created loading script

gdds.py

@@ -0,0 +1,609 @@
+import json
+import os
+
+import datasets
+
+
+_SUPER_GLUE_CITATION = """\
+"""
+
+_GLUE_DESCRIPTION = """\
+SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
+GLUE with a new set of more difficult language understanding tasks, improved
+resources, and a new public leaderboard.
+"""
+
+_BOOLQ_DESCRIPTION = """\
+BoolQ (Boolean Questions, Clark et al., 2019a) is a QA task where each example consists of a short
+passage and a yes/no question about the passage. The questions are provided anonymously and
+unsolicited by users of the Google search engine, and afterwards paired with a paragraph from a
+Wikipedia article containing the answer. Following the original work, we evaluate with accuracy."""
+
+_CB_DESCRIPTION = """\
+The CommitmentBank (De Marneffe et al., 2019) is a corpus of short texts in which at least
+one sentence contains an embedded clause. Each of these embedded clauses is annotated with the
+degree to which we expect that the person who wrote the text is committed to the truth of the clause.
+The resulting task framed as three-class textual entailment on examples that are drawn from the Wall
+Street Journal, fiction from the British National Corpus, and Switchboard. Each example consists
+of a premise containing an embedded clause and the corresponding hypothesis is the extraction of
+that clause. We use a subset of the data that had inter-annotator agreement above 0.85. The data is
+imbalanced (relatively fewer neutral examples), so we evaluate using accuracy and F1, where for
+multi-class F1 we compute the unweighted average of the F1 per class."""
+
+_COPA_DESCRIPTION = """\
+The Choice Of Plausible Alternatives (COPA, Roemmele et al., 2011) dataset is a causal
+reasoning task in which a system is given a premise sentence and two possible alternatives. The
+system must choose the alternative which has the more plausible causal relationship with the premise.
+The method used for the construction of the alternatives ensures that the task requires causal reasoning
+to solve. Examples either deal with alternative possible causes or alternative possible effects of the
+premise sentence, accompanied by a simple question disambiguating between the two instance
+types for the model. All examples are handcrafted and focus on topics from online blogs and a
+photography-related encyclopedia. Following the recommendation of the authors, we evaluate using
+accuracy."""
+
+_RECORD_DESCRIPTION = """\
+(Reading Comprehension with Commonsense Reasoning Dataset, Zhang et al., 2018) is a
+multiple-choice QA task. Each example consists of a news article and a Cloze-style question about
+the article in which one entity is masked out. The system must predict the masked out entity from a
+given list of possible entities in the provided passage, where the same entity may be expressed using
+multiple different surface forms, all of which are considered correct. Articles are drawn from CNN
+and Daily Mail. Following the original work, we evaluate with max (over all mentions) token-level
+F1 and exact match (EM)."""
+
+_RTE_DESCRIPTION = """\
+The Recognizing Textual Entailment (RTE) datasets come from a series of annual competitions
+on textual entailment, the problem of predicting whether a given premise sentence entails a given
+hypothesis sentence (also known as natural language inference, NLI). RTE was previously included
+in GLUE, and we use the same data and format as before: We merge data from RTE1 (Dagan
+et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli
+et al., 2009). All datasets are combined and converted to two-class classification: entailment and
+not_entailment. Of all the GLUE tasks, RTE was among those that benefited from transfer learning
+the most, jumping from near random-chance performance (~56%) at the time of GLUE's launch to
+85% accuracy (Liu et al., 2019c) at the time of writing. Given the eight point gap with respect to
+human performance, however, the task is not yet solved by machines, and we expect the remaining
+gap to be difficult to close."""
+
+_MULTIRC_DESCRIPTION = """\
+The Multi-Sentence Reading Comprehension dataset (MultiRC, Khashabi et al., 2018)
+is a true/false question-answering task. Each example consists of a context paragraph, a question
+about that paragraph, and a list of possible answers to that question which must be labeled as true or
+false. Question-answering (QA) is a popular problem with many datasets. We use MultiRC because
+of a number of desirable properties: (i) each question can have multiple possible correct answers,
+so each question-answer pair must be evaluated independent of other pairs, (ii) the questions are
+designed such that answering each question requires drawing facts from multiple context sentences,
+and (iii) the question-answer pair format more closely matches the API of other SuperGLUE tasks
+than span-based extractive QA does. The paragraphs are drawn from seven domains including news,
+fiction, and historical text."""
