Update files from the datasets library (from 1.2.0)

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

.gitattributes

@@ -0,0 +1,27 @@
+*.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

README.md

@@ -0,0 +1,156 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- cc-by-3-0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- closed-domain-qa
+---
+
+
+# Dataset Card for WikiMovies
+
+## 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: [WikiMovies Homepage](https://research.fb.com/downloads/babi/)**
+- **Repository:**
+- **Paper: [Key-Value Memory Networks for Directly Reading Documents](https://arxiv.org/pdf/1606.03126.pdf)**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The WikiMovies dataset consists of roughly 100k (templated) questions over 75k entitiesbased on questions with answers in the open movie database (OMDb). It is the QA part of the Movie Dialog dataset.
+
+### Supported Tasks and Leaderboards
+
+- Question Answering
+
+### Languages
+
+The text in the dataset is written in English.
+
+## Dataset Structure
+
+### Data Instances
+
+The raw data consists of question answer pairs separated by a tab. Here are 3 examples:
+```buildoutcfg
+1 what does Grégoire Colin appear in?	Before the Rain
+1 Joe Thomas appears in which movies?	The Inbetweeners Movie, The Inbetweeners 2
+1 what films did Michelle Trachtenberg star in?	Inspector Gadget, Black Christmas, Ice Princess, Harriet the Spy, The Scribbler
+```
+It is unclear what the `1` is for at the beginning of each line, but it has been removed in the `Dataset` object.
+
+### Data Fields
+Here is an example of the raw data ingested by `Datasets`:
+```buildoutcfg
+{
+'answer': 'Before the Rain', 
+'question': 'what does Grégoire Colin appear in?'
+}
+```
+`answer`: a string containing the answer to a corresponding question.
+`question`: a string containing the relevant question.
+
+### Data Splits
+The data is split into train, test, and dev sets. The split sizes are as follows:
+
+| wiki-entities_qa_* | n examples|
+| -----              | ----      |
+| train.txt          | 96185     |
+| dev.txt            | 10000     |
+| test.txt           | 9952      | 
+
+## Dataset Creation
+
+### Curation Rationale
+
+WikiMovies was built with the following goals in mind: (i) machine learning techniques should have ample training examples for learning; and (ii) one can analyze easily the performance of different representations of knowledge and break down the results by question type. The datasetcan be downloaded fromhttp://fb.ai/babi
+
+### 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

@@ -0,0 +1 @@
+{"default": {"description": "The WikiMovies dataset consists of roughly 100k (templated) questions over 75k entities based on questions with answers in the open movie database (OMDb).\n", "citation": "@misc{miller2016keyvalue,\n      title={Key-Value Memory Networks for Directly Reading Documents},\n      author={Alexander Miller and Adam Fisch and Jesse Dodge and Amir-Hossein Karimi and Antoine Bordes and Jason Weston},\n      year={2016},\n      eprint={1606.03126},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}", "homepage": "https://research.fb.com/downloads/babi/", "license": "Creative Commons Public License (CCPL)", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "wiki_movies", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7274490, "num_examples": 96185, "dataset_name": "wiki_movies"}, "test": {"name": "test", "num_bytes": 755258, "num_examples": 9952, "dataset_name": "wiki_movies"}, "validation": {"name": "validation", "num_bytes": 754755, "num_examples": 10000, "dataset_name": "wiki_movies"}}, "download_checksums": {"https://thespermwhale.com/jaseweston/babi/movieqa.tar.gz": {"num_bytes": 57070041, "checksum": "ed062b49922b602ebee6073f58951bf38c4772a8b53d46682f3ff80ed57de948"}}, "download_size": 57070041, "post_processing_size": null, "dataset_size": 8784503, "size_in_bytes": 65854544}}

dummy/1.1.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e15b615a17084236994a7466f9292fde2e4100636d35124f4ec59ec9c414ecaa
+size 39466

wiki_movies.py

@@ -0,0 +1,134 @@
+# 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.
+"""WikiMovies: A Question-Answering dataset that contains raw text alongside a preprocessed knowledge base, in the domain of movies from the Open Movie Database. It is the QA part of the Movie Dialog dataset.
+It was built with the following goals in mind: (i) machine learning techniques should have ample training examples for learning; and (ii) one can analyze easily the performance of different representations of knowledge and break down the results by question type. The dataset can be downloaded fromhttp://fb.ai/babi
+"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+_CITATION = """\
+@misc{miller2016keyvalue,
+      title={Key-Value Memory Networks for Directly Reading Documents},
+      author={Alexander Miller and Adam Fisch and Jesse Dodge and Amir-Hossein Karimi and Antoine Bordes and Jason Weston},
+      year={2016},
+      eprint={1606.03126},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}"""
+
+_DESCRIPTION = """\
+The WikiMovies dataset consists of roughly 100k (templated) questions over 75k entities based on questions with answers in the open movie database (OMDb).
+"""
+
+_HOMEPAGE = "https://research.fb.com/downloads/babi/"
+
+_LICENSE = "Creative Commons Public License (CCPL)"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLs = {"default": "https://thespermwhale.com/jaseweston/babi/movieqa.tar.gz"}
+
+
+class WikiMovies(datasets.GeneratorBasedBuilder):
+    """The WikiMovies dataset consists of roughly 100k (templated) questions over 75k entities based on questions with answers in the open movie database (OMDb)."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "question": datasets.Value("string"),
+                "answer": datasets.Value("string"),
+                # These are the features of your dataset like images, labels ...
+            }
+        )
+        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=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        my_urls = _URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "movieqa", "questions", "wiki_entities", "wiki-entities_qa_train.txt"
+                    ),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "movieqa", "questions", "wiki_entities", "wiki-entities_qa_test.txt"
+                    ),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "movieqa", "questions", "wiki_entities", "wiki-entities_qa_dev.txt"
+                    ),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
+        # The key is not important, it's more here for legacy reason (legacy from tfds)
+
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                tmp_data = row.split("\t")
+                tmp_question = tmp_data[0][1:]
+                yield id_, {
+                    "question": tmp_question,
+                    "answer": tmp_data[1],
+                }