diff --git a/corpus/betz_rau.txt b/data/corpus/betz_rau.txt
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diff --git a/corpus/bowell_kemp_ctcg.txt b/data/corpus/bowell_kemp_ctcg.txt
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diff --git a/corpus/brucebarbone_jta.txt b/data/corpus/brucebarbone_jta.txt
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diff --git a/corpus/morrow_weston_awfa.txt b/data/corpus/morrow_weston_awfa.txt
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diff --git a/deepa2-corpus.py b/deepa2-corpus.py
deleted file mode 100644
index ddc000a22f07131bd6122cd265e90652b621f14f..0000000000000000000000000000000000000000
--- a/deepa2-corpus.py
+++ /dev/null
@@ -1,166 +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.
-# TODO: Address all TODOs and remove all explanatory comments
-"""Dataset with argument reconstructions (DA2) manually gathered from the literature."""
-
-import logging
-import json
-import os
-
-logger = logging.getLogger(__name__)
-
-import datasets  # pylint: disable=import-error
-from huggingface_hub import hf_hub_download  # pylint: disable=import-error
-
-
-
-
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@InProceedings{betz:dataset,
-title = {A great new dataset},
-author={Betz, Gregor
-},
-year={2020}
-}
-"""
-
-_DESCRIPTION = """\
-Dataset with argument reconstructions (DA2) manually gathered from the literature.
-"""
-
-_HOMEPAGE = ""
-
-_LICENSE = ""
-
-_URLS = {
-    "all": ["corpus.zip"],
-    "books_only": ["corpus.zip"],
-}
-
-
-class DeepA2Corpus(datasets.GeneratorBasedBuilder):
-    """Dataset with argument reconstructions (DA2) manually gathered from the literature."""
-
-    VERSION = datasets.Version("0.1.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="all", version=VERSION, description="This part of my dataset covers a first domain"),
-        datasets.BuilderConfig(name="books_only", version=VERSION, description="NOT IMPLEMENTED YET"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "all"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "text": datasets.Value("string"),
-            }
-        )
-
-        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):
-        # 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
-        
-        #filenames = _URLS[self.config.name]
-        #
-        #filepaths = []
-        #for filename in filenames:
-        #    logger.info("Downloading %s", filename)
-        #    filepaths.append(
-        #        hf_hub_download(
-        #            repo_id="debatelab/deepa2-harvest",
-        #            repo_type="dataset",
-        #            filename=filename,
-        #            token=True,
-        #        )
-        #    )
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepaths": ["corpus"],
-                    "split": datasets.Split.TRAIN,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepaths": [],
-                    "split": datasets.Split.VALIDATION,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepaths": [],
-                    "split": datasets.Split.TEST,
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepaths, split):
-        # 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.
-
-
-        for filepath in filepaths:
-            dataset = datasets.load_dataset("debatelab/deepa2-corpus", datafiles=f"{filepath}/*.txt",split=split)
-
-
-            with open(filepath, encoding="utf-8") as f:
-                for key, row in enumerate(f):
-                    data = json.loads(row)
-                    data = {
-                        k:v for k,v in data.items()
-                        if k in _DA2_FEATURES
-                    }
-                    data["metadata"] = []
-                    yield key, data