+
+_WIC_DESCRIPTION = """\
+The Word-in-Context (WiC, Pilehvar and Camacho-Collados, 2019) dataset supports a word
+sense disambiguation task cast as binary classification over sentence pairs. Given two sentences and a
+polysemous (sense-ambiguous) word that appears in both sentences, the task is to determine whether
+the word is used with the same sense in both sentences. Sentences are drawn from WordNet (Miller,
+1995), VerbNet (Schuler, 2005), and Wiktionary. We follow the original work and evaluate using
+accuracy."""
+
+_WSC_DESCRIPTION = """\
+The Winograd Schema Challenge (WSC, Levesque et al., 2012) is a reading comprehension
+task in which a system must read a sentence with a pronoun and select the referent of that pronoun
+from a list of choices. Given the difficulty of this task and the headroom still left, we have included
+WSC in SuperGLUE and recast the dataset into its coreference form. The task is cast as a binary
+classification problem, as opposed to N-multiple choice, in order to isolate the model's ability to
+understand the coreference links within a sentence as opposed to various other strategies that may
+come into play in multiple choice conditions. With that in mind, we create a split with 65% negative
+majority class in the validation set, reflecting the distribution of the hidden test set, and 52% negative
+class in the training set. The training and validation examples are drawn from the original Winograd
+Schema dataset (Levesque et al., 2012), as well as those distributed by the affiliated organization
+Commonsense Reasoning. The test examples are derived from fiction books and have been shared
+with us by the authors of the original dataset. Previously, a version of WSC recast as NLI as included
+in GLUE, known as WNLI. No substantial progress was made on WNLI, with many submissions
+opting to submit only majority class predictions. WNLI was made especially difficult due to an
+adversarial train/dev split: Premise sentences that appeared in the training set sometimes appeared
+in the development set with a different hypothesis and a flipped label. If a system memorized the
+training set without meaningfully generalizing, which was easy due to the small size of the training
+set, it could perform far below chance on the development set. We remove this adversarial design
+in the SuperGLUE version of WSC by ensuring that no sentences are shared between the training,
+validation, and test sets.
+However, the validation and test sets come from different domains, with the validation set consisting
+of ambiguous examples such that changing one non-noun phrase word will change the coreference
+dependencies in the sentence. The test set consists only of more straightforward examples, with a
+high number of noun phrases (and thus more choices for the model), but low to no ambiguity."""
+
+_AXB_DESCRIPTION = """\
+An expert-constructed,
+diagnostic dataset that automatically tests models for a broad range of linguistic, commonsense, and
+world knowledge. Each example in this broad-coverage diagnostic is a sentence pair labeled with
+a three-way entailment relation (entailment, neutral, or contradiction) and tagged with labels that
+indicate the phenomena that characterize the relationship between the two sentences. Submissions
+to the GLUE leaderboard are required to include predictions from the submission's MultiNLI
+classifier on the diagnostic dataset, and analyses of the results were shown alongside the main
+leaderboard. Since this broad-coverage diagnostic task has proved difficult for top models, we retain
+it in SuperGLUE. However, since MultiNLI is not part of SuperGLUE, we collapse contradiction
+and neutral into a single not_entailment label, and request that submissions include predictions
+on the resulting set from the model used for the RTE task.
+"""
+
+_AXG_DESCRIPTION = """\
+Winogender is designed to measure gender
+bias in coreference resolution systems. We use the Diverse Natural Language Inference Collection
+(DNC; Poliak et al., 2018) version that casts Winogender as a textual entailment task. Each example
+consists of a premise sentence with a male or female pronoun and a hypothesis giving a possible
+antecedent of the pronoun. Examples occur in minimal pairs, where the only difference between
+an example and its pair is the gender of the pronoun in the premise. Performance on Winogender
+is measured with both accuracy and the gender parity score: the percentage of minimal pairs for
+which the predictions are the same. We note that a system can trivially obtain a perfect gender parity
+score by guessing the same class for all examples, so a high gender parity score is meaningless unless
+accompanied by high accuracy. As a diagnostic test of gender bias, we view the schemas as having high
+positive predictive value and low negative predictive value; that is, they may demonstrate the presence
+of gender bias in a system, but not prove its absence.
