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

dataset_infos.json

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+{"default": {"description": "\nEraser Multi RC is a dataset for queries over multi-line passages, along with\nanswers and a rationalte. Each example in this dataset has the following 5 parts\n1. A Mutli-line Passage\n2. A Query about the passage\n3. An Answer to the query\n4. A Classification as to whether the answer is right or wrong\n5. An Explanation justifying the classification\n", "citation": "\n@unpublished{eraser2019,\n    title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},\n    author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}\n}\n@inproceedings{MultiRC2018,\n    author = {Daniel Khashabi and Snigdha Chaturvedi and Michael Roth and Shyam Upadhyay and Dan Roth},\n    title = {Looking Beyond the Surface:A Challenge Set for Reading Comprehension over Multiple Sentences},\n    booktitle = {NAACL},\n    year = {2018}\n}\n", "homepage": "https://cogcomp.seas.upenn.edu/multirc/", "license": "", "features": {"passage": {"dtype": "string", "id": null, "_type": "Value"}, "query_and_answer": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["False", "True"], "names_file": null, "id": null, "_type": "ClassLabel"}, "evidences": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "eraser_multi_rc", "config_name": "default", "version": {"version_str": "0.1.1", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 1}, "splits": {"test": {"name": "test", "num_bytes": 9194475, "num_examples": 4848, "dataset_name": "eraser_multi_rc"}, "train": {"name": "train", "num_bytes": 47922877, "num_examples": 24029, "dataset_name": "eraser_multi_rc"}, "validation": {"name": "validation", "num_bytes": 6529020, "num_examples": 3214, "dataset_name": "eraser_multi_rc"}}, "download_checksums": {"http://www.eraserbenchmark.com/zipped/multirc.tar.gz": {"num_bytes": 1667550, "checksum": "7d3364fad630d0949b40cc5c4b9fd7fac0ec9ebb3a31ffa92a491e3883edd826"}}, "download_size": 1667550, "dataset_size": 63646372, "size_in_bytes": 65313922}}

dummy/0.1.1/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6586ebece48eaf697b40cf5a630c45080bcd499649b7d4aaae216e327e16c194
+size 8216

eraser_multi_rc.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
+"""Passage, query, answers and answer classification with explanations."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """
+@unpublished{eraser2019,
+    title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
+    author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
+}
+@inproceedings{MultiRC2018,
+    author = {Daniel Khashabi and Snigdha Chaturvedi and Michael Roth and Shyam Upadhyay and Dan Roth},
+    title = {Looking Beyond the Surface:A Challenge Set for Reading Comprehension over Multiple Sentences},
+    booktitle = {NAACL},
+    year = {2018}
+}
+"""
+
+_DESCRIPTION = """
+Eraser Multi RC is a dataset for queries over multi-line passages, along with
+answers and a rationalte. Each example in this dataset has the following 5 parts
+1. A Mutli-line Passage
+2. A Query about the passage
+3. An Answer to the query
+4. A Classification as to whether the answer is right or wrong
+5. An Explanation justifying the classification
+"""
+
+_DOWNLOAD_URL = "http://www.eraserbenchmark.com/zipped/multirc.tar.gz"
+
+
+class EraserMultiRc(datasets.GeneratorBasedBuilder):
+    """Multi Sentence Reasoning with Explanations (Eraser Benchmark)."""
+
+    VERSION = datasets.Version("0.1.1")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "passage": datasets.Value("string"),
+                    "query_and_answer": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["False", "True"]),
+                    "evidences": datasets.features.Sequence(datasets.Value("string")),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://cogcomp.seas.upenn.edu/multirc/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
+        data_dir = os.path.join(dl_dir, "multirc")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "train.jsonl")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "val.jsonl")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "test.jsonl")},
+            ),
+        ]
+
+    def _generate_examples(self, data_dir, filepath):
+        """Yields examples."""
+
+        multirc_dir = os.path.join(data_dir, "docs")
+        with open(filepath, encoding="utf-8") as f:
+            for line in f:
+                row = json.loads(line)
+                evidences = []
+
+                for evidence in row["evidences"][0]:
+                    docid = evidence["docid"]
+                    evidences.append(evidence["text"])
+
+                passage_file = os.path.join(multirc_dir, docid)
+                with open(passage_file, encoding="utf-8") as f1:
+                    passage_text = f1.read()
+
+                yield row["annotation_id"], {
+                    "passage": passage_text,
+                    "query_and_answer": row["query"],
+                    "label": row["classification"],
+                    "evidences": evidences,
+                }