disable unstranslated tasks

README.md

@@ -25,6 +25,15 @@ If you use this dataset please cite:
       primaryClass={cs.CL}
     }
 
+Thus far, only 4 tasks have been translated to European Portuguese:
+
+- MRPC
+- RTE
+- STS-B
+- WNLI
+
+The remainder tasks will be added in the future.
+
 
 See [gluebenchmark.com](https://gluebenchmark.com/) for information about the General Language Understanding Evaluation (GLUE) dataset.
 

glue-ptpt.py

@@ -130,55 +130,55 @@ class GLUEPTPT(datasets.GeneratorBasedBuilder):
     """The General Language Understanding Evaluation (GLUE) benchmark."""
 
     BUILDER_CONFIGS = [
-        GLUEPTPTConfig(
-            name="cola",
-            description=textwrap.dedent(
-                """\
-            The Corpus of Linguistic Acceptability consists of English
-            acceptability judgments drawn from books and journal articles on
-            linguistic theory. Each example is a sequence of words annotated
-            with whether it is a grammatical English sentence."""
-            ),
-            text_features={"sentence": "sentence"},
-            label_classes=["unacceptable", "acceptable"],
-            label_column="is_acceptable",
-            data_dir="glue_data_ptpt/CoLA",
-            citation=textwrap.dedent(
-                """\
-            @article{warstadt2018neural,
-              title={Neural Network Acceptability Judgments},
-              author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
-              journal={arXiv preprint arXiv:1805.12471},
-              year={2018}
-            }"""
-            ),
-            url="https://nyu-mll.github.io/CoLA/",
-        ),
-        GLUEPTPTConfig(
-            name="sst2",
-            description=textwrap.dedent(
-                """\
-            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."""
-            ),
-            text_features={"sentence": "sentence"},
-            label_classes=["negative", "positive"],
-            label_column="label",
-            data_dir="glue_data_ptpt/SST-2",
-            citation=textwrap.dedent(
-                """\
-            @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}
-            }"""
-            ),
-            url="https://datasets.stanford.edu/sentiment/index.html",
-        ),
+        # GLUEPTPTConfig(
+        #     name="cola",
+        #     description=textwrap.dedent(
+        #         """\
+        #     The Corpus of Linguistic Acceptability consists of English
+        #     acceptability judgments drawn from books and journal articles on
+        #     linguistic theory. Each example is a sequence of words annotated
+        #     with whether it is a grammatical English sentence."""
+        #     ),
+        #     text_features={"sentence": "sentence"},
+        #     label_classes=["unacceptable", "acceptable"],
+        #     label_column="is_acceptable",
+        #     data_dir="glue_data_ptpt/CoLA",
+        #     citation=textwrap.dedent(
+        #         """\
+        #     @article{warstadt2018neural,
+        #       title={Neural Network Acceptability Judgments},
+        #       author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
+        #       journal={arXiv preprint arXiv:1805.12471},
+        #       year={2018}
+        #     }"""
+        #     ),
+        #     url="https://nyu-mll.github.io/CoLA/",
+        # ),
+        # GLUEPTPTConfig(
+        #     name="sst2",
+        #     description=textwrap.dedent(
+        #         """\
+        #     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."""
+        #     ),
+        #     text_features={"sentence": "sentence"},
+        #     label_classes=["negative", "positive"],
+        #     label_column="label",
+        #     data_dir="glue_data_ptpt/SST-2",
+        #     citation=textwrap.dedent(
+        #         """\
+        #     @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}
+        #     }"""
+        #     ),
+        #     url="https://datasets.stanford.edu/sentiment/index.html",
+        # ),
         GLUEPTPTConfig(
             name="mrpc",
             description=textwrap.dedent(
@@ -202,30 +202,30 @@ class GLUEPTPT(datasets.GeneratorBasedBuilder):
             ),
             url="https://www.microsoft.com/en-us/download/details.aspx?id=52398",
         ),
-        GLUEPTPTConfig(
-            name="qqp_v2",
-            description=textwrap.dedent(
-                """\
-            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."""
-            ),
-            text_features={"question1": "question1", "question2": "question2",},
-            label_classes=["not_duplicate", "duplicate"],
-            label_column="is_duplicate",
-            data_dir="glue_data_ptpt/QQP_v2",
-            citation=textwrap.dedent(
-                """\
-          @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}
-          }"""
-            ),
-            url="https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs",
-        ),
+        # GLUEPTPTConfig(
+        #     name="qqp_v2",
+        #     description=textwrap.dedent(
+        #         """\
+        #     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."""
+        #     ),
