Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.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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+*.gz filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- machine-generated
+language_creators:
+- found
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- other
+task_ids:
+- other-other-sentence-compression
+---
+
+# Dataset Card for Google Sentence Compression
+
+## Table of Contents
+
+- [Dataset Card for Google Sentence Compression](#dataset-card-for-google-sentence-compression)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [Personal and Sensitive Information](#personal-and-sensitive-information)
+  - [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+    - [Discussion of Biases](#discussion-of-biases)
+    - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:[https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression)**
+- **Repository:[https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression)**
+- **Paper:[https://www.aclweb.org/anthology/D13-1155/](https://www.aclweb.org/anthology/D13-1155/)**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+A major challenge in supervised sentence compression is making use of rich feature representations because of very scarce parallel data. We address this problem and present a method to automatically build a compression corpus with hundreds of thousands of instances on which deletion-based algorithms can be trained. In our corpus, the syntactic trees of the compressions are subtrees of their uncompressed counterparts, and hence supervised systems which require a structural alignment between the input and output can be successfully trained. We also extend an existing unsupervised compression method with a learning module. The new system uses structured prediction to learn from lexical, syntactic and other features. An evaluation with human raters shows that the presented data harvesting method indeed produces a parallel corpus of high quality. Also, the supervised system trained on this corpus gets high scores both from human raters and in an automatic evaluation setting, significantly outperforming a strong baseline.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+Each data instance should contains the information about the original sentence in `instance["graph"]["sentence"]` as well as the compressed sentence in `instance["compression"]["text"]`. As this dataset was created by pruning dependency connections, the author also includes the dependency tree and transformed graph of the original sentence and compressed sentence.
+
+### Data Fields
+
+Each instance should contains these information:
+
+- `graph` (`Dict`): the transformation graph/tree for extracting compression (a modified version of a dependency tree).
+  - This will have features similar to a dependency tree (listed bellow)
+- `compression` (`Dict`)
+  - `text` (`str`)
+  - `edge` (`List`)
+- `headline` (`str`): the headline of the original news page.
+- `compression_ratio` (`float`): the ratio between compressed sentence vs original sentence.
+- `doc_id` (`str`): url of the original news page.
+- `source_tree` (`Dict`): the original dependency tree (features listed bellow).
+- `compression_untransformed` (`Dict`)
+  - `text` (`str`)
+  - `edge` (`List`)
+
+Dependency tree features:
+
+- `id` (`str`)
+- `sentence` (`str`)
+- `node` (`List`): list of nodes, each node represent a word/word phrase in the tree.
+  - `form` (`string`)
+  - `type` (`string`): the enity type of a node. Defaults to `""` if it's not an entity.
+  - `mid` (`string`)
+  - `word` (`List`): list of words the node contains.
+    - `id` (`int`)
+    - `form` (`str`): the word from the sentence.
+    - `stem` (`str`): the stemmed/lemmatized version of the word.
+    - `tag` (`str`): dependency tag of the word.
+  - `gender` (`int`)
+  - `head_word_index` (`int`)
+- `edge`: list of the dependency connections between words.
+  - `parent_id` (`int`)
+  - `child_id` (`int`)
+  - `label` (`str`)
+- `entity_mention` list of the entities in the sentence.
+  - `start` (`int`)
+  - `end` (`int`)
+  - `head` (`str`)
+  - `name` (`str`)
+  - `type` (`str`)
+  - `mid` (`str`)
+  - `is_proper_name_entity` (`bool`)
+  - `gender` (`int`)
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"default": {"description": "Large corpus of uncompressed and compressed sentences from news articles.\n", "citation": "@inproceedings{filippova-altun-2013-overcoming,\n    title = \"Overcoming the Lack of Parallel Data in Sentence Compression\",\n    author = \"Filippova, Katja  and\n      Altun, Yasemin\",\n    booktitle = \"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing\",\n    month = oct,\n    year = \"2013\",\n    address = \"Seattle, Washington, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D13-1155\",\n    pages = \"1481--1491\",\n}\n", "homepage": "https://github.com/google-research-datasets/sentence-compression", "license": "", "features": {"graph": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "node": {"feature": {"form": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "mid": {"dtype": "string", "id": null, "_type": "Value"}, "word": {"feature": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "form": {"dtype": "string", "id": null, "_type": "Value"}, "stem": {"dtype": "string", "id": null, "_type": "Value"}, "tag": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "gender": {"dtype": "int32", "id": null, "_type": "Value"}, "head_word_index": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "edge": {"feature": {"parent_id": {"dtype": "int32", "id": null, "_type": "Value"}, "child_id": {"dtype": "int32", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "entity_mention": {"feature": {"start": {"dtype": "int32", "id": null, "_type": "Value"}, "end": {"dtype": "int32", "id": null, "_type": "Value"}, "head": {"dtype": "int32", "id": null, "_type": "Value"}, "name": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "mid": {"dtype": "string", "id": null, "_type": "Value"}, "is_proper_name_entity": {"dtype": "bool", "id": null, "_type": "Value"}, "gender": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "compression": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "edge": {"feature": {"parent_id": {"dtype": "int32", "id": null, "_type": "Value"}, "child_id": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "headline": {"dtype": "string", "id": null, "_type": "Value"}, "compression_ratio": {"dtype": "float32", "id": null, "_type": "Value"}, "doc_id": {"dtype": "string", "id": null, "_type": "Value"}, "source_tree": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "node": {"feature": {"form": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "mid": {"dtype": "string", "id": null, "_type": "Value"}, "word": {"feature": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "form": {"dtype": "string", "id": null, "_type": "Value"}, "stem": {"dtype": "string", "id": null, "_type": "Value"}, "tag": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "gender": {"dtype": "int32", "id": null, "_type": "Value"}, "head_word_index": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "edge": {"feature": {"parent_id": {"dtype": "int32", "id": null, "_type": "Value"}, "child_id": {"dtype": "int32", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "entity_mention": {"feature": {"start": {"dtype": "int32", "id": null, "_type": "Value"}, "end": {"dtype": "int32", "id": null, "_type": "Value"}, "head": {"dtype": "int32", "id": null, "_type": "Value"}, "name": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "mid": {"dtype": "string", "id": null, "_type": "Value"}, "is_proper_name_entity": {"dtype": "bool", "id": null, "_type": "Value"}, "gender": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "compression_untransformed": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "edge": {"feature": {"parent_id": {"dtype": "int32", "id": null, "_type": "Value"}, "child_id": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}}, "post_processed": null, "supervised_keys": null, "builder_name": "sent_comp", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": 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dummy/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:783e0810f87fb1975f87dd2bcac93b4b72ab1a9c5512f85a4bc1186e0178f1b8
+size 6103

sent_comp.py

@@ -0,0 +1,155 @@
+# 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.
