Hugging Face's logo

Datasets:
NortheasternUniversity
/
big_patent

Tasks:
Summarization
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
patent-summarization
License:
Dataset card Files Community
5
big_patent / README.md
lhoestq's picture
lhoestq HF staff
rename configs to config_name
cc72d53
preview code
|
raw
history blame contribute delete
9.71 kB
metadata
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
        num_bytes: 2115827002
        num_examples: 67068
      - name: test
        num_bytes: 2129505280
        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
        num_bytes: 313324505
        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
        num_examples: 161520
      - name: validation
        num_bytes: 234425138
        num_examples: 8973
      - name: test
        num_bytes: 231538734
        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
        num_examples: 101042
      - name: validation
        num_bytes: 244684775
        num_examples: 5613
      - name: test
        num_bytes: 252566793
        num_examples: 5614
    download_size: 10142923776
    dataset_size: 5003500874
  - config_name: d
    features:
      - name: description
        dtype: string
      - name: abstract
        dtype: string
    splits:
      - name: train
        num_bytes: 264717412
        num_examples: 10164
      - name: validation
        num_bytes: 14560482
        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
        num_bytes: 48646158
        num_examples: 1914
      - name: test
        num_bytes: 48586429
        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
        num_bytes: 119632631
        num_examples: 4754
      - name: test
        num_bytes: 119596303
        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
        num_bytes: 492581177
        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
        num_bytes: 447602356
        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
        num_examples: 124397
      - name: validation
        num_bytes: 200369780
        num_examples: 6911
      - name: test
        num_bytes: 204394948
        num_examples: 6911
    download_size: 10142923776
    dataset_size: 4100353733
config_names:
  - a
  - all
  - b
  - c
  - d
  - e
  - f
  - g
  - h
  - 'y'

Dataset Card for Big Patent

Table of Contents

Dataset Description

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:

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:

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 

@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 for adding this dataset.