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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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+languages: []
+licenses:
+- unknown
+multilinguality: []
+size_categories:
+- n<1K
+source_datasets:
+- extended|other-80-Million-Tiny-Images
+task_categories:
+- other
+task_ids:
+- other-other-image-classification
+---
+
+# Dataset Card for CIFAR-10
+
+## 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://www.cs.toronto.edu/~kriz/cifar.html
+- **Repository:** 
+- **Paper:** Learning Multiple Layers of Features from Tiny Images by Alex Krizhevsky
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
+The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+- img:    32x32x3 image
+- label:  0-9 with the following correspondence
+          0 airplane 
+          1 automobile 
+          2 bird 
+          3 cat 
+          4 deer
+          5 dog
+          6 frog
+          7 horse
+          8 ship
+          9 truck
+
+### Data Splits
+
+Train and Test
+
+## 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
+
+```
+@TECHREPORT{Krizhevsky09learningmultiple,
+    author = {Alex Krizhevsky},
+    title = {Learning multiple layers of features from tiny images},
+    institution = {},
+    year = {2009}
+}
+```

cifar10.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
+"""CIFAR-10 Data Set"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+import pickle
+
+import numpy as np
+
+import datasets
+
+
+_CITATION = """\
+@TECHREPORT{Krizhevsky09learningmultiple,
+    author = {Alex Krizhevsky},
+    title = {Learning multiple layers of features from tiny images},
+    institution = {},
+    year = {2009}
+}
+"""
+
+_DESCRIPTION = """\
+The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images
+per class. There are 50000 training images and 10000 test images.
+"""
+
+_DATA_URL = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+
+
+class Cifar10(datasets.GeneratorBasedBuilder):
+    """CIFAR-10 Data Set"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text import of CIFAR-10 Data Set",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "img": datasets.Array3D(shape=(32, 32, 3), dtype="uint8"),
+                    "label": datasets.features.ClassLabel(
+                        names=[
+                            "airplane",
+                            "automobile",
+                            "bird",
+                            "cat",
+                            "deer",
+                            "dog",
+                            "frog",
+                            "horse",
+                            "ship",
+                            "truck",
+                        ]
+                    ),
+                }
+            ),
+            supervised_keys=("img", "label"),
+            homepage="https://www.cs.toronto.edu/~kriz/cifar.html",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_dir = dl_manager.download_and_extract(_DATA_URL)
+        final_dir = os.path.join(dl_dir, "cifar-10-batches-py")
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": final_dir, "split": "train"}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": final_dir, "split": "test"}),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """This function returns the examples in the raw (text) form."""
+
+        if split == "train":
+            batches = ["data_batch_1", "data_batch_2", "data_batch_3", "data_batch_4", "data_batch_5"]
+
+        if split == "test":
+            batches = ["test_batch"]
+
+        for batch in batches:
+
+            file = os.path.join(filepath, batch)
+
+            with open(file, "rb") as fo:
+
+                dict = pickle.load(fo, encoding="bytes")
+
+                labels = dict[b"labels"]
+                images = dict[b"data"]
+
+                for idx, _ in enumerate(images):
+
+                    img_reshaped = np.transpose(np.reshape(images[idx], (3, 32, 32)), (1, 2, 0))
+
+                    yield idx, {
+                        "img": img_reshaped,
+                        "label": labels[idx],
+                    }

dataset_infos.json

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+{"plain_text": {"description": "The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images\nper class. There are 50000 training images and 10000 test images.\n", "citation": "@TECHREPORT{Krizhevsky09learningmultiple,\n    author = {Alex Krizhevsky},\n    title = {Learning multiple layers of features from tiny images},\n    institution = {},\n    year = {2009}\n}\n", "homepage": "https://www.cs.toronto.edu/~kriz/cifar.html", "license": "", "features": {"img": {"shape": [32, 32, 3], "dtype": "uint8", "id": null, "_type": "Array3D"}, "label": {"num_classes": 10, "names": ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "img", "output": "label"}, "builder_name": "cifar10", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 365400060, "num_examples": 50000, "dataset_name": "cifar10"}, "test": {"name": "test", "num_bytes": 73080012, "num_examples": 10000, "dataset_name": "cifar10"}}, "download_checksums": {"https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz": {"num_bytes": 170498071, "checksum": "6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce"}}, "download_size": 170498071, "post_processing_size": null, "dataset_size": 438480072, "size_in_bytes": 608978143}}

dummy/plain_text/1.0.0/dummy_data.zip

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