+        #     text_features={"question1": "question1", "question2": "question2",},
+        #     label_classes=["not_duplicate", "duplicate"],
+        #     label_column="is_duplicate",
+        #     data_dir="glue_data_ptpt/QQP_v2",
+        #     citation=textwrap.dedent(
+        #         """\
+        #   @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}
+        #   }"""
+        #     ),
+        #     url="https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs",
+        # ),
         GLUEPTPTConfig(
             name="stsb",
             description=textwrap.dedent(
@@ -250,124 +250,124 @@ class GLUEPTPT(datasets.GeneratorBasedBuilder):
             url="http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark",
             process_label=np.float32,
         ),
-        GLUEPTPTConfig(
-            name="snli",
-            description=textwrap.dedent(
-                """\
-            The SNLI corpus (version 1.0) is a collection of 570k human-written English
-            sentence pairs manually labeled for balanced classification with the labels
-            entailment, contradiction, and neutral, supporting the task of natural language
-            inference (NLI), also known as recognizing textual entailment (RTE).
-            """
-            ),
-            text_features={"premise": "sentence1", "hypothesis": "sentence2",},
-            label_classes=["entailment", "neutral", "contradiction"],
-            label_column="gold_label",
-            data_dir="SNLI",
-            citation=textwrap.dedent(
-                """\
-            @inproceedings{snli:emnlp2015,
-                Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
-                Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
-                Publisher = {Association for Computational Linguistics},
-                Title = {A large annotated corpus for learning natural language inference},
-                Year = {2015}
-            }
-            """
-            ),
-            url="https://nlp.stanford.edu/projects/snli/",
-        ),
-        GLUEPTPTConfig(
-            name="mnli",
-            description=textwrap.dedent(
-                """\
-            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_BASE_KWARGS,
-        ),
-        GLUEPTPTConfig(
-            name="mnli_mismatched",
-            description=textwrap.dedent(
-                """\
-          The mismatched validation and test splits from MNLI.
-          See the "mnli" BuilderConfig for additional information."""
-            ),
-            **_MNLI_BASE_KWARGS,
-        ),
-        GLUEPTPTConfig(
-            name="mnli_matched",
-            description=textwrap.dedent(
-                """\
-          The matched validation and test splits from MNLI.
-          See the "mnli" BuilderConfig for additional information."""
-            ),
-            **_MNLI_BASE_KWARGS,
-        ),
-        GLUEPTPTConfig(
-            name="qnli",
-            description=textwrap.dedent(
-                """\
-            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."""
-            ),  # pylint: disable=line-too-long
-            text_features={"question": "question", "sentence": "sentence",},
-            label_classes=["entailment", "not_entailment"],
-            label_column="label",
-            data_dir="glue_data_ptpt/QNLI",
-            citation=textwrap.dedent(
-                """\
-            @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}
-            }"""
-            ),
-            url="https://rajpurkar.github.io/SQuAD-explorer/",
-        ),
-        GLUEPTPTConfig(
-            name="qnli_v2",
-            description=textwrap.dedent(
-                """\
-            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."""
-            ),  # pylint: disable=line-too-long
-            text_features={"question": "question", "sentence": "sentence",},
-            label_classes=["entailment", "not_entailment"],
-            label_column="label",
-            data_dir="glue_data_ptpt/QNLI_v2",
-            citation=textwrap.dedent(
-                """\
-            @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}
-            }"""
-            ),
-            url="https://rajpurkar.github.io/SQuAD-explorer/",
-        ),
+        # GLUEPTPTConfig(
+        #     name="snli",
+        #     description=textwrap.dedent(
+        #         """\
+        #     The SNLI corpus (version 1.0) is a collection of 570k human-written English
+        #     sentence pairs manually labeled for balanced classification with the labels
+        #     entailment, contradiction, and neutral, supporting the task of natural language
+        #     inference (NLI), also known as recognizing textual entailment (RTE).
+        #     """
+        #     ),
+        #     text_features={"premise": "sentence1", "hypothesis": "sentence2",},
+        #     label_classes=["entailment", "neutral", "contradiction"],
+        #     label_column="gold_label",
+        #     data_dir="SNLI",
+        #     citation=textwrap.dedent(
+        #         """\
+        #     @inproceedings{snli:emnlp2015,
+        #         Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
