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@@ -53,3 +53,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+minority_examples/train.anti_biased.jsonl filter=lfs diff=lfs merge=lfs -text
+minority_examples/train.biased.jsonl filter=lfs diff=lfs merge=lfs -text
+partial_input/train.biased.jsonl filter=lfs diff=lfs merge=lfs -text

minority_examples/train.anti_biased.jsonl

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minority_examples/train.biased.jsonl

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+oid sha256:df886f1ff71ed64f63a6bf3574d9ff75b3d0c3f35e3bd1797fd72b7b6dbfd81f
+size 52871395

partial_input/train.biased.jsonl

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+size 53267457

qqp.py

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+# 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
+"""The General Language Understanding Evaluation (GLUE) benchmark."""
+
+
+import csv
+import os
+import textwrap
+import json
+
+import numpy as np
+
+import datasets
+
+
+_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.
+"""
+
+
+class GlueConfig(datasets.BuilderConfig):
+    """BuilderConfig for GLUE."""
+
+    def __init__(
+        self,
+        text_features,
+        label_column,
+        data_url,
+        data_dir,
+        citation,
+        url,
+        label_classes=None,
+        process_label=lambda x: x,
+        **kwargs,
+    ):
+        """BuilderConfig for GLUE.
+        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(GlueConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.text_features = text_features
+        self.label_column = label_column
+        self.label_classes = label_classes
+        self.data_url = data_url
+        self.data_dir = data_dir
+        self.citation = citation
+        self.url = url
+        self.process_label = process_label
+
+
+class Glue(datasets.GeneratorBasedBuilder):
+    """The General Language Understanding Evaluation (GLUE) benchmark."""
+
+    BUILDER_CONFIGS = [
+        GlueConfig(
+            name=bias_amplified_splits_type,
+            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="label",
+            data_url="https://dl.fbaipublicfiles.com/glue/data/QQP-clean.zip",
+            data_dir="QQP",
+            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",
+        ) for bias_amplified_splits_type in ['minority_examples', 'partial_input']
+    ]
+
+    def _info(self):
+        features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
+        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,
+            features=datasets.Features(features),
+            homepage=self.config.url,
+            citation=self.config.citation + "\n" + _GLUE_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name="train.biased",
+                gen_kwargs={
+                    "filepath": dl_manager.download(os.path.join(self.config.name, "train.biased.jsonl")),
+                },
+            ),
+            datasets.SplitGenerator(
+                name="train.anti_biased",
+                gen_kwargs={
+                    "filepath": dl_manager.download(os.path.join(self.config.name, "train.anti_biased.jsonl")),
+                },
+            ),
+            datasets.SplitGenerator(
+                name="validation.biased",
+                gen_kwargs={
+                    "filepath": dl_manager.download(os.path.join(self.config.name, "validation.biased.jsonl")),
+                },
+            ),
+            datasets.SplitGenerator(
+                name="validation.anti_biased",
+                gen_kwargs={
+                    "filepath": dl_manager.download(os.path.join(self.config.name, "validation.anti_biased.jsonl")),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate examples.
+
+        Args:
+          filepath: a string
+
+        Yields:
+          dictionaries containing "premise", "hypothesis" and "label" strings
+        """
+        process_label = self.config.process_label
+        label_classes = self.config.label_classes
+
+        for idx, line in enumerate(open(filepath, "rb")):
+            if line is not None:
+                line = line.strip().decode("utf-8")
+                item = json.loads(line)
+                example = {
+                    "idx": item["idx"],
+                    "question1": item["question1"],
+                    "question2": item["question2"],
+                }
+                if self.config.label_column in item:
+                    label = item[self.config.label_column]
+                    # For some tasks, the label is represented as 0 and 1 in the tsv
+                    # files and needs to be cast to integer to work with the feature.
+                    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