Update files from the datasets library (from 1.1.3)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.1.3

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.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

asnq.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""Answer-Sentence Natural Questions (ASNQ)
+
+ASNQ is a dataset for answer sentence selection derived from Google's
+Natural Questions (NQ) dataset (Kwiatkowski et al. 2019). It converts
+NQ's dataset into an AS2 (answer-sentence-selection) format.
+
+The dataset details can be found in the paper at
+https://arxiv.org/abs/1911.04118
+
+The dataset can be downloaded at
+https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar
+"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@article{garg2019tanda,
+    title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
+    author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},
+    year={2019},
+    eprint={1911.04118},
+}
+"""
+
+_DESCRIPTION = """\
+ASNQ is a dataset for answer sentence selection derived from
+Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).
+
+Each example contains a question, candidate sentence, label indicating whether or not
+the sentence answers the question, and two additional features --
+sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the
+candidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.
+
+For more details please see
+https://arxiv.org/pdf/1911.04118.pdf
+
+and
+
+https://research.google/pubs/pub47761/
+"""
+
+_URL = "https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar"
+
+
+class ASNQ(datasets.GeneratorBasedBuilder):
+    """ASNQ is a dataset for answer sentence selection derived
+    ASNQ is a dataset for answer sentence selection derived from
+    Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).
+
+    The dataset details can be found in the paper:
+    https://arxiv.org/abs/1911.04118
+    """
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(
+                {
+                    "question": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=["neg", "pos"]),
+                    "sentence_in_long_answer": datasets.Value("bool"),
+                    "short_answer_in_sentence": datasets.Value("bool"),
+                }
+            ),
+            # No default supervised_keys
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        dl_dir = dl_manager.download_and_extract(_URL)
+        data_dir = os.path.join(dl_dir, "data", "asnq")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.tsv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "dev.tsv"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples.
+
+        Original dataset contains labels '1', '2', '3' and '4', with labels
+        '1', '2' and '3' considered negative (sentence does not answer the question),
+        and label '4' considered positive (sentence does answer the question).
+        We map these labels to two classes, returning the other properties as additional
+        features."""
+
+        # Mapping of dataset's original labels to a tuple of
+        # (label, sentence_in_long_answer, short_answer_in_sentence)
+        label_map = {
+            "1": ("neg", False, False),
+            "2": ("neg", False, True),
+            "3": ("neg", True, False),
+            "4": ("pos", True, True),
+        }
+        with open(filepath, encoding="utf-8") as tsvfile:
+            tsvreader = csv.reader(tsvfile, delimiter="\t")
+            for id_, row in enumerate(tsvreader):
+                question, sentence, orig_label = row
+                label, sentence_in_long_answer, short_answer_in_sentence = label_map[orig_label]
+                yield id_, {
+                    "question": question,
+                    "sentence": sentence,
+                    "label": label,
+                    "sentence_in_long_answer": sentence_in_long_answer,
+                    "short_answer_in_sentence": short_answer_in_sentence,
+                }

dataset_infos.json

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+{"default": {"description": "ASNQ is a dataset for answer sentence selection derived from\nGoogle's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).\n\nEach example contains a question, candidate sentence, label indicating whether or not\nthe sentence answers the question, and two additional features -- \nsentence_in_long_answer and short_answer_in_sentence indicating whether ot not the \ncandidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.\n\nFor more details please see \nhttps://arxiv.org/pdf/1911.04118.pdf\n\nand \n\nhttps://research.google/pubs/pub47761/\n", "citation": "@article{garg2019tanda,\n    title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},\n    author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},\n    year={2019},\n    eprint={1911.04118},\n}\n", "homepage": "https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["neg", "pos"], "names_file": null, "id": null, "_type": "ClassLabel"}, "sentence_in_long_answer": {"dtype": "bool", "id": null, "_type": "Value"}, "short_answer_in_sentence": {"dtype": "bool", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "asnq", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3656881376, "num_examples": 20377568, "dataset_name": "asnq"}, "validation": {"name": "validation", "num_bytes": 168005155, "num_examples": 930062, "dataset_name": "asnq"}}, "download_checksums": {"https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar": {"num_bytes": 3563857920, "checksum": "4211d3e507e7cfa345a9eea3c5222b7d79fd963cf27407555c5558c37344ddf1"}}, "download_size": 3563857920, "post_processing_size": null, "dataset_size": 3824886531, "size_in_bytes": 7388744451}}

dummy/1.0.0/dummy_data.zip

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