Upload PET.py

PET.py

@@ -0,0 +1,170 @@
+# TO CREATE dataset_infos.json use: datasets-cli test PET --save_infos --all_configs
+#
+# 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URL = "https://pdi.fbk.eu/pet/PETHuggingFace/"
+# _TRAINING_FILE = "train.json"
+# _DEV_FILE = "emerging.dev.conll"
+_TEST_FILE = "test.json"
+
+
+
+class PETConfig(datasets.BuilderConfig):
+    """The WNUT 17 Emerging Entities Dataset."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for PET.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(PETConfig, self).__init__(**kwargs)
+
+class PET(datasets.GeneratorBasedBuilder):
+    """PET DATASET."""
+    BUILDER_CONFIGS = [
+        PETConfig(
+            name="PET", version=datasets.Version("1.0.0"), description="The PET Dataset"
+        ),
+    ]
+
+    def _info(self):
+        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",
+                            ]
+                        )
+                    ),
+                }
+            )
+
+        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
+            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"], #'/Users/patrizio/Documents/PhD/PythonProjects/HuggingFacePETtest/PET/train.json', #os.path.join(data_dir, "train.json"),
+            #         "split": "train",
+            #     },
+            # ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": downloaded_files["test"], #'/Users/patrizio/Documents/PhD/PythonProjects/HuggingFacePETtest/PET/test.json', #os.path.join(data_dir, "test.json"),
+                    "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"]
+                            }