Delete loading script

anli.py

@@ -1,152 +0,0 @@
-# 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="plain_text",
-            description="Plain text",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "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):
-
-        downloaded_dir = dl_manager.download_and_extract("https://dl.fbaipublicfiles.com/anli/anli_v0.1.zip")
-
-        anli_path = os.path.join(downloaded_dir, "anli_v0.1")
-
-        path_dict = dict()
-        for round_tag in ["R1", "R2", "R3"]:
-            path_dict[round_tag] = dict()
-            for split_name in ["train", "dev", "test"]:
-                path_dict[round_tag][split_name] = os.path.join(anli_path, round_tag, f"{split_name}.jsonl")
-
-        return [
-            # Round 1
-            datasets.SplitGenerator(name="train_r1", gen_kwargs={"filepath": path_dict["R1"]["train"]}),
-            datasets.SplitGenerator(name="dev_r1", gen_kwargs={"filepath": path_dict["R1"]["dev"]}),
-            datasets.SplitGenerator(name="test_r1", gen_kwargs={"filepath": path_dict["R1"]["test"]}),
-            # Round 2
-            datasets.SplitGenerator(name="train_r2", gen_kwargs={"filepath": path_dict["R2"]["train"]}),
-            datasets.SplitGenerator(name="dev_r2", gen_kwargs={"filepath": path_dict["R2"]["dev"]}),
-            datasets.SplitGenerator(name="test_r2", gen_kwargs={"filepath": path_dict["R2"]["test"]}),
-            # Round 3
-            datasets.SplitGenerator(name="train_r3", gen_kwargs={"filepath": path_dict["R3"]["train"]}),
-            datasets.SplitGenerator(name="dev_r3", gen_kwargs={"filepath": path_dict["R3"]["dev"]}),
-            datasets.SplitGenerator(name="test_r3", gen_kwargs={"filepath": path_dict["R3"]["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Generate mnli 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 item["uid"], {
-                    "uid": item["uid"],
-                    "premise": item["context"],
-                    "hypothesis": item["hypothesis"],
-                    "label": stdnli_label[item["label"]],
-                    "reason": reason_text,
-                }