Delete PET.py

PET.py

@@ -1,170 +0,0 @@
-# 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"]
-                            }