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Arguement_Mining_CL2017.py

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-# 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
-"""
-Arguement Mining Dataset created by Stab , Gurevych et. al. CL 2017 
-"""
-
-import datasets
-import os
-
-
-_CITATION = """\
-@article{stab2017parsing,
-  title={Parsing argumentation structures in persuasive essays},
-  author={Stab, Christian and Gurevych, Iryna},
-  journal={Computational Linguistics},
-  volume={43},
-  number={3},
-  pages={619--659},
-  year={2017},
-  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
-}
-"""
-
-_DESCRIPTION = """\
-tokens along with chunk id. IOB1 format Begining of arguement denoted by B-ARG,inside arguement
-denoted by I-ARG, other chunks are O
-Orginial train,test split as used by the paper is provided
-"""
-
-_URL = "https://raw.githubusercontent.com/Sam131112/Argument-Mining-Dataset/main/"
-_TRAINING_FILE = "train.txt"
-_TEST_FILE = "test.txt"
-
-
-class ArguementMiningCL2017Config(datasets.BuilderConfig):
-    """BuilderConfig for CL2017"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig forCl2017.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(ArguementMiningCL2017Config, self).__init__(**kwargs)
-
-
-class ArguementMiningCL2017(datasets.GeneratorBasedBuilder):
-    """CL2017 dataset."""
-
-    BUILDER_CONFIGS = [
-        ArguementMiningCL2017Config(name="cl2017", version=datasets.Version("1.0.0"), description="Cl2017 dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "chunk_tags":datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-ARG",
-                                "I-ARG",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://direct.mit.edu/coli/article/43/3/619/1573/Parsing-Argumentation-Structures-in-Persuasive",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "train": _TRAINING_FILE,
-            "test": _TEST_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.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        print("⏳ Generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            guid = 0
-            tokens = []
-            pos_tags = []
-            chunk_tags = []
-            ner_tags = []
-            for line in f:
-                if line == "\n":
-                    if tokens:
-                        yield guid, {
-                            "id": str(guid),
-                            "tokens": tokens,
-                            "chunk_tags": chunk_tags,
-                        }
-                        guid = guid+1
-                        tokens = []
-                        chunk_tags = []
-                else:
-                    # cl2017 tokens are space separated
-                    line=line.strip('\n')
-                    splits = line.split("\t")
-                    #print(splits)
-                    tokens.append(splits[0])
-                    chunk_tags.append(splits[1])
-                    #print({"id": str(guid),"tokens": tokens,"chunk_tags": chunk_tags,})
-            # last example
-            yield guid, {
-                "id": str(guid),
-                "tokens": tokens,
-                "chunk_tags": chunk_tags,
-            }

cl2017/arguement_mining_cl2017-test.parquet

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cl2017/arguement_mining_cl2017-train.parquet

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