+"""
+
+_BOOLQ_CITATION = """\
+@inproceedings{clark2019boolq,
+  title={BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
+  author={Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
+  booktitle={NAACL},
+  year={2019}
+}"""
+
+_CB_CITATION = """\
+@article{de marneff_simons_tonhauser_2019,
+  title={The CommitmentBank: Investigating projection in naturally occurring discourse},
+  journal={proceedings of Sinn und Bedeutung 23},
+  author={De Marneff, Marie-Catherine and Simons, Mandy and Tonhauser, Judith},
+  year={2019}
+}"""
+
+_COPA_CITATION = """\
+@inproceedings{roemmele2011choice,
+  title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning},
+  author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S},
+  booktitle={2011 AAAI Spring Symposium Series},
+  year={2011}
+}"""
+
+_RECORD_CITATION = """\
+@article{zhang2018record,
+  title={Record: Bridging the gap between human and machine commonsense reading comprehension},
+  author={Zhang, Sheng and Liu, Xiaodong and Liu, Jingjing and Gao, Jianfeng and Duh, Kevin and Van Durme, Benjamin},
+  journal={arXiv preprint arXiv:1810.12885},
+  year={2018}
+}"""
+
+_RTE_CITATION = """\
+@inproceedings{dagan2005pascal,
+  title={The PASCAL recognising textual entailment challenge},
+  author={Dagan, Ido and Glickman, Oren and Magnini, Bernardo},
+  booktitle={Machine Learning Challenges Workshop},
+  pages={177--190},
+  year={2005},
+  organization={Springer}
+}
+@inproceedings{bar2006second,
+  title={The second pascal recognising textual entailment challenge},
+  author={Bar-Haim, Roy and Dagan, Ido and Dolan, Bill and Ferro, Lisa and Giampiccolo, Danilo and Magnini, Bernardo and Szpektor, Idan},
+  booktitle={Proceedings of the second PASCAL challenges workshop on recognising textual entailment},
+  volume={6},
+  number={1},
+  pages={6--4},
+  year={2006},
+  organization={Venice}
+}
+@inproceedings{giampiccolo2007third,
+  title={The third pascal recognizing textual entailment challenge},
+  author={Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill},
+  booktitle={Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing},
+  pages={1--9},
+  year={2007},
+  organization={Association for Computational Linguistics}
+}
+@inproceedings{bentivogli2009fifth,
+  title={The Fifth PASCAL Recognizing Textual Entailment Challenge.},
+  author={Bentivogli, Luisa and Clark, Peter and Dagan, Ido and Giampiccolo, Danilo},
+  booktitle={TAC},
+  year={2009}
+}"""
+
+_MULTIRC_CITATION = """\
+@inproceedings{MultiRC2018,
+    author = {Daniel Khashabi and Snigdha Chaturvedi and Michael Roth and Shyam Upadhyay and Dan Roth},
+    title = {Looking Beyond the Surface:A Challenge Set for Reading Comprehension over Multiple Sentences},
+    booktitle = {Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL)},
+    year = {2018}
+}"""
+
+_WIC_CITATION = """\
+@article{DBLP:journals/corr/abs-1808-09121,
+  author={Mohammad Taher Pilehvar and os{\'{e}} Camacho{-}Collados},
+  title={WiC: 10, 000 Example Pairs for Evaluating Context-Sensitive Representations},
+  journal={CoRR},
+  volume={abs/1808.09121},
+  year={2018},
+  url={http://arxiv.org/abs/1808.09121},
+  archivePrefix={arXiv},
+  eprint={1808.09121},
+  timestamp={Mon, 03 Sep 2018 13:36:40 +0200},
+  biburl={https://dblp.org/rec/bib/journals/corr/abs-1808-09121},
+  bibsource={dblp computer science bibliography, https://dblp.org}
+}"""
+
+_WSC_CITATION = """\
+@inproceedings{levesque2012winograd,
+  title={The winograd schema challenge},
+  author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},
+  booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},
+  year={2012}
+}"""
+
+_AXG_CITATION = """\
+@inproceedings{rudinger-EtAl:2018:N18,
+  author    = {Rudinger, Rachel  and  Naradowsky, Jason  and  Leonard, Brian  and  {Van Durme}, Benjamin},
+  title     = {Gender Bias in Coreference Resolution},
+  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
+  month     = {June},
+  year      = {2018},
+  address   = {New Orleans, Louisiana},
+  publisher = {Association for Computational Linguistics}
+}
+"""
+
+
+class SuperGlueConfig(datasets.BuilderConfig):
+    """BuilderConfig for SuperGLUE."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SuperGLUE.
