Update files from the datasets library (from 1.2.0)

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

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README.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- n>1M
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+task_ids:
+- summarization
+---
+
+# Dataset Card for Big Patent
+
+## Table of Contents
+
+- [Dataset Card for Big Patent](#dataset-card-for-big-patent)
+  - [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:[Big Patent](https://evasharma.github.io/bigpatent/)**
+- **Repository:**
+- **Paper:[BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741)**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Each US patent application is filed under a Cooperative Patent Classification (CPC) code. There are nine such classification categories:
+
+- A (Human Necessities)
+- B (Performing Operations; Transporting)
+- C (Chemistry; Metallurgy)
+- D (Textiles; Paper)
+- E (Fixed Constructions)
+- F (Mechanical Engineering; Lightning; Heating; Weapons; Blasting)
+- G (Physics)
+- H (Electricity)
+- Y (General tagging of new or cross-sectional technology)
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section.
+
+### Data Fields
+
+- `description`: detailed description of patent.
+- `abstract`: Patent abastract.
+
+### 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]

big_patent.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
+"""BigPatent Dataset."""
+import glob
+import gzip
+import json
+import os
+
+import datasets
+
+
+_CITATION = """
+@misc{sharma2019bigpatent,
+    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},
+    author={Eva Sharma and Chen Li and Lu Wang},
+    year={2019},
+    eprint={1906.03741},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """
+BIGPATENT, consisting of 1.3 million records of U.S. patent documents
+along with human written abstractive summaries.
+Each US patent application is filed under a Cooperative Patent Classification
+(CPC) code. There are nine such classification categories:
+A (Human Necessities), B (Performing Operations; Transporting),
+C (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),
+F (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),
+G (Physics), H (Electricity), and
+Y (General tagging of new or cross-sectional technology)
+There are two features:
+  - description: detailed description of patent.
+  - abstract: Patent abastract.
+"""
+
+_URL = "https://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa"
+
+_DOCUMENT = "description"
+_SUMMARY = "abstract"
+
+_CPC_DESCRIPTION = {
+    "a": "Human Necessities",
+    "b": "Performing Operations; Transporting",
+    "c": "Chemistry; Metallurgy",
+    "d": "Textiles; Paper",
+    "e": "Fixed Constructions",
+    "f": "Mechanical Engineering; Lightning; Heating; Weapons; Blasting",
+    "g": "Physics",
+    "h": "Electricity",
+    "y": "General tagging of new or cross-sectional technology",
+}
+
+# Available versions:
+# 1.0.0 lower cased tokenized words.
+# 2.0.0 cased raw strings.
+# 2.1.0 cased raw strings (fixed).
+# TODO Add raw string versions
+
+_VERSION = "1.0.0"
+
+
+class BigPatentConfig(datasets.BuilderConfig):
+    """BuilderConfig for BigPatent."""
+
+    def __init__(self, *args, cpc_codes=None, **kwargs):
+        """BuilderConfig for BigPatent.
+        Args:
+            cpc_codes: str, cpc_codes
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(*args, version=_VERSION, **kwargs)
+        self.cpc_codes = cpc_codes
+
+
+class BigPatent(datasets.GeneratorBasedBuilder):
+    """BigPatent datasets."""
+
+    BUILDER_CONFIGS = [
+        BigPatentConfig(
+            cpc_codes=list(_CPC_DESCRIPTION),
+            name="all",
+            description="Patents under all categories.",
+        ),
+    ] + [
+        BigPatentConfig(  # pylint:disable=g-complex-comprehension
+            cpc_codes=[k],
+            name=k,
+            description=("Patents under Cooperative Patent Classification (CPC)" "{0}: {1}".format(k, v)),
+        )
+        for k, v in sorted(_CPC_DESCRIPTION.items())
+    ]
+    DEFAULT_CONFIG_NAME = "all"
+    VERSION = _VERSION
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({_DOCUMENT: datasets.Value("string"), _SUMMARY: datasets.Value("string")}),
+            supervised_keys=(_DOCUMENT, _SUMMARY),
+            homepage="https://evasharma.github.io/bigpatent/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_path = dl_manager.download_and_extract(_URL)
+        split_types = ["train", "val", "test"]
+        extract_paths = dl_manager.extract(
+            {k: os.path.join(dl_path, "bigPatentData", k + ".tar.gz") for k in split_types}
+        )
+        extract_paths = {k: os.path.join(extract_paths[k], k) for k in split_types}
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"path": extract_paths["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"path": extract_paths["val"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"path": extract_paths["test"]},
+            ),
+        ]
+
+    def _generate_examples(self, path=None):
+        """Yields examples."""
+        for cpc_code in self.config.cpc_codes:
+            filenames = glob.glob(os.path.join(path, cpc_code, "*"))
+            for filename in sorted(filenames):
+                with open(filename, "rb") as fin:
+                    fin = gzip.GzipFile(fileobj=fin)
+                    for row in fin:
+                        json_obj = json.loads(row)
+                        yield json_obj["publication_number"], {
+                            _DOCUMENT: json_obj[_DOCUMENT],
+                            _SUMMARY: json_obj[_SUMMARY],
+                        }

