Delete DiscoEval.py

DiscoEval.py

@@ -1,257 +0,0 @@
-# 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.
-
-import os
-import io
-import datasets
-import constants
-import pickle
-import logging
-
-_CITATION = """\
-@InProceedings{mchen-discoeval-19,
-                title = {Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations},
-                author = {Mingda Chen and Zewei Chu and Kevin Gimpel},
-                booktitle = {Proc. of {EMNLP}},
-                year={2019}
-              }
-"""
-
-_DESCRIPTION = """\
-This dataset contains all tasks of the DiscoEval benchmark for sentence representation learning.
-"""
-
-_HOMEPAGE = "https://github.com/ZeweiChu/DiscoEval"
-
-
-class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
-    """DiscoEval Benchmark"""
-    VERSION = datasets.Version("1.1.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name=constants.SPARXIV,
-            version=VERSION,
-            description="Sentence positioning dataset from arXiv",
-        ),
-        datasets.BuilderConfig(
-            name=constants.SPROCSTORY,
-            version=VERSION,
-            description="Sentence positioning dataset from ROCStory",
-        ),
-        datasets.BuilderConfig(
-            name=constants.SPWIKI,
-            version=VERSION,
-            description="Sentence positioning dataset from Wikipedia",
-        ),
-        datasets.BuilderConfig(
-            name=constants.DCCHAT,
-            version=VERSION,
-            description="Discourse Coherence dataset from chat",
-        ),
-        datasets.BuilderConfig(
-            name=constants.DCWIKI,
-            version=VERSION,
-            description="Discourse Coherence dataset from Wikipedia",
-        ),
-        datasets.BuilderConfig(
-            name=constants.RST,
-            version=VERSION,
-            description="The RST Discourse Treebank dataset ",
-        ),
-        datasets.BuilderConfig(
-            name=constants.PDTB_E,
-            version=VERSION,
-            description="The Penn Discourse Treebank - Explicit dataset.",
-        ),
-        datasets.BuilderConfig(
-            name=constants.PDTB_I,
-            version=VERSION,
-            description="The Penn Discourse Treebank - Implicit dataset.",
-        ),
-        datasets.BuilderConfig(
-            name=constants.SSPABS,
-            version=VERSION,
-            description="The SSP dataset.",
-        ),
-        datasets.BuilderConfig(
-            name=constants.BSOARXIV,
-            version=VERSION,
-            description="The BSO Task with the arxiv dataset.",
-        ),
-        datasets.BuilderConfig(
-            name=constants.BSOWIKI,
-            version=VERSION,
-            description="The BSO Task with the wiki dataset.",
-        ),
-        datasets.BuilderConfig(
-            name=constants.BSOROCSTORY,
-            version=VERSION,
-            description="The BSO Task with the rocstory dataset.",
-        ),
-    ]
-
-    def _info(self):
-        if self.config.name in [constants.SPARXIV, constants.SPROCSTORY, constants.SPWIKI]:
-            features_dict = {
-                constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
-                for i in range(constants.SP_TEXT_COLUMNS)
-            }
-            features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.SP_LABELS)
-            features = datasets.Features(features_dict)
-
-        elif self.config.name in [constants.BSOARXIV, constants.BSOWIKI, constants.BSOROCSTORY]:
-            features_dict = {
-                constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
-                for i in range(constants.BSO_TEXT_COLUMNS)
-            }
-            features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.BSO_LABELS)
-            features = datasets.Features(features_dict)
-
-        elif self.config.name in [constants.DCCHAT, constants.DCWIKI]:
-            features_dict = {
-                constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
-                for i in range(constants.DC_TEXT_COLUMNS)
-            }
-            features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.DC_LABELS)
-            features = datasets.Features(features_dict)
-
-        elif self.config.name in [constants.RST]:
-            features_dict = {
-                constants.TEXT_COLUMN_NAME[i]: [datasets.Value('string')]
-                for i in range(constants.RST_TEXT_COLUMNS)
-            }
-            features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.RST_LABELS)
-            features = datasets.Features(features_dict)
-
-        elif self.config.name in [constants.PDTB_E]:
-            features_dict = {
