| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| """The SuperGLUE benchmark.""" |
|
|
| import json |
| import os |
|
|
| import datasets |
|
|
| _CITATION = """\ |
| """ |
|
|
| |
| _DESCRIPTION = """\ |
| """ |
|
|
| _HOMEPAGE = "" |
|
|
| _LICENSE = "" |
|
|
| _GLUE_CITATION = """\ |
| @inproceedings{wang2019glue, |
| title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding}, |
| author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.}, |
| note={In the Proceedings of ICLR.}, |
| year={2019} |
| } |
| """ |
|
|
| _GLUE_DESCRIPTION = """\ |
| GLUE, the General Language Understanding Evaluation benchmark |
| (https://gluebenchmark.com/) is a collection of resources for training, |
| evaluating, and analyzing natural language understanding systems. |
| |
| """ |
| _SST_DESCRIPTION = """\ |
| The Stanford Sentiment Treebank consists of sentences from movie reviews and |
| human annotations of their sentiment. The task is to predict the sentiment of a |
| given sentence. We use the two-way (positive/negative) class split, and use only |
| sentence-level labels.""" |
| _SST_CITATION = """\ |
| @inproceedings{socher2013recursive, |
| title={Recursive deep models for semantic compositionality over a sentiment treebank}, |
| author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher}, |
| booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing}, |
| pages={1631--1642}, |
| year={2013} |
| }""" |
| _MRPC_DESCRIPTION = """\ |
| The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of |
| sentence pairs automatically extracted from online news sources, with human annotations |
| for whether the sentences in the pair are semantically equivalent.""" |
| _MRPC_CITATION = """\ |
| @inproceedings{dolan2005automatically, |
| title={Automatically constructing a corpus of sentential paraphrases}, |
| author={Dolan, William B and Brockett, Chris}, |
| booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)}, |
| year={2005} |
| }""" |
| _QQP_DESCRIPTION = """\ |
| The Quora Question Pairs2 dataset is a collection of question pairs from the |
| community question-answering website Quora. The task is to determine whether a |
| pair of questions are semantically equivalent.""" |
| _QQP_CITATION = """\ |
| @online{WinNT, |
| author = {Iyer, Shankar and Dandekar, Nikhil and Csernai, Kornel}, |
| title = {First Quora Dataset Release: Question Pairs}, |
| year = {2017}, |
| url = {https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs}, |
| urldate = {2019-04-03} |
| }""" |
| _STSB_DESCRIPTION = """\ |
| The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of |
| sentence pairs drawn from news headlines, video and image captions, and natural |
| language inference data. Each pair is human-annotated with a similarity score |
| from 1 to 5.""" |
| _STSB_CITATION = """\ |
| @article{cer2017semeval, |
| title={Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation}, |
| author={Cer, Daniel and Diab, Mona and Agirre, Eneko and Lopez-Gazpio, Inigo and Specia, Lucia}, |
| journal={arXiv preprint arXiv:1708.00055}, |
| year={2017} |
| }""" |
| _MNLI_DESCRIPTION = """\ |
| The Multi-Genre Natural Language Inference Corpus is a crowdsourced |
| collection of sentence pairs with textual entailment annotations. Given a premise sentence |
| and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis |
| (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are |
| gathered from ten different sources, including transcribed speech, fiction, and government reports. |
| We use the standard test set, for which we obtained private labels from the authors, and evaluate |
| on both the matched (in-domain) and mismatched (cross-domain) section. We also use and recommend |
| the SNLI corpus as 550k examples of auxiliary training data.""" |
| _MNLI_CITATION = """\ |
| @InProceedings{N18-1101, |
| author = "Williams, Adina |
| and Nangia, Nikita |
| and Bowman, Samuel", |
| title = "A Broad-Coverage Challenge Corpus for |
| Sentence Understanding through Inference", |
| booktitle = "Proceedings of the 2018 Conference of |
| the North American Chapter of the |
| Association for Computational Linguistics: |
| Human Language Technologies, Volume 1 (Long |
| Papers)", |
| year = "2018", |
| publisher = "Association for Computational Linguistics", |
| pages = "1112--1122", |
| location = "New Orleans, Louisiana", |
| url = "http://aclweb.org/anthology/N18-1101" |
| } |
| @article{bowman2015large, |
