Update README.md

README.md

@@ -1,2 +1,406 @@
+---
+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
+---
 # Sampled big_patent Dataset
-This is a sampled big_patent dataset containing 5000 train and 500 test rows.
+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.