---
language: en
size_categories: 10K<n<100K
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
tags:
- long context
dataset_info:
- config_name: abstract
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': Human Necessities
          '1': Performing Operations; Transporting
          '2': Chemistry; Metallurgy
          '3': Textiles; Paper
          '4': Fixed Constructions
          '5': Mechanical Engineering; Lightning; Heating; Weapons; Blasting
          '6': Physics
          '7': Electricity
          '8': General tagging of new or cross-sectional technology
  splits:
  - name: train
    num_bytes: 17225101
    num_examples: 25000
  - name: validation
    num_bytes: 3472854
    num_examples: 5000
  - name: test
    num_bytes: 3456733
    num_examples: 5000
  download_size: 12067953
  dataset_size: 24154688
- config_name: patent
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': Human Necessities
          '1': Performing Operations; Transporting
          '2': Chemistry; Metallurgy
          '3': Textiles; Paper
          '4': Fixed Constructions
          '5': Mechanical Engineering; Lightning; Heating; Weapons; Blasting
          '6': Physics
          '7': Electricity
          '8': General tagging of new or cross-sectional technology
  splits:
  - name: train
    num_bytes: 466788625
    num_examples: 25000
  - name: validation
    num_bytes: 95315107
    num_examples: 5000
  - name: test
    num_bytes: 93844869
    num_examples: 5000
  download_size: 272966251
  dataset_size: 655948601
configs:
- config_name: abstract
  data_files:
  - split: train
    path: abstract/train-*
  - split: validation
    path: abstract/validation-*
  - split: test
    path: abstract/test-*
- config_name: patent
  data_files:
  - split: train
    path: patent/train-*
  - split: validation
    path: patent/validation-*
  - split: test
    path: patent/test-*
  default: true
---

**Patent Classification: a classification of Patents and abstracts (9 classes).** 

This dataset is intended for long context classification (non abstract documents are longer that 512 tokens). \
Data are sampled from "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization." by Eva Sharma, Chen Li and Lu Wang 
 * See: https://aclanthology.org/P19-1212.pdf 
 * See: https://evasharma.github.io/bigpatent/

It contains 9 unbalanced classes, 35k Patents and abstracts divided into 3 splits: train (25k), val (5k) and test (5k). 

**Note that documents are uncased and space separated (by authors)**

Compatible with [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) script:
```
export MODEL_NAME=roberta-base
export MAX_SEQ_LENGTH=512

python run_glue.py \
  --model_name_or_path $MODEL_NAME \
  --dataset_name ccdv/patent-classification  \
  --do_train \
  --do_eval \
  --max_seq_length $MAX_SEQ_LENGTH \
  --per_device_train_batch_size 8 \
  --gradient_accumulation_steps 4 \
  --learning_rate 2e-5 \
  --num_train_epochs 1 \
  --max_eval_samples 500 \
  --output_dir tmp/patent
```