| title={A large annotated corpus for learning natural language inference}, |
| author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D}, |
| journal={arXiv preprint arXiv:1508.05326}, |
| year={2015} |
| }""" |
| _QNLI_DESCRIPTION = """\ |
| The Stanford Question Answering Dataset is a question-answering |
| dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn |
| from Wikipedia) contains the answer to the corresponding question (written by an annotator). We |
| convert the task into sentence pair classification by forming a pair between each question and each |
| sentence in the corresponding context, and filtering out pairs with low lexical overlap between the |
| question and the context sentence. The task is to determine whether the context sentence contains |
| the answer to the question. This modified version of the original task removes the requirement that |
| the model select the exact answer, but also removes the simplifying assumptions that the answer |
| is always present in the input and that lexical overlap is a reliable cue.""" |
| _QNLI_CITATION = """\ |
| @article{rajpurkar2016squad, |
| title={Squad: 100,000+ questions for machine comprehension of text}, |
| author={Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy}, |
| journal={arXiv preprint arXiv:1606.05250}, |
| year={2016} |
| }""" |
| _WNLI_DESCRIPTION = """\ |
| The Winograd Schema Challenge (Levesque et al., 2011) 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. The examples are manually constructed to foil simple statistical methods: Each |
| one is contingent on contextual information provided by a single word or phrase in the sentence. |
| To convert the problem into sentence pair classification, we construct sentence pairs by replacing |
| the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the |
| pronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of |
| new examples derived from fiction books that was shared privately by the authors of the original |
| corpus. While the included training set is balanced between two classes, the test set is imbalanced |
| between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: |
| hypotheses are sometimes shared between training and development examples, so if a model memorizes the |
| training examples, they will predict the wrong label on corresponding development set |
| example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence |
| between a model's score on this task and its score on the unconverted original task. We |
| call converted dataset WNLI (Winograd NLI).""" |
| _WNLI_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} |
| }""" |
|
|
| _SUPER_GLUE_CITATION = """\ |
| @article{wang2019superglue, |
| title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems}, |
| author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R}, |
| journal={arXiv preprint arXiv:1905.00537}, |
| year={2019} |
| } |
| |
| Note that each SuperGLUE dataset has its own citation. Please see the source to |
| get the correct citation for each contained dataset. |
| """ |
|
|
| _SUPER_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.""" |
|
|
| _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.""" |
|
|
| _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} |
| }""" |
|
|
| _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} |
| }""" |
|
|
| |
| |
| |
| _URL = "https://huggingface.co/datasets/KBLab/overlim/resolve/main/data/" |
| _TASKS = { |
| "boolq": "boolq.tar.gz", |
| "cb": "cb.tar.gz", |
| "copa": "copa.tar.gz", |
| "mnli": "mnli.tar.gz", |
| "mrpc": "mrpc.tar.gz", |
| "qnli": "qnli.tar.gz", |
| "qqp": "qqp.tar.gz", |
| "rte": "rte.tar.gz", |
| "sst": "sst.tar.gz", |
| "stsb": "stsb.tar.gz", |
| "wnli": "wnli.tar.gz" |
| } |
| _LANGUAGES = {"sv", "da", "nb"} |
|
|
|
|
| class OverLimConfig(datasets.BuilderConfig): |
| """BuilderConfig for Suc.""" |
| def __init__(self, name, description, features, citation, language, label_classes=None, **kwargs): |
| """BuilderConfig for OverLim. |
| """ |
| self.full_name = name + "_" + language |
| super(OverLimConfig, |
| self).__init__(name=self.full_name, version=datasets.Version("1.0.2"), **kwargs) |
| self.features = features + ["label"] |
| self.label_classes = label_classes |
| self.citation = citation |
| self.description = description |
| self.task_name = name |
| self.language = language |
| self.data_url = _TASKS[name] |
|
|
|
|
|
|
