Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
natural-language-inference
Languages:
English
Size:
10K - 100K
License:
| # coding=utf-8 | |
| # Copyright 2020 The TensorFlow Datasets Authors and the 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 | |
| """bAbI_nli datasets""" | |
| from __future__ import absolute_import, division, print_function | |
| import csv | |
| import os | |
| import textwrap | |
| import six | |
| import datasets | |
| bAbI_nli_CITATION = r"""@article{weston2015towards, | |
| title={Towards ai-complete question answering: A set of prerequisite toy tasks}, | |
| author={Weston, Jason and Bordes, Antoine and Chopra, Sumit and Rush, Alexander M and Van Merri{\"e}nboer, Bart and Joulin, Armand and Mikolov, Tomas}, | |
| journal={arXiv preprint arXiv:1502.05698}, | |
| year={2015} | |
| } | |
| @inproceedings{sileo-moens-2022-analysis, | |
| title = "Analysis and Prediction of {NLP} Models via Task Embeddings", | |
| author = "Sileo, Damien and | |
| Moens, Marie-Francine", | |
| booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", | |
| month = jun, | |
| year = "2022", | |
| address = "Marseille, France", | |
| publisher = "European Language Resources Association", | |
| url = "https://aclanthology.org/2022.lrec-1.67", | |
| pages = "633--647", | |
| abstract = "Task embeddings are low-dimensional representations that are trained to capture task properties. In this paper, we propose MetaEval, a collection of 101 NLP tasks. We fit a single transformer to all MetaEval tasks jointly while conditioning it on learned embeddings. The resulting task embeddings enable a novel analysis of the space of tasks. We then show that task aspects can be mapped to task embeddings for new tasks without using any annotated examples. Predicted embeddings can modulate the encoder for zero-shot inference and outperform a zero-shot baseline on GLUE tasks. The provided multitask setup can function as a benchmark for future transfer learning research.", | |
| } | |
| """ | |
| _babi_nli_DESCRIPTION = """\ | |
| bAbi tasks recasted as natural language inference""" | |
| DATA_URL = "https://www.dropbox.com/s/0b98tbrv2mej3cu/babi_nli.zip?dl=1" | |
| LABELS=["not-entailed", "entailed"] | |
| CONFIGS=['single-supporting-fact', | |
| 'two-supporting-facts', | |
| 'three-supporting-facts', | |
| 'two-arg-relations', | |
| 'three-arg-relations', | |
| 'yes-no-questions', | |
| 'counting', | |
| 'lists-sets', | |
| 'simple-negation', | |
| 'indefinite-knowledge', | |
| 'basic-coreference', | |
| 'conjunction', | |
| 'compound-coreference', | |
| 'time-reasoning', | |
| 'basic-deduction', | |
| 'basic-induction', | |
| 'positional-reasoning', | |
| 'size-reasoning', | |
| 'path-finding', | |
| 'agents-motivations'] | |
| class bAbI_nli_Config(datasets.BuilderConfig): | |
| """BuilderConfig for bAbI_nli.""" | |
| def __init__( | |
| self, | |
| text_features, | |
| label_classes=None, | |
| process_label=lambda x: x, | |
| **kwargs, | |
| ): | |
| """BuilderConfig for bAbI_nli. | |
| Args: | |
| text_features: `dict[string, string]`, map from the name of the feature | |
| dict for each text field to the name of the column in the tsv file | |
| label_column: `string`, name of the column in the tsv file corresponding | |
| to the label | |
| data_url: `string`, url to download the zip file from | |
| data_dir: `string`, the path to the folder containing the tsv files in the | |
| downloaded zip | |
| citation: `string`, citation for the data set | |
| url: `string`, url for information about the data set | |
| label_classes: `list[string]`, the list of classes if the label is | |
| categorical. If not provided, then the label will be of type | |
| `datasets.Value('float32')`. | |
| process_label: `Function[string, any]`, function taking in the raw value | |
| of the label and processing it to the form required by the label feature | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(bAbI_nli_Config, self).__init__( | |
| version=datasets.Version("1.0.0", ""), **kwargs | |
| ) | |
| self.text_features = text_features | |
| self.label_column = "label" | |
| self.label_classes = LABELS | |
| self.data_url = DATA_URL | |
| self.data_dir = self.name #os.path.join("babi_nli", self.name) | |
| self.citation = textwrap.dedent(bAbI_nli_CITATION) | |
| self.process_label = lambda x: str(x) | |
| self.description = "" | |
| self.url = "" | |
| class bAbI_nli(datasets.GeneratorBasedBuilder): | |
| """The General Language Understanding Evaluation (bAbI_nli) benchmark.""" | |
| BUILDER_CONFIG_CLASS = bAbI_nli_Config | |
| BUILDER_CONFIGS = [ | |
| bAbI_nli_Config( | |
| name=name, | |
| text_features={"premise": "premise", "hypothesis": "hypothesis"}, | |
| ) for name in CONFIGS | |
| ] | |
| def _info(self): | |
| features = { | |
| text_feature: datasets.Value("string") | |
| for text_feature in six.iterkeys(self.config.text_features) | |
| } | |
| 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=_babi_nli_DESCRIPTION, | |
| features=datasets.Features(features), | |
| homepage=self.config.url, | |
| citation=self.config.citation + "\n" + bAbI_nli_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| dl_dir = dl_manager.download_and_extract(self.config.data_url) | |
| data_dir = os.path.join(dl_dir, self.config.data_dir) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "data_file": os.path.join(data_dir or "", "train.tsv"), | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "data_file": os.path.join(data_dir or "", "validation.tsv"), | |
| "split": "validation", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "data_file": os.path.join(data_dir or "", "test.tsv"), | |
| "split": "test", | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, data_file, split): | |
| process_label = self.config.process_label | |
| label_classes = self.config.label_classes | |
| with open(data_file, encoding="utf8") as f: | |
| reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) | |
| for n, row in enumerate(reader): | |
| example = { | |
| feat: row[col] | |
| for feat, col in six.iteritems(self.config.text_features) | |
| } | |
| example["idx"] = n | |
| if self.config.label_column in row: | |
| label = row[self.config.label_column] | |
| if label_classes and label not in label_classes: | |
| label = int(label) if label else None | |
| example["label"] = process_label(label) | |
| else: | |
| example["label"] = process_label(-1) | |
| yield example["idx"], example | |