dataset_infos.json

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+{"all": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - summary: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "builder_name": "big_patent", "config_name": "all", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 23363518650, "num_examples": 1207222, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 1290154487, "num_examples": 67068, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 1296234391, "num_examples": 67072, "dataset_name": "big_patent"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa": {"num_bytes": 6448045871, "checksum": "7e1093c7e0d09677c79bd872a07b6a6dd2b3235633207e9918b75056205f04dc"}}, "download_size": 6448045871, "post_processing_size": null, "dataset_size": 25949907528, "size_in_bytes": 32397953399}, "a": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - summary: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "builder_name": "big_patent", "config_name": "a", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3329778447, "num_examples": 174134, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 184116486, "num_examples": 9674, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 185987552, "num_examples": 9675, "dataset_name": "big_patent"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa": {"num_bytes": 6448045871, "checksum": "7e1093c7e0d09677c79bd872a07b6a6dd2b3235633207e9918b75056205f04dc"}}, "download_size": 6448045871, "post_processing_size": null, "dataset_size": 3699882485, "size_in_bytes": 10147928356}, "b": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - summary: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "builder_name": "big_patent", "config_name": "b", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2574594655, "num_examples": 161520, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 143029380, "num_examples": 8973, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 140741033, "num_examples": 8974, "dataset_name": "big_patent"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa": {"num_bytes": 6448045871, "checksum": "7e1093c7e0d09677c79bd872a07b6a6dd2b3235633207e9918b75056205f04dc"}}, "download_size": 6448045871, "post_processing_size": null, "dataset_size": 2858365068, "size_in_bytes": 9306410939}, "c": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - summary: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "builder_name": "big_patent", "config_name": "c", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2641973267, "num_examples": 101042, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 145441704, "num_examples": 5613, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 149052258, "num_examples": 5614, "dataset_name": "big_patent"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa": {"num_bytes": 6448045871, "checksum": "7e1093c7e0d09677c79bd872a07b6a6dd2b3235633207e9918b75056205f04dc"}}, "download_size": 6448045871, "post_processing_size": null, "dataset_size": 2936467229, "size_in_bytes": 9384513100}, "d": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - summary: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "builder_name": "big_patent", "config_name": "d", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 160467163, "num_examples": 10164, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 8667961, "num_examples": 565, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 8713720, "num_examples": 565, "dataset_name": "big_patent"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa": {"num_bytes": 6448045871, "checksum": "7e1093c7e0d09677c79bd872a07b6a6dd2b3235633207e9918b75056205f04dc"}}, "download_size": 6448045871, "post_processing_size": null, "dataset_size": 177848844, "size_in_bytes": 6625894715}, "e": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. 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