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README.md

@@ -1,406 +1,2 @@
----
-annotations_creators:
-- no-annotation
-language_creators:
-- found
-language:
-- en
-license:
-- cc-by-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 100K<n<1M
-- 10K<n<100K
-- 1M<n<10M
-source_datasets:
-- original
-task_categories:
-- summarization
-task_ids: []
-paperswithcode_id: bigpatent
-pretty_name: Big Patent
-tags:
-- patent-summarization
-dataset_info:
-- config_name: all
-  features:
-  - name: description
-    dtype: string
-  - name: abstract
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 38367048389
-    num_examples: 1207222
-  - name: validation
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-    num_examples: 67068
-  - name: test
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-    num_examples: 67072
-  download_size: 10142923776
-  dataset_size: 42612380671
-- config_name: a
-  features:
-  - name: description
-    dtype: string
-  - name: abstract
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 5683460620
-    num_examples: 174134
-  - name: validation
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-    num_examples: 9674
-  - name: test
-    num_bytes: 316633277
-    num_examples: 9675
-  download_size: 10142923776
-  dataset_size: 6313418402
-- config_name: b
-  features:
-  - name: description
-    dtype: string
-  - name: abstract
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 4236070976
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-  - name: validation
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-    num_examples: 8973
-  - name: test
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-    num_examples: 8974
-  download_size: 10142923776
-  dataset_size: 4702034848
-- config_name: c
-  features:
-  - name: description
-    dtype: string
-  - name: abstract
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 4506249306
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-    num_examples: 5613
-  - name: test
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-    num_examples: 5614
-  download_size: 10142923776
-  dataset_size: 5003500874
-- config_name: d
-  features:
-  - name: description
-    dtype: string
-  - name: abstract
-    dtype: string
-  splits:
-  - name: train
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-  - name: validation
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-    num_examples: 565
-  - name: test
-    num_bytes: 14403430
-    num_examples: 565
-  download_size: 10142923776
-  dataset_size: 293681324
-- config_name: e
-  features:
-  - name: description
-    dtype: string
-  - name: abstract
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 881101433
-    num_examples: 34443
-  - name: validation
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-    num_examples: 1914
-  - name: test
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-    num_examples: 1914
-  download_size: 10142923776
-  dataset_size: 978334020
-- config_name: f
-  features:
-  - name: description
-    dtype: string
-  - name: abstract
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2146383473
-    num_examples: 85568
-  - name: validation
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-    num_examples: 4754
-  - name: test
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-    num_examples: 4754
-  download_size: 10142923776
-  dataset_size: 2385612407
-- config_name: g
-  features:
-  - name: description
-    dtype: string
-  - name: abstract
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 8877854206
-    num_examples: 258935
-  - name: validation
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-    num_examples: 14385
-  - name: test
-    num_bytes: 496324853
-    num_examples: 14386
-  download_size: 10142923776
-  dataset_size: 9866760236
-- config_name: h
-  features:
-  - name: description
-    dtype: string
-  - name: abstract
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 8075621958
-    num_examples: 257019
-  - name: validation
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-    num_examples: 14279
-  - name: test
-    num_bytes: 445460513
-    num_examples: 14279
-  download_size: 10142923776
-  dataset_size: 8968684827
-- config_name: y
-  features:
-  - name: description
-    dtype: string
-  - name: abstract
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 3695589005
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-  - name: validation
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-  - name: test
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-    num_examples: 6911
-  download_size: 10142923776
-  dataset_size: 4100353733
-config_names:
-- a
-- all
-- b
-- c
-- d
-- e
-- f
-- g
-- h
-- y
----
 # Sampled big_patent Dataset
-This is a sampled big_patent dataset containing 5000 train and 500 test rows.
-
-The original repo card follows below.
-
-# Dataset Card for Big Patent
-
-## 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)
-  - [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)
-  - [Contributions](#contributions)
-
-## 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:** [Lu Wang](mailto:[email protected])
-
-### 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
-
-Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes:
-```python
-from datasets import load_dataset
-ds = load_dataset("big_patent")  # default is 'all' CPC codes
-ds = load_dataset("big_patent", "all")  # the same as above
-ds = load_dataset("big_patent", "a")  # only 'a' CPC codes
-ds = load_dataset("big_patent", codes=["a", "b"])
-```
-
-To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`:
-```python
-ds = load_dataset("big_patent", codes="all", version="1.0.0")
-ds = load_dataset("big_patent", codes="a", version="1.0.0")
-ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0")
-```
-
-
-### 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.
-```
-{
-  'description': 'FIELD OF THE INVENTION  \n       [0001]     This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...',
-  'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...'
-}
-```
-
-### Data Fields
-
-- `description`: detailed description of patent.
-- `abstract`: Patent abastract.
-
-### Data Splits
-
-|     |             train |   validation |   test |
-|:----|------------------:|-------------:|-------:|
-| all | 1207222           |        67068 |  67072 |
-| a   |  174134           |         9674 |   9675 |
-| b   |  161520           |         8973 |   8974 |
-| c   |  101042           |         5613 |   5614 |
-| d   |   10164           |          565 |    565 |
-| e   |   34443           |         1914 |   1914 |
-| f   |   85568           |         4754 |   4754 |
-| g   |  258935           |        14385 |  14386 |
-| h   |  257019           |        14279 |  14279 |
-| y   |  124397           |         6911 |   6911 |
-
-## 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
-
-```bibtex
-@article{DBLP:journals/corr/abs-1906-03741,
-  author    = {Eva Sharma and
-               Chen Li and
-               Lu Wang},
-  title     = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent
-               Summarization},
-  journal   = {CoRR},
-  volume    = {abs/1906.03741},
-  year      = {2019},
-  url       = {http://arxiv.org/abs/1906.03741},
-  eprinttype = {arXiv},
-  eprint    = {1906.03741},
-  timestamp = {Wed, 26 Jun 2019 07:14:58 +0200},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-```
-
-### Contributions
-
-Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.
+This is a sampled big_patent dataset containing 5000 train and 500 test rows.