", version=datasets.Version("1.0.0"), description="The PET Dataset"
-        ),
-    ]
+
+    features_ner = {
+            "document name": datasets.Value("string"),
+            "sentence-ID": datasets.Value("int8"),
+            "tokens": datasets.Sequence(datasets.Value("string")),
+            "ner-tags": datasets.Sequence(datasets.features.ClassLabel(names=_NER_TAGS)),
+        }
+
+    features_relations = datasets.Sequence(
+        datasets.Features(
+
+        {
+            'source-head-sentence-ID': datasets.Value("int8"),
+            'source-head-word-ID': datasets.Value("int8"),
+            'relation-type': datasets.Value("string"),
+            'target-head-sentence-ID': datasets.Value("int8"),
+            'target-head-word-ID' : datasets.Value("int8"),
+        }
+    ))
+    BUILDER_CONFIGS = [ PETConfig(
+                            name=_NER,
+                            version=datasets.Version("1.0.1"),
+                            description="The PET Dataset for Token Classification"
+                            ),
+                        PETConfig(
+                            name=_RELATIONS_EXTRACTION,
+                            version=datasets.Version("1.0.1"),
+                            description="The PET Dataset for Relation Extraction"
+                            ),
+                        ]
+
+    DEFAULT_CONFIG_NAME = _RELATIONS_EXTRACTION
 
     def _info(self):
-        features = datasets.Features(
+        print(_STR_PET)
+        if self.config.name == _NER:
+            features = datasets.Features(self.features_ner)
+        else:
+            features = datasets.Features(
                 {
-                    "document name": datasets.Value("string"),
-                    "sentence-ID": datasets.Value("int8"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "ner-tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-Actor",
-                                "I-Actor",
-                                "B-Activity",
-                                "I-Activity",
-                                "B-Activity Data",
-                                "I-Activity Data",
-                                "B-Further Specification",
-                                "I-Further Specification",
-                                "B-XOR Gateway",
-                                "I-XOR Gateway",
-                                "B-Condition Specification",
-                                "I-Condition Specification",
-                                "B-AND Gateway",
-                                "I-AND Gateway",
-                            ]
-                        )
-                    ),
+                "document name": datasets.Value("string"),
+                'tokens':datasets.Sequence(datasets.Value("string")),
+                'tokens-IDs':datasets.Sequence(datasets.Value("int8")),
+                'ner_tags': datasets.Sequence(datasets.Value("string")),
+                'sentence-IDs':datasets.Sequence(datasets.Value("int8")),
+                "relations": self.features_relations
                 }
             )
-
+        # print(features)
         return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
+            features=datasets.Features(features),
             homepage=_HOMEPAGE,
-            # License for the dataset if available
             license=_LICENSE,
-            # Citation for the dataset
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
-        urls_to_download = {
-            # "train": f"{_URL}{_TRAINING_FILE}",
-            # "dev": f"{_URL}{_DEV_FILE}",
-            "test": f"{_URL}{_TEST_FILE}",
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.TRAIN,
-            #     # These kwargs will be passed to _generate_examples
-            #     gen_kwargs={
-            #         "filepath": downloaded_files["train"],
-            #         "split": "train",
-            #     },
-            # ),
-            datasets.SplitGenerator(
+        if self.config.name == _NER:
+            urls_to_download = {
+                # "train": f"{_URL}{_TRAINING_FILE}",
+                # "dev": f"{_URL}{_DEV_FILE}",
+                "test": f"{_URL}{_TEST_FILE}",
+            }
+            downloaded_files = dl_manager.download_and_extract(urls_to_download)
+            return [datasets.SplitGenerator(
+                        name=datasets.Split.TEST,
+                        # These kwargs will be passed to _generate_examples
+                        gen_kwargs={
+                            "filepath": downloaded_files["test"],
+                            "split": "test"
+                        },
+                    )]
+        else:
+            urls_to_download = {
+                # "train": f"{_URL}{_TRAINING_FILE}",
+                # "dev": f"{_URL}{_DEV_FILE}",
+                "test": f"{_URL}{_TEST_FILE_RELATIONS}",
+            }
+            downloaded_files = dl_manager.download_and_extract(urls_to_download)
+            return [datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "filepath": downloaded_files["test"],
                     "split": "test"
                 },
-            ),
-            #
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.VALIDATION,
-            #     # These kwargs will be passed to _generate_examples
-            #     gen_kwargs={
-            #         "filepath": os.path.join(data_dir, "dev.jsonl"),
-            #         "split": "dev",
-            #     },
-            # ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+            )]
+
     def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-
-        with open(filepath, encoding="utf-8", mode='r') as f:
-            for key, row in enumerate(f):
-                row = json.loads(row)
-                yield key, {
-                            "document name": row["document name"],
-                            "sentence-ID": row["sentence-ID"],
-                            "tokens": row["tokens"],
-                            "ner-tags": row["ner-tags"]
-                            }
+        if self.config.name == _NER:
+            with open(filepath, encoding="utf-8", mode='r') as f:
+                for key, row in enumerate(f):
+                    row = json.loads(row)
+                    yield key, {
+                                "document name": row["document name"],
+                                "sentence-ID": row["sentence-ID"],
+                                "tokens": row["tokens"],
+                                "ner-tags": row["ner-tags"]
+                                }
+        else:
+            with open(filepath, encoding="utf-8", mode='r') as f:
+                for key, row in enumerate(json.load(f)):
+                    yield key, {"document name": row["document name"],  # datasets.Value("string"),
+                                'tokens': row["tokens"],  # sentences['tokens'],
+                                'tokens-IDs': row["tokens-IDs"],
+                                'ner_tags': row["ner_tags"],
+                                'sentence-IDs': row["sentence-IDs"],  # sentences['sentence-IDs'],
+
+                                "relations": row["relations"]
+                                }