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
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+*.bin.* 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:
+- cc-by-3-0-at
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets: []
+task_categories:
+- question-answering
+task_ids:
+- open-domain-qa
+---
+
+# Dataset Card Creation Guide
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [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://research.fb.com/downloads/babi/
+- **Repository:** https://github.com/fbougares/TSAC
+- **Paper:** https://research.fb.com/publications/large-scale-simple-question-answering-with-memory-networks/
+- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
+- **Point of Contact:** Antoine Bordes ([email protected]) Nicolas Usunier ([email protected]) Sumit Chopra ([email protected]), Jason Weston([email protected])
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+Here are some examples of questions and facts:
+
+* What American cartoonist is the creator of Andy Lippincott?
+  Fact: (andy_lippincott, character_created_by, garry_trudeau) 
+* Which forest is Fires Creek in?
+  Fact: (fires_creek, containedby, nantahala_national_forest)
+* What does Jimmy Neutron do?
+  Fact: (jimmy_neutron, fictional_character_occupation, inventor)
+* What dietary restriction is incompatible with kimchi?
+  Fact: (kimchi, incompatible_with_dietary_restrictions, veganism)
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### 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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+{"annotated": {"description": "SimpleQuestions is a dataset for simple QA, which consists\nof a total of 108,442 questions written in natural language by human\nEnglish-speaking annotators each paired with a corresponding fact,\nformatted as (subject, relationship, object), that provides the answer\nbut also a complete explanation.  Fast have been extracted from the\nKnowledge Base Freebase (freebase.com).  We randomly shuffle these\nquestions and use 70% of them (75910) as training set, 10% as\nvalidation set (10845), and the remaining 20% as test set.\n", "citation": "@misc{bordes2015largescale,\n      title={Large-scale Simple Question Answering with Memory Networks},\n      author={Antoine Bordes and Nicolas Usunier and Sumit Chopra and Jason Weston},\n      year={2015},\n      eprint={1506.02075},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n", "homepage": "https://research.fb.com/downloads/babi/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "subject_entity": {"dtype": "string", "id": null, "_type": "Value"}, "relationship": {"dtype": "string", "id": null, "_type": "Value"}, "object_entity": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "simple_questions_v2", "config_name": "annotated", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 12376039, "num_examples": 75910, "dataset_name": "simple_questions_v2"}, "validation": {"name": "validation", "num_bytes": 12376039, "num_examples": 75910, "dataset_name": "simple_questions_v2"}, "test": {"name": "test", "num_bytes": 12376039, "num_examples": 75910, "dataset_name": "simple_questions_v2"}}, "download_checksums": {"https://www.dropbox.com/s/tohrsllcfy7rch4/SimpleQuestions_v2.tgz?dl=1": {"num_bytes": 423435590, "checksum": "58f65630895de4f9712eeb33458ca20538972436fd48bf5913df4765e6788bf5"}}, "download_size": 423435590, "post_processing_size": null, "dataset_size": 37128117, "size_in_bytes": 460563707}, "freebase2m": {"description": "SimpleQuestions is a dataset for simple QA, which consists\nof a total of 108,442 questions written in natural language by human\nEnglish-speaking annotators each paired with a corresponding fact,\nformatted as (subject, relationship, object), that provides the answer\nbut also a complete explanation.  Fast have been extracted from the\nKnowledge Base Freebase (freebase.com).  We randomly shuffle these\nquestions and use 70% of them (75910) as training set, 10% as\nvalidation set (10845), and the remaining 20% as test set.\n", "citation": "@misc{bordes2015largescale,\n      title={Large-scale Simple Question Answering with Memory Networks},\n      author={Antoine Bordes and Nicolas Usunier and Sumit Chopra and Jason Weston},\n      year={2015},\n      eprint={1506.02075},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n", "homepage": "https://research.fb.com/downloads/babi/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "subject_entity": {"dtype": "string", "id": null, "_type": "Value"}, "relationship": {"dtype": "string", "id": null, "_type": "Value"}, "object_entities": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "simple_questions_v2", "config_name": "freebase2m", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1964037256, "num_examples": 10843106, "dataset_name": "simple_questions_v2"}}, "download_checksums": {"https://www.dropbox.com/s/tohrsllcfy7rch4/SimpleQuestions_v2.tgz?dl=1": {"num_bytes": 423435590, "checksum": "58f65630895de4f9712eeb33458ca20538972436fd48bf5913df4765e6788bf5"}}, "download_size": 423435590, "post_processing_size": null, "dataset_size": 1964037256, "size_in_bytes": 2387472846}, "freebase5m": {"description": "SimpleQuestions is a dataset for simple QA, which consists\nof a total of 108,442 questions written in natural language by human\nEnglish-speaking annotators each paired with a corresponding fact,\nformatted as (subject, relationship, object), that provides the answer\nbut also a complete explanation.  Fast have been extracted from the\nKnowledge Base Freebase (freebase.com).  We randomly shuffle these\nquestions and use 70% of them (75910) as training set, 10% as\nvalidation set (10845), and the remaining 20% as test set.\n", "citation": "@misc{bordes2015largescale,\n      title={Large-scale Simple Question Answering with Memory Networks},\n      author={Antoine Bordes and Nicolas Usunier and Sumit Chopra and Jason Weston},\n      year={2015},\n      eprint={1506.02075},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n", "homepage": "https://research.fb.com/downloads/babi/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "subject_entity": {"dtype": "string", "id": null, "_type": "Value"}, "relationship": {"dtype": "string", "id": null, "_type": "Value"}, "object_entities": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "simple_questions_v2", "config_name": "freebase5m", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2481753516, "num_examples": 12010500, "dataset_name": "simple_questions_v2"}}, "download_checksums": {"https://www.dropbox.com/s/tohrsllcfy7rch4/SimpleQuestions_v2.tgz?dl=1": {"num_bytes": 423435590, "checksum": "58f65630895de4f9712eeb33458ca20538972436fd48bf5913df4765e6788bf5"}}, "download_size": 423435590, "post_processing_size": null, "dataset_size": 2481753516, "size_in_bytes": 2905189106}}

