Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* 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."""
+
+from __future__ import absolute_import, division, print_function
+
+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,
+                }

dataset_infos.json

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+{"plain_text": {"description": "The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, \nThe dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.\nANLI is much more difficult than its predecessors including SNLI and MNLI.\nIt contains three rounds. Each round has train/dev/test splits.\n", "citation": "@InProceedings{nie2019adversarial,\n    title={Adversarial NLI: A New Benchmark for Natural Language Understanding},\n    author={Nie, Yixin \n                and Williams, Adina \n                and Dinan, Emily \n                and Bansal, Mohit \n                and Weston, Jason \n                and Kiela, Douwe},\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    year = \"2020\",\n    publisher = \"Association for Computational Linguistics\",\n}\n", "homepage": "https://github.com/facebookresearch/anli/", "license": "", "features": {"uid": {"dtype": "string", "id": null, "_type": "Value"}, "premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}, "reason": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "anli", "config_name": "plain_text", "version": {"version_str": "0.1.0", "description": "", "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train_r1": {"name": "train_r1", "num_bytes": 8006920, "num_examples": 16946, "dataset_name": "anli"}, "dev_r1": {"name": "dev_r1", "num_bytes": 573444, "num_examples": 1000, "dataset_name": "anli"}, "test_r1": {"name": "test_r1", "num_bytes": 574933, "num_examples": 1000, "dataset_name": "anli"}, "train_r2": {"name": "train_r2", "num_bytes": 20801661, "num_examples": 45460, "dataset_name": "anli"}, "dev_r2": {"name": "dev_r2", "num_bytes": 556082, "num_examples": 1000, "dataset_name": "anli"}, "test_r2": {"name": "test_r2", "num_bytes": 572655, "num_examples": 1000, "dataset_name": "anli"}, "train_r3": {"name": "train_r3", "num_bytes": 44720895, "num_examples": 100459, "dataset_name": "anli"}, "dev_r3": {"name": "dev_r3", "num_bytes": 663164, "num_examples": 1200, "dataset_name": "anli"}, "test_r3": {"name": "test_r3", "num_bytes": 657602, "num_examples": 1200, "dataset_name": "anli"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/anli/anli_v0.1.zip": {"num_bytes": 18621352, "checksum": "16ac929a7e90ecf9093deaec89cc81fe86a379265a5320a150028efe50c5cde8"}}, "download_size": 18621352, "dataset_size": 77127356, "size_in_bytes": 95748708}}

dummy/plain_text/0.1.0/dummy_data.zip

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+size 18710394