+        Args:
+          features: `list[string]`, list of the features that will appear in the
+            feature dict. Should not include "label".
+          data_url: `string`, url to download the zip file from.
+          citation: `string`, citation for the data set.
+          url: `string`, url for information about the data set.
+          label_classes: `list[string]`, the list of classes for the label if the
+            label is present as a string. Non-string labels will be cast to either
+            'False' or 'True'.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        # Version history:
+        # 1.0.3: Fix not including entity position in ReCoRD.
+        # 1.0.2: Fixed non-nondeterminism in ReCoRD.
+        # 1.0.1: Change from the pre-release trial version of SuperGLUE (v1.9) to
+        #        the full release (v2.0).
+        # 1.0.0: S3 (new shuffling, sharding and slicing mechanism).
+        # 0.0.2: Initial version.
+        super(SuperGlueConfig, self).__init__(version=datasets.Version("1.0.3"), **kwargs)
+        self.features = datasets.Features({"text": datasets.Value("string"), "label":datasets.Value("int32")})
+        self.label_classes = [0, 1]
+
+
+class SuperGlue(datasets.GeneratorBasedBuilder):
+    """The SuperGLUE benchmark."""
+
+    BUILDER_CONFIGS = [
+        SuperGlueConfig(
+            name="fake_news",
+            description=_BOOLQ_DESCRIPTION,
+        ),
+        SuperGlueConfig(
+            name="job_scams",
+            description=_CB_DESCRIPTION,
+        ),
+        SuperGlueConfig(
+            name="phishing",
+            description=_COPA_DESCRIPTION,
+        ),
+        SuperGlueConfig(
+            name="political_statements",
+            description=_MULTIRC_DESCRIPTION,
+        ),
+        SuperGlueConfig(
+            name="product_reviews",
+            description=_RECORD_DESCRIPTION,
+        ),
+        SuperGlueConfig(
+            name="sms",
+            description=_RTE_DESCRIPTION,
+        ),
+        SuperGlueConfig(
+            name="twitter_rumours",
+            description=_WIC_DESCRIPTION,
+        ),
+    ]
+
+    def _info(self):
+        features = {feature: datasets.Value("string") for feature in self.config.features}
+        
+        features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
+
+        return datasets.DatasetInfo(
+            description=_GLUE_DESCRIPTION + self.config.description,
+            features=datasets.Features(features),
+        )
+
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_file": os.path.join(self.config.name, "train.jsonl"),
+                    "split": datasets.Split.TRAIN,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data_file": os.path.join(self.config.name, "validation.jsonl"),
+                    "split": datasets.Split.VALIDATION,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_file": os.path.join(self.config.name, "test.jsonl"),
+                    "split": datasets.Split.TEST,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_file, split):
+        with open(data_file, encoding="utf-8") as f:
+            for line in f:
+                row = json.loads(line)
+
+                if self.config.name == "multirc":
+                    paragraph = row["passage"]
+                    for question in paragraph["questions"]:
+                        for answer in question["answers"]:
+                            label = answer.get("label")
+                            key = "%s_%s_%s" % (row["idx"], question["idx"], answer["idx"])
+                            yield key, {
+                                "paragraph": paragraph["text"],
+                                "question": question["question"],
+                                "answer": answer["text"],
+                                "label": -1 if label is None else _cast_label(bool(label)),
+                                "idx": {"paragraph": row["idx"], "question": question["idx"], "answer": answer["idx"]},
+                            }
+                elif self.config.name == "record":
+                    passage = row["passage"]
+                    entity_texts, entity_spans = _get_record_entities(passage)
+                    for qa in row["qas"]:
+                        yield qa["idx"], {
+                            "passage": passage["text"],
+                            "query": qa["query"],
+                            "entities": entity_texts,
+                            "entity_spans": entity_spans,
+                            "answers": _get_record_answers(qa),
+                            "idx": {"passage": row["idx"], "query": qa["idx"]},
+                        }
+                else:
+                    if self.config.name.startswith("wsc"):
+                        row.update(row["target"])
+                    example = {feature: row[feature] for feature in self.config.features}
+                    if self.config.name == "wsc.fixed":
+                        example = _fix_wst(example)
+                    example["idx"] = row["idx"]
+
+                    if "label" in row:
+                        if self.config.name == "copa":
+                            example["label"] = "choice2" if row["label"] else "choice1"
+                        else:
+                            example["label"] = _cast_label(row["label"])
+                    else:
+                        assert split == datasets.Split.TEST, row
+                        example["label"] = -1
+                    yield example["idx"], example
+
+
+def _fix_wst(ex):
+    """Fixes most cases where spans are not actually substrings of text."""
