Delete FromTo.py

FromTo.py

@@ -1,164 +0,0 @@
-# coding=utf-8
-# Copyright 2020 HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-"""The FromTo Emerging Entities Dataset."""
-
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@inproceedings{derczynski-etal-2017-results,
-    title = "Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition",
-    author = "Derczynski, Leon  and
-      Nichols, Eric  and
-      van Erp, Marieke  and
-      Limsopatham, Nut",
-    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
-    month = sep,
-    year = "2017",
-    address = "Copenhagen, Denmark",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/W17-4418",
-    doi = "10.18653/v1/W17-4418",
-    pages = "140--147",
-    abstract = "This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
-                Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization),
-                but recall on them is a real problem in noisy text - even among annotators.
-                This drop tends to be due to novel entities and surface forms.
-                Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'}
-                hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities,
-                and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the
-                ability of participating entries to detect and classify novel and emerging named entities in noisy text.",
-}
-"""
-
-_DESCRIPTION = """\
-WNUT 17: Emerging and Rare entity recognition
-This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
-Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation),
-but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms.
-Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve.
-This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text.
-The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities.
-"""
-
-_URL = "https://raw.githubusercontent.com/stephaneDoss/emerging_entities_fromTo/main/"
-_TRAINING_FILE = "train.conll"
-_DEV_FILE = "dev.conll"
-_TEST_FILE = "test.conll"
-
-
-class FromToConfig(datasets.BuilderConfig):
-    """The FromTo Emerging Entities Dataset."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for FromTo.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(FromToConfig, self).__init__(**kwargs)
-
-
-class FromTo(datasets.GeneratorBasedBuilder):
-    """The FromTo Emerging Entities Dataset."""
-
-    BUILDER_CONFIGS = [
-       FromToConfig(
-            name="FromTo", version=datasets.Version("1.0.0"), description="The FromTo Emerging Entities Dataset"
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                            "O",
-                            "B-depart",
-                            "I-depart",
-                            "B-arrive",
-                            "I-arrive",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="http://noisy-text.github.io/2017/emerging-rare-entities.html",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "train": f"{_URL}{_TRAINING_FILE}",
-            "dev": f"{_URL}{_DEV_FILE}",
-            "test": f"{_URL}{_TRAINING_FILE}",
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        logger.info("⏳ Generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            current_tokens = []
-            current_labels = []
-            sentence_counter = 0
-            for row in f:
-                row = row.rstrip()
-                if row:
-                    token, label = row.split("\t")
-                    current_tokens.append(token)
-                    current_labels.append(label)
-                else:
-                    # New sentence
-                    if not current_tokens:
-                        # Consecutive empty lines will cause empty sentences
-                        continue
-                    assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
-                    sentence = (
-                        sentence_counter,
-                        {
-                            "id": str(sentence_counter),
-                            "tokens": current_tokens,
-                            "ner_tags": current_labels,
-                        },
-                    )
-                    sentence_counter += 1
-                    current_tokens = []
-                    current_labels = []
-                    yield sentence
-            # Don't forget last sentence in dataset 🧐
-            if current_tokens:
-                yield sentence_counter, {
-                    "id": str(sentence_counter),
-                    "tokens": current_tokens,
-                    "ner_tags": current_labels,
-                }