| class OverLim(datasets.GeneratorBasedBuilder): |
| """OverLim""" |
|
|
| BUILDER_CONFIGS = [[ |
| OverLimConfig( |
| name="boolq", |
| description=_BOOLQ_DESCRIPTION, |
| features=["question", "passage"], |
| label_classes=["False", "True"], |
| citation=_BOOLQ_CITATION, |
| language=lang, |
| ), |
| OverLimConfig( |
| name="cb", |
| description=_CB_DESCRIPTION, |
| features=["premise", "hypothesis"], |
| label_classes=["entailment", "contradiction", "neutral"], |
| citation=_CB_CITATION, |
| language=lang, |
| ), |
| OverLimConfig( |
| name="copa", |
| description=_COPA_DESCRIPTION, |
| label_classes=["choice1", "choice2"], |
| |
| |
| features=["premise", "choice1", "choice2", "question"], |
| citation=_COPA_CITATION, |
| language=lang, |
| ), |
| OverLimConfig( |
| name="rte", |
| description=_RTE_DESCRIPTION, |
| features=["premise", "hypothesis"], |
| label_classes=["entailment", "not_entailment"], |
| citation=_RTE_CITATION, |
| language=lang, |
| ), |
| OverLimConfig( |
| name="qqp", |
| description=_QQP_DESCRIPTION, |
| features=["text_a", "text_b"], |
| label_classes=["not_duplicate", "duplicate"], |
| citation=_QQP_CITATION, |
| language=lang, |
| ), |
| OverLimConfig( |
| name="qnli", |
| description=_QNLI_DESCRIPTION, |
| features=["premise", "hypothesis"], |
| label_classes=["entailment", "not_entailment"], |
| citation=_QNLI_CITATION, |
| language=lang, |
| ), |
| OverLimConfig( |
| name="stsb", |
| description=_STSB_DESCRIPTION, |
| features=["text_a", "text_b"], |
| citation=_STSB_CITATION, |
| language=lang, |
| ), |
| OverLimConfig( |
| name="mnli", |
| description=_MNLI_DESCRIPTION, |
| features=["premise", "hypothesis"], |
| label_classes=["entailment", "neutral", "contradiction"], |
| citation=_MNLI_CITATION, |
| language=lang, |
| ), |
| OverLimConfig( |
| name="mrpc", |
| description=_MRPC_DESCRIPTION, |
| features=["text_a", "text_b"], |
| label_classes=["not_equivalent", "equivalent"], |
| citation=_MRPC_CITATION, |
| language=lang, |
| ), |
| OverLimConfig( |
| name="wnli", |
| description=_WNLI_DESCRIPTION, |
| features=["premise", "hypothesis"], |
| label_classes=["not_entailment", "entailment"], |
| citation=_WNLI_CITATION, |
| language=lang, |
| ), |
| OverLimConfig( |
| name="sst", |
| description=_SST_DESCRIPTION, |
| features=["text"], |
| label_classes=["negative", "positive"], |
| citation=_SST_CITATION, |
| language=lang, |
| ) |
|
|
| ] for lang in _LANGUAGES] |
| BUILDER_CONFIGS = [element for inner in BUILDER_CONFIGS for element in inner] |
|
|
| def _info(self): |
| features = {feature: datasets.Value("string") for feature in self.config.features if feature != "label"} |
| if self.config.label_classes: |
| |
| |
| |
| features["label"] = datasets.features.ClassLabel(names=self.config.label_classes) |
| else: |
| features["label"] = datasets.Value("float32") |
| features["idx"] = datasets.Value("int32") |
|
|
| return datasets.DatasetInfo( |
| description=_GLUE_DESCRIPTION + self.config.description, |
| features=datasets.Features(features), |
| homepage=_HOMEPAGE, |
| citation=self.config.citation + "\n" + _SUPER_GLUE_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| dl_dir = dl_manager.download_and_extract(os.path.join(_URL, self.config.language, self.config.data_url)) |
| |
| dl_dir = os.path.join(dl_dir, self.config.task_name) |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "data_file": os.path.join(dl_dir, "train.jsonl"), |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "data_file": os.path.join(dl_dir, "val.jsonl"), |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "data_file": os.path.join(dl_dir, "test.jsonl"), |
| }, |
| ), |
| ] |
|
|
| def _generate_examples(self, data_file): |
| with open(data_file, encoding="utf-8") as f: |
| for line in f: |
| row = json.loads(line) |
| example = {feature: row[feature] for feature in self.config.features} |
| example["idx"] = row["idx"] |
|
|
| if self.config.name == "copa": |
| example["label"] = "choice2" if row["label"] else "choice1" |
| else: |
| example["label"] = _cast_label(row["label"]) |
| yield example["idx"], example |
|
|
|
|
| def _cast_label(label): |
| """Converts the label into the appropriate string version.""" |
| try: |
| label = float(label) |
| return label |
| except ValueError: |
| pass |
| try: |
| label = int(label) |
| return label |
| except ValueError: |
| pass |
| |
| |
| |
| |
| |
| return label |
| |
|
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
