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DiscoEval.py

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+# 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
+
+_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"
+
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+# _URLS = {
+#     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+#     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+# }
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+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.",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = constants.SPARXIV  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+
+        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 + 1)
+            }
+            features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.SP_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 + 1)
+            }
+            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 + 1)
+            }
+            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 + 1)
+            }
+            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 + 1)
+            }
+            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 + 1)
+            }
+            features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.SSPABS_LABELS)
+            features = datasets.Features(features_dict)
+
+        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "option2": datasets.Value("string"),
+                    "second_domain_answer": datasets.Value("string")
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        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,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+
+        # urls = _URLS[self.config.name]
+        # data_dir = dl_manager.download_and_extract(urls)
+        if self.config.name in [constants.SPARXIV, constants.SPROCSTORY, constants.SPWIKI]:
+            data_dir = os.path.join(constants.SP_DATA_DIR, 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.DCCHAT, constants.DCWIKI]:
+            data_dir = os.path.join(constants.DC_DATA_DIR, 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]:
+            data_dir = os.path.join(constants.PDTB_DATA_DIR, constants.PDTB_E)
+            train_name = constants.PDTB_TRAIN_NAME
+            valid_name = constants.PDTB_VALID_NAME
+            test_name = constants.PDTB_TEST_NAME
+
+        elif self.config.name in [constants.PDTB_I]:
+            data_dir = os.path.join(constants.PDTB_DATA_DIR, constants.PDTB_I)
+            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
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, train_name),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, valid_name),
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, test_name),
+                    "split": "test"
+                },
+            ),
+        ]
+
+    # 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.
+        if self.config.name in [constants.SPARXIV, constants.SPROCSTORY, constants.SPWIKI,
+                                constants.DCWIKI, constants.DCCHAT,
+                                constants.PDTB_E, constants.PDTB_I,
+                                constants.SSPABS]:
+            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
+
+        elif self.config.name in [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
+
+        # TODO: implement other datasets
+        else:
+            yield 0, {
+                "sentence": 'example sentences',
+                "option2": 'another example sentence',
+                "second_domain_answer": "" if split == "test" else 'label',
+            }
+
+
+if __name__ == '__main__':
+    data = pickle.load(open(r'/data/RST/RST_TRAIN.pkl', "rb"))
+    sents = []
+    labels = []
+    for d in data:
+        input1= d[1]
+        input2 = d[2]
+        label = d[0]
+    ofek = 5

