Create README.md

README.md

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+---
+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:wangluxy@umich.edu)
+
+### 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.