+        #         Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
+        #         Publisher = {Association for Computational Linguistics},
+        #         Title = {A large annotated corpus for learning natural language inference},
+        #         Year = {2015}
+        #     }
+        #     """
+        #     ),
+        #     url="https://nlp.stanford.edu/projects/snli/",
+        # ),
+        # GLUEPTPTConfig(
+        #     name="mnli",
+        #     description=textwrap.dedent(
+        #         """\
+        #     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_BASE_KWARGS,
+        # ),
+        # GLUEPTPTConfig(
+        #     name="mnli_mismatched",
+        #     description=textwrap.dedent(
+        #         """\
+        #   The mismatched validation and test splits from MNLI.
+        #   See the "mnli" BuilderConfig for additional information."""
+        #     ),
+        #     **_MNLI_BASE_KWARGS,
+        # ),
+        # GLUEPTPTConfig(
+        #     name="mnli_matched",
+        #     description=textwrap.dedent(
+        #         """\
+        #   The matched validation and test splits from MNLI.
+        #   See the "mnli" BuilderConfig for additional information."""
+        #     ),
+        #     **_MNLI_BASE_KWARGS,
+        # ),
+        # GLUEPTPTConfig(
+        #     name="qnli",
+        #     description=textwrap.dedent(
+        #         """\
+        #     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."""
+        #     ),  # pylint: disable=line-too-long
+        #     text_features={"question": "question", "sentence": "sentence",},
+        #     label_classes=["entailment", "not_entailment"],
+        #     label_column="label",
+        #     data_dir="glue_data_ptpt/QNLI",
+        #     citation=textwrap.dedent(
+        #         """\
+        #     @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}
+        #     }"""
+        #     ),
+        #     url="https://rajpurkar.github.io/SQuAD-explorer/",
+        # ),
+        # GLUEPTPTConfig(
+        #     name="qnli_v2",
+        #     description=textwrap.dedent(
+        #         """\
+        #     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."""
+        #     ),  # pylint: disable=line-too-long
+        #     text_features={"question": "question", "sentence": "sentence",},
+        #     label_classes=["entailment", "not_entailment"],
+        #     label_column="label",
+        #     data_dir="glue_data_ptpt/QNLI_v2",
+        #     citation=textwrap.dedent(
+        #         """\
+        #     @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}
+        #     }"""
+        #     ),
+        #     url="https://rajpurkar.github.io/SQuAD-explorer/",
+        # ),
         GLUEPTPTConfig(
             name="rte",
             description=textwrap.dedent(
@@ -454,31 +454,31 @@ class GLUEPTPT(datasets.GeneratorBasedBuilder):
             ),
             url="https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html",
         ),
-        GLUEPTPTConfig(
-            name="scitail",
-            description=textwrap.dedent(
-                """\
-            The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
-and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
-retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We
-crowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create
-the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples
-with neutral label"""
-            ),
-            text_features={"premise": "premise", "hypothesis": "hypothesis",},
-            label_classes=["entails", "neutral"],
-            label_column="label",
-            data_dir="glue_data_ptpt/SciTail",
-            citation=""""\
-            inproceedings{scitail,
-                Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
-                Booktitle = {AAAI},
-                Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},
-                Year = {2018}
-            }
-            """,
-            url="https://gluebenchmark.com/diagnostics",
-        ),
+#         GLUEPTPTConfig(
+#             name="scitail",
+#             description=textwrap.dedent(
+#                 """\
+#             The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
+# and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
+# retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We
+# crowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create
+# the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples
+# with neutral label"""
+#             ),
+#             text_features={"premise": "premise", "hypothesis": "hypothesis",},
+#             label_classes=["entails", "neutral"],
+#             label_column="label",
+#             data_dir="glue_data_ptpt/SciTail",
+#             citation=""""\
+#             inproceedings{scitail,
+#                 Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
+#                 Booktitle = {AAAI},
+#                 Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},
+#                 Year = {2018}
+#             }
+#             """,
+#             url="https://gluebenchmark.com/diagnostics",
+#         ),
     ]
 
     def _info(self):