constants.py

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+# General Constants:
+LABEL_NAME = 'label'
+TEXT_COLUMN_NAME = [f"sentence_{i}" for i in range(1, 10)]
+
+# SSPabs Constants:
+SSPABS = 'SSPabs'
+SSPABS_TRAIN_NAME = 'train.txt'
+SSPABS_VALID_NAME = 'valid.txt'
+SSPABS_TEST_NAME = 'test.txt'
+SSPABS_DATA_DIR = './data/SSP/abs/'
+SSPABS_LABELS = ["0", "1"]
+SSPABS_TEXT_COLUMNS = 1
+
+# PDTB Constants:
+PDTB_I = 'PDTB-I'
+PDTB_E = 'PDTB-E'
+PDTB_TRAIN_NAME = 'train.txt'
+PDTB_VALID_NAME = 'valid.txt'
+PDTB_TEST_NAME = 'test.txt'
+PDTB_DATA_DIR = './data/PDTB/'
+PDTB_E_LABELS = [
+    'Comparison.Concession',
+    'Comparison.Contrast',
+    'Contingency.Cause',
+    'Contingency.Condition',
+    'Contingency.Pragmatic condition',
+    'Expansion.Alternative',
+    'Expansion.Conjunction',
+    'Expansion.Instantiation',
+    'Expansion.List',
+    'Expansion.Restatement',
+    'Temporal.Asynchronous',
+    'Temporal.Synchrony',
+]
+PDTB_I_LABELS = [
+    'Comparison.Concession',
+    'Comparison.Contrast',
+    'Contingency.Cause',
+    'Contingency.Pragmatic cause',
+    'Expansion.Alternative',
+    'Expansion.Conjunction',
+    'Expansion.Instantiation',
+    'Expansion.List',
+    'Expansion.Restatement',
+    'Temporal.Asynchronous',
+    'Temporal.Synchrony',
+]
+PDTB_E_TEXT_COLUMNS = 2
+PDTB_I_TEXT_COLUMNS = 2
+
+
+# SP Constants:
+SPARXIV = 'SParxiv'
+SPROCSTORY = 'SProcstory'
+SPWIKI = 'SPwiki'
+SP_TRAIN_NAME = 'train.txt'
+SP_VALID_NAME = 'valid.txt'
+SP_TEST_NAME = 'test.txt'
+SP_DATA_DIR = './data/SP/'
+SP_LABELS = ["0", "1", "2", "3", "4"]
+SP_TEXT_COLUMNS = 5
+
+# DC Constants:
+DCCHAT = 'DCchat'
+DCWIKI = 'DCwiki'
+DC_TRAIN_NAME = 'train.txt'
+DC_VALID_NAME = 'valid.txt'
+DC_TEST_NAME = 'test.txt'
+DC_DATA_DIR = './data/DC/'
+DC_LABELS = ["0", "1"]
+DC_TEXT_COLUMNS = 6
+
+
+# RST Constants:
+RST = 'RST'
+RST_TRAIN_NAME = 'RST_TRAIN.pkl'
+RST_VALID_NAME = 'RST_DEV.pkl'
+RST_TEST_NAME = 'RST_TEST.pkl'
+RST_DATA_DIR = './data/RST/'
+RST_LABELS = [
+    'NS-Explanation',
+    'NS-Evaluation',
+    'NN-Condition',
+    'NS-Summary',
+    'SN-Cause',
+    'SN-Background',
+    'NS-Background',
+    'SN-Summary',
+    'NS-Topic-Change',
+    'NN-Explanation',
+    'SN-Topic-Comment',
+    'NS-Elaboration',
+    'SN-Attribution',
+    'SN-Manner-Means',
+    'NN-Evaluation',
+    'NS-Comparison',
+    'NS-Contrast',
+    'SN-Condition',
+    'NS-Temporal',
+    'NS-Enablement',
+    'SN-Evaluation',
+    'NN-Topic-Comment',
+    'NN-Temporal',
+    'NN-Textual-organization',
+    'NN-Same-unit',
+    'NN-Comparison',
+    'NN-Topic-Change',
+    'SN-Temporal',
+    'NN-Joint',
+    'SN-Enablement',
+    'SN-Explanation',
+    'NN-Contrast',
+    'NN-Cause',
+    'SN-Contrast',
+    'NS-Attribution',
+    'NS-Topic-Comment',
+    'SN-Elaboration',
+    'SN-Comparison',
+    'NS-Cause',
+    'NS-Condition',
+    'NS-Manner-Means'
+]
+RST_TEXT_COLUMNS = 2

data/PDTB/Explicit/labelset.txt

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+Comparison.Concession
+Comparison.Contrast
+Contingency.Cause
+Contingency.Condition
+Contingency.Pragmatic condition
+Expansion.Alternative
+Expansion.Conjunction
+Expansion.Instantiation
+Expansion.List
+Expansion.Restatement
+Temporal.Asynchronous
+Temporal.Synchrony

data/PDTB/Implicit/labelset.txt

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+Comparison.Concession
+Comparison.Contrast
+Contingency.Cause
+Contingency.Pragmatic cause
+Expansion.Alternative
+Expansion.Conjunction
+Expansion.Instantiation
+Expansion.List
+Expansion.Restatement
+Temporal.Asynchronous
+Temporal.Synchrony

data/RST/RST_DEV.pkl

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data/RST/cmds.txt

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