Create recast.py

recast.py

@@ -0,0 +1,230 @@
+# 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
+"""Recast datasets"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+import textwrap
+
+import six
+
+import datasets
+
+
+_Recast_CITATION = r"""inproceedings{poliak-etal-2018-collecting,
+    title = "Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation",
+    author = "Poliak, Adam  and
+      Haldar, Aparajita  and
+      Rudinger, Rachel  and
+      Hu, J. Edward  and
+      Pavlick, Ellie  and
+      White, Aaron Steven  and
+      Van Durme, Benjamin",
+    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
+    month = oct # "-" # nov,
+    year = "2018",
+    address = "Brussels, Belgium",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D18-1007",
+    doi = "10.18653/v1/D18-1007",
+    pages = "67--81",
+    abstract = "We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at \url{https://www.decomp.net}, and will grow over time as additional resources are recast and added from novel sources.",
+}
+"""
+
+_Recast_DESCRIPTION = """\
+A diverse collection of tasks recasted as natural language inference tasks.
+"""
+
+DATA_URL = "https://www.dropbox.com/s/z1mcq6ygfsae0wj/recast.zip?dl=1"
+
+TASK_TO_LABELS = {
+    "recast_kg_relations": ["1", "2", "3", "4", "5", "6"],
+    "recast_puns": ["not-entailed", "entailed"],
+    "recast_factuality": ["not-entailed", "entailed"],
+    "recast_verbnet": ["not-entailed", "entailed"],
+    "recast_verbcorner": ["not-entailed", "entailed"],
+    "recast_sentiment": ["not-entailed", "entailed"],
+    "recast_megaveridicality": ["not-entailed", "entailed"],
+    "recast_ner": ["not-entailed", "entailed"],
+    "recast_winogender": ["not-entailed", "entailed"],
+    "recast_ner": ["not-entailed", "entailed"],
+}
+
+
+def get_labels(task):
+    return TASK_TO_LABELS[task]
+
+
+class RecastConfig(datasets.BuilderConfig):
+    """BuilderConfig for Recast."""
+
+    def __init__(
+        self,
+        text_features,
+        label_classes=None,
+        process_label=lambda x: x,
+        **kwargs,
+    ):
+        """BuilderConfig for Recast.
+        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(RecastConfig, self).__init__(
+            version=datasets.Version("1.0.0", ""), **kwargs
+        )
+
+        self.text_features = text_features
+        self.label_column = "label"
+        self.label_classes = get_labels(self.name)
+        self.data_url = DATA_URL
+        self.data_dir = os.path.join("recast", self.name)
+        self.citation = textwrap.dedent(_Recast_CITATION)
+        self.process_label = lambda x: str(x)
+        self.description = ""
+        self.url = ""
+
+
+class Recast(datasets.GeneratorBasedBuilder):
+
+    """The General Language Understanding Evaluation (Recast) benchmark."""
+
+    BUILDER_CONFIG_CLASS = RecastConfig
+
+    BUILDER_CONFIGS = [
+        RecastConfig(
+            name="recast_kg_relations",
+            text_features={"context": "context", "hypothesis": "hypothesis"},
+        ),
+        RecastConfig(
+            name="recast_puns",
+            text_features={"context": "context", "hypothesis": "hypothesis"},
+        ),
+        RecastConfig(
+            name="recast_factuality",
+            text_features={"context": "context", "hypothesis": "hypothesis"},
+        ),
+        RecastConfig(
+            name="recast_verbnet",
+            text_features={"context": "context", "hypothesis": "hypothesis"},
+        ),
+        RecastConfig(
+            name="recast_verbcorner",
+            text_features={"context": "context", "hypothesis": "hypothesis"},
+        ),
+        RecastConfig(
+            name="recast_ner",
+            text_features={"context": "context", "hypothesis": "hypothesis"},
+        ),
+        RecastConfig(
+            name="recast_sentiment",
+            text_features={"context": "context", "hypothesis": "hypothesis"},
+        ),
+        RecastConfig(
+            name="recast_megaveridicality",
+            text_features={"context": "context", "hypothesis": "hypothesis"},
+        ),
+    ]
+
+    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=_Recast_DESCRIPTION,
+            features=datasets.Features(features),
+            homepage=self.config.url,
+            citation=self.config.citation + "\n" + _Recast_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 "", "dev.tsv"),
+                    "split": "dev",
+                },
+            ),
+            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