Datasets:

Trelis
/

big_patent_100k_characters

	---
	annotations_creators:
	- no-annotation
	language_creators:
	- found
	language:
	- en
	license:
	- cc-by-4.0
	multilinguality:
	- monolingual
	size_categories:
	- n<1k
	source_datasets:
	- big_patent
	task_categories:
	- summarization
	task_ids: []
	paperswithcode_id: bigpatent
	pretty_name: Big Patent <100k characters
	tags:
	- patent-summarization
	---
	# Sampled Big Patent Dataset
	This is a sampled Trelis/big_patent_sample dataset containing rows of data with descriptions shorter than or equal to 100,000 characters in length.

	--- Sampled from Trelis/big_patent_sampled ---

	# Sampled big_patent Dataset
	This is a sampled big_patent dataset - sampled down for shorter fine-tunings.

	The data is sampled with the aim of providing an even distribution across data lengths. The distribution is quite flat up until 1 million characters in length, making the dataset good for training on lengths up to 250,000 tokens.

	# 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.