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dummy/freebase2m/1.0.0/dummy_data.zip

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simple_questions_v2.py

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+# coding=utf-8
+# Copyright 2020 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
+import os
+
+import datasets
+
+
+_DESCRIPTION = """\
+SimpleQuestions is a dataset for simple QA, which consists
+of a total of 108,442 questions written in natural language by human
+English-speaking annotators each paired with a corresponding fact,
+formatted as (subject, relationship, object), that provides the answer
+but also a complete explanation.  Fast have been extracted from the
+Knowledge Base Freebase (freebase.com).  We randomly shuffle these
+questions and use 70% of them (75910) as training set, 10% as
+validation set (10845), and the remaining 20% as test set.
+"""
+_HOMEPAGE_URL = "https://research.fb.com/downloads/babi/"
+_CITATION = """\
+@misc{bordes2015largescale,
+      title={Large-scale Simple Question Answering with Memory Networks},
+      author={Antoine Bordes and Nicolas Usunier and Sumit Chopra and Jason Weston},
+      year={2015},
+      eprint={1506.02075},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+"""
+
+_URL = "https://www.dropbox.com/s/tohrsllcfy7rch4/SimpleQuestions_v2.tgz?dl=1"
+
+
+class SimpleQuestionsV2Config(datasets.BuilderConfig):
+    def __init__(self, *args, data_type=None, **kwargs):
+        super().__init__(*args, version=datasets.Version("1.0.0", ""), **kwargs)
+        self.data_type = data_type
+
+
+class SimpleQuestionsV2(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        SimpleQuestionsV2Config(name="annotated", data_type="annotated", description=f"Annotated dataset"),
+        SimpleQuestionsV2Config(name="freebase2m", data_type="freebase2m", description=f"Freebase subset 2M"),
+        SimpleQuestionsV2Config(name="freebase5m", data_type="freebase5m", description=f"Freebase subset 5M"),
+    ]
+    BUILDER_CONFIG_CLASS = SimpleQuestionsV2Config
+    DEFAULT_CONFIG_NAME = "annotated"
+
+    def _info(self):
+        if self.config.data_type == "annotated":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "subject_entity": datasets.Value("string"),
+                    "relationship": datasets.Value("string"),
+                    "object_entity": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                },
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "subject_entity": datasets.Value("string"),
+                    "relationship": datasets.Value("string"),
+                    "object_entities": datasets.Sequence(datasets.Value("string")),
+                },
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download_and_extract(_URL)
+        if self.config.data_type == "annotated":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"datapath": os.path.join(path, "SimpleQuestions_v2", "annotated_fb_data_train.txt")},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={"datapath": os.path.join(path, "SimpleQuestions_v2", "annotated_fb_data_train.txt")},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"datapath": os.path.join(path, "SimpleQuestions_v2", "annotated_fb_data_train.txt")},
+                ),
+            ]
+        elif self.config.data_type == "freebase2m":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "datapath": os.path.join(
+                            path,
+                            "SimpleQuestions_v2",
+                            "freebase-subsets",
+                            "freebase-FB2M.txt",
+                        )
+                    },
+                )
+            ]
+        elif self.config.data_type == "freebase5m":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "datapath": os.path.join(
+                            path,
+                            "SimpleQuestions_v2",
+                            "freebase-subsets",
+                            "freebase-FB5M.txt",
+                        )
+                    },
+                )
+            ]
+        else:
+            raise Exception("Unknown data type. Try one of: annotated, freebase2m and freebase5m")
+
+    def _generate_examples(self, datapath):
+        if self.config.data_type == "annotated":
+            with open(datapath, encoding="utf-8") as f:
+                for sentence_counter, row in enumerate(f):
+                    row = row.split("\t")
+                    result = (
+                        sentence_counter,
+                        {
+                            "id": str(sentence_counter),
+                            "subject_entity": row[0],
+                            "relationship": row[1],
+                            "object_entity": row[2],
+                            "question": row[3],
+                        },
+                    )
+                    yield result
+        else:
+            with open(datapath, encoding="utf-8") as f:
+                for sentence_counter, row in enumerate(f):
+                    row = row.split("\t")
+                    result = (
+                        sentence_counter,
+                        {
+                            "id": str(sentence_counter),
+                            "subject_entity": row[0],
+                            "relationship": row[1],
+                            "object_entities": row[2].split(),
+                        },
+                    )
+                    yield result