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@@ -53,3 +53,7 @@ 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.anti_biased.jsonl filter=lfs diff=lfs merge=lfs -text
+partial_input/train.biased.jsonl filter=lfs diff=lfs merge=lfs -text

anli.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 Adversarial NLI Corpus."""
+
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{nie2019adversarial,
+    title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
+    author={Nie, Yixin
+                and Williams, Adina
+                and Dinan, Emily
+                and Bansal, Mohit
+                and Weston, Jason
+                and Kiela, Douwe},
+    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+}
+"""
+
+_DESCRIPTION = """\
+The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
+The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
+ANLI is much more difficult than its predecessors including SNLI and MNLI.
+It contains three rounds. Each round has train/dev/test splits.
+"""
+
+stdnli_label = {
+    "e": "entailment",
+    "n": "neutral",
+    "c": "contradiction",
+}
+
+
+class ANLIConfig(datasets.BuilderConfig):
+    """BuilderConfig for ANLI."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for ANLI.
+
+            Args:
+        .
+              **kwargs: keyword arguments forwarded to super.
+        """
+        super(ANLIConfig, self).__init__(version=datasets.Version("0.1.0", ""), **kwargs)
+
+
+class ANLI(datasets.GeneratorBasedBuilder):
+    """ANLI: The ANLI Dataset."""
+
+    BUILDER_CONFIGS = [
+        ANLIConfig(
+            name=bias_amplified_splits_type,
+            description="",
+        ) for bias_amplified_splits_type in ["minority_examples", "partial_input"]
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "round": datasets.Value("string"),
+                    "uid": datasets.Value("string"),
+                    "premise": datasets.Value("string"),
+                    "hypothesis": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
+                    "reason": datasets.Value("string"),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both premise
+            # and hypothesis as input).
+            supervised_keys=None,
+            homepage="https://github.com/facebookresearch/anli/",
+            citation=_CITATION,
+        )
+
+    def _vocab_text_gen(self, filepath):
+        for _, ex in self._generate_examples(filepath):
+            yield " ".join([ex["premise"], ex["hypothesis"]])
+
+    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"))}),
+            datasets.SplitGenerator(name="test.biased", gen_kwargs={"filepath": dl_manager.download(os.path.join(self.config.name, "test.biased.jsonl"))}),
+            datasets.SplitGenerator(name="test.anti-biased", gen_kwargs={"filepath": dl_manager.download(os.path.join(self.config.name, "test.anti-biased.jsonl"))})
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate examples.
+
+        Args:
+          filepath: a string
+
+        Yields:
+          dictionaries containing "premise", "hypothesis" and "label" strings
+        """
+        for idx, line in enumerate(open(filepath, "rb")):
+            if line is not None:
+                line = line.strip().decode("utf-8")
+                item = json.loads(line)
+
+                reason_text = ""
+                if "reason" in item:
+                    reason_text = item["reason"]
+
+                yield f'{item["round"]}-{item["uid"]}', {
+                    "round": item["round"],
+                    "uid": item["uid"],
+                    "premise": item["context"],
+                    "hypothesis": item["hypothesis"],
+                    "label": stdnli_label[item["label"]],
+                    "reason": reason_text,
+                }

minority_examples/train.anti_biased.jsonl

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+size 14636850

minority_examples/train.biased.jsonl

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+size 67780649

partial_input/train.anti_biased.jsonl

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partial_input/train.biased.jsonl

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