-                constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
-                for i in range(constants.PDTB_E_TEXT_COLUMNS)
-            }
-            features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.PDTB_E_LABELS)
-            features = datasets.Features(features_dict)
-
-        elif self.config.name in [constants.PDTB_I]:
-            features_dict = {
-                constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
-                for i in range(constants.PDTB_I_TEXT_COLUMNS)
-            }
-            features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.PDTB_I_LABELS)
-            features = datasets.Features(features_dict)
-
-        elif self.config.name in [constants.SSPABS]:
-            features_dict = {
-                constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
-                for i in range(constants.SSPABS_TEXT_COLUMNS)
-            }
-            features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.SSPABS_LABELS)
-            features = datasets.Features(features_dict)
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        if self.config.name in [constants.SPARXIV, constants.SPROCSTORY, constants.SPWIKI]:
-            data_dir = constants.SP_DATA_DIR + "/" + constants.SP_DIRS[self.config.name]
-            train_name = constants.SP_TRAIN_NAME
-            valid_name = constants.SP_VALID_NAME
-            test_name = constants.SP_TEST_NAME
-
-        elif self.config.name in [constants.BSOARXIV, constants.BSOWIKI, constants.BSOROCSTORY]:
-            data_dir = constants.BSO_DATA_DIR + "/" + constants.BSO_DIRS[self.config.name]
-            train_name = constants.BSO_TRAIN_NAME
-            valid_name = constants.BSO_VALID_NAME
-            test_name = constants.BSO_TEST_NAME
-
-        elif self.config.name in [constants.DCCHAT, constants.DCWIKI]:
-            data_dir = constants.DC_DATA_DIR + "/" + constants.DC_DIRS[self.config.name]
-            train_name = constants.DC_TRAIN_NAME
-            valid_name = constants.DC_VALID_NAME
-            test_name = constants.DC_TEST_NAME
-
-        elif self.config.name in [constants.RST]:
-            data_dir = constants.RST_DATA_DIR
-            train_name = constants.RST_TRAIN_NAME
-            valid_name = constants.RST_VALID_NAME
-            test_name = constants.RST_TEST_NAME
-
-        elif self.config.name in [constants.PDTB_E, constants.PDTB_I]:
-            data_dir = os.path.join(constants.PDTB_DATA_DIR, constants.PDTB_DIRS[self.config.name])
-            train_name = constants.PDTB_TRAIN_NAME
-            valid_name = constants.PDTB_VALID_NAME
-            test_name = constants.PDTB_TEST_NAME
-
-        elif self.config.name in [constants.SSPABS]:
-            data_dir = constants.SSPABS_DATA_DIR
-            train_name = constants.SSPABS_TRAIN_NAME
-            valid_name = constants.SSPABS_VALID_NAME
-            test_name = constants.SSPABS_TEST_NAME
-
-        urls_to_download = {
-            "train": data_dir + "/" + train_name,
-            "valid": data_dir + "/" + valid_name,
-            "test": data_dir + "/" + test_name,
-        }
-        logger = logging.getLogger(__name__)
-        data_dirs = dl_manager.download_and_extract(urls_to_download)
-        logger.info(f"Data directories: {data_dirs}")
-        downloaded_files = dl_manager.download_and_extract(data_dirs)
-        logger.info(f"Downloading Completed")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": downloaded_files['train'],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": downloaded_files['valid'],
-                    "split": "dev",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": downloaded_files['test'],
-                    "split": "test"
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        logger = logging.getLogger(__name__)
-        logger.info(f"Current working dir: {os.getcwd()}")
-        logger.info("generating examples from = %s", filepath)
-        if self.config.name == constants.RST:
-            data = pickle.load(open(filepath, "rb"))
-            for key, line in enumerate(data):
-                example = {constants.TEXT_COLUMN_NAME[i]: sent for i, sent in enumerate(line[1:])}
-                example[constants.LABEL_NAME] = line[0]
-                yield key, example
-
-        else:
-            with io.open(filepath, mode='r', encoding='utf-8') as f:
-                for key, line in enumerate(f):
-                    line = line.strip().split("\t")
-                    example = {constants.TEXT_COLUMN_NAME[i]: sent for i, sent in enumerate(line[1:])}
-                    example[constants.LABEL_NAME] = line[0]
-                    yield key, example