+"""Google Sentence Compression dataset"""
+
+from __future__ import absolute_import, division, print_function
+
+import gzip
+import json
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{filippova-altun-2013-overcoming,
+    title = "Overcoming the Lack of Parallel Data in Sentence Compression",
+    author = "Filippova, Katja  and
+      Altun, Yasemin",
+    booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
+    month = oct,
+    year = "2013",
+    address = "Seattle, Washington, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D13-1155",
+    pages = "1481--1491",
+}
+"""
+
+_DESCRIPTION = """\
+Large corpus of uncompressed and compressed sentences from news articles.
+"""
+
+_HOMEPAGE = "https://github.com/google-research-datasets/sentence-compression"
+
+
+_URLs = {
+    datasets.Split.VALIDATION: [
+        "https://github.com/google-research-datasets/sentence-compression/raw/master/data/comp-data.eval.json.gz"
+    ],
+    datasets.Split.TRAIN: [
+        f"https://github.com/google-research-datasets/sentence-compression/raw/master/data/sent-comp.train{str(i).zfill(2)}.json.gz"
+        for i in range(1, 11)
+    ],
+}
+
+
+class SentComp(datasets.GeneratorBasedBuilder):
+    """Google Setence Compression dataset"""
+
+    def _info(self):
+        node_features = {
+            "form": datasets.Value("string"),
+            "type": datasets.Value("string"),
+            "mid": datasets.Value("string"),
+            "word": datasets.features.Sequence(
+                {
+                    "id": datasets.Value("int32"),
+                    "form": datasets.Value("string"),
+                    "stem": datasets.Value("string"),
+                    "tag": datasets.Value("string"),
+                }
+            ),
+            "gender": datasets.Value("int32"),
+            "head_word_index": datasets.Value("int32"),
+        }
+        compression_edge_features = {
+            "parent_id": datasets.Value("int32"),
+            "child_id": datasets.Value("int32"),
+        }
+        edge_features = {**compression_edge_features, "label": datasets.Value("string")}
+        entity_features = {
+            "start": datasets.Value("int32"),
+            "end": datasets.Value("int32"),
+            "head": datasets.Value("int32"),
+            "name": datasets.Value("string"),
+            "type": datasets.Value("string"),
+            "mid": datasets.Value("string"),
+            "is_proper_name_entity": datasets.Value("bool"),
+            "gender": datasets.Value("int32"),
+        }
+        tree_features = {
+            "id": datasets.Value("string"),
+            "sentence": datasets.Value("string"),
+            "node": datasets.features.Sequence(node_features),
+            "edge": datasets.features.Sequence(edge_features),
+            "entity_mention": datasets.features.Sequence(entity_features),
+        }
+        compression_features = {
+            "text": datasets.Value("string"),
+            "edge": datasets.features.Sequence(compression_edge_features),
+        }
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "graph": tree_features,
+                    "compression": compression_features,
+                    "headline": datasets.Value("string"),
+                    "compression_ratio": datasets.Value("float"),
+                    "doc_id": datasets.Value("string"),
+                    "source_tree": tree_features,
+                    "compression_untransformed": compression_features,
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepaths": dl_manager.download(_URLs[split])},
+            )
+            for split in _URLs
+        ]
+
+    def _generate_examples(self, filepaths):
+        """ Yields examples. """
+        id_ = -1
+        for ix, filepath in enumerate(filepaths):
+            with gzip.open(filepath, mode="rt", encoding="utf-8") as f:
+                all_text = f.read()
+
+            # in the data file, it's in the form of JSON objects, separated with '\n\n' characters
+            # we'll format the file to be able to read with json package
+            all_text = "[" + all_text + "]"
+            all_text = all_text.replace("}\n\n{", "},\n{")
+
+            samples = json.loads(all_text)
+            for sample in samples:
+                # add some default values
+                for node in sample["graph"]["node"] + sample["source_tree"]["node"]:
+                    if "type" not in node:
+                        node["type"] = ""
+                    if "mid" not in node:
+                        node["mid"] = ""
+
+                id_ += 1
+                yield id_, sample