+
+    def _fix_span_text(k):
+        """Fixes a single span."""
+        text = ex[k + "_text"]
+        index = ex[k + "_index"]
+
+        if text in ex["text"]:
+            return
+
+        if text in ("Kamenev and Zinoviev", "Kamenev, Zinoviev, and Stalin"):
+            # There is no way to correct these examples since the subjects have
+            # intervening text.
+            return
+
+        if "theyscold" in text:
+            ex["text"].replace("theyscold", "they scold")
+            ex["span2_index"] = 10
+        # Make sure case of the first words match.
+        first_word = ex["text"].split()[index]
+        if first_word[0].islower():
+            text = text[0].lower() + text[1:]
+        else:
+            text = text[0].upper() + text[1:]
+        # Remove punctuation in span.
+        text = text.rstrip(".")
+        # Replace incorrect whitespace character in span.
+        text = text.replace("\n", " ")
+        ex[k + "_text"] = text
+        assert ex[k + "_text"] in ex["text"], ex
+
+    _fix_span_text("span1")
+    _fix_span_text("span2")
+    return ex
+
+
+def _cast_label(label):
+    """Converts the label into the appropriate string version."""
+    if isinstance(label, str):
+        return label
+    elif isinstance(label, bool):
+        return "True" if label else "False"
+    elif isinstance(label, int):
+        assert label in (0, 1)
+        return str(label)
+    else:
+        raise ValueError("Invalid label format.")
+
+
+def _get_record_entities(passage):
+    """Returns the unique set of entities."""
+    text = passage["text"]
+    entity_spans = list()
+    for entity in passage["entities"]:
+        entity_text = text[entity["start"] : entity["end"] + 1]
+        entity_spans.append({"text": entity_text, "start": entity["start"], "end": entity["end"] + 1})
+    entity_spans = sorted(entity_spans, key=lambda e: e["start"])  # sort by start index
+    entity_texts = set(e["text"] for e in entity_spans)  # for backward compatability
+    return entity_texts, entity_spans
+
+
+def _get_record_answers(qa):
+    """Returns the unique set of answers."""
+    if "answers" not in qa:
+        return []
+    answers = set()
+    for answer in qa["answers"]:
+        answers.add(answer["text"])
+    return sorted(answers)
+
+
+# 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
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+TODO: Add citation here
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("2.1.0")
+
+    # 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="fake_news", version=VERSION, description="This part of my dataset covers a first domain"),
+        datasets.BuilderConfig(name="job_scams", version=VERSION, description="This part of my dataset covers a second domain"),
+        datasets.BuilderConfig(name="phishing", version=VERSION, description="This part of my dataset covers a second domain"),
+        datasets.BuilderConfig(name="political_statements", version=VERSION, description="This part of my dataset covers a first domain"),
+        datasets.BuilderConfig(name="product_reviews", version=VERSION, description="This part of my dataset covers a second domain"),
+        datasets.BuilderConfig(name="sms", version=VERSION, description="This part of my dataset covers a second domain"),
+        datasets.BuilderConfig(name="twitter_rumours", version=VERSION, description="This part of my dataset covers a first domain"),
+    ]
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.ClassLabel(num_classes=2)
+                    # 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,
+            # License for the dataset if available
+            license=_LICENSE,
+            # 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
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(self.config.name, "train.jsonl"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(self.config.name, "validate.jsonl"),
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(self.config.name, "test.jsonl"),
+                    "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.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                yield key, {
+                    "text": data["text"],
+                    "label": "" if split == "test" else data["label"],
+                }
+