
**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
```
task_categories:text-classification task_ids:multi-class-classification task_ids:topic-classification size_categories:10K<n<100K language:en long context region:us
{"language": "en", "size_categories": "10K<n<100K", "task_categories": ["text-classification"], "task_ids": ["multi-class-classification", "topic-classification"], "tags": ["long context"]}
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #task_ids-topic-classification #size_categories-10K<n<100K #language-English #long context #region-us 


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: URL 
 * See: URL

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 script:

TAGS
#task_categories-text-classification #task_ids-multi-class-classification #task_ids-topic-classification #size_categories-10K<n<100K #language-English #long context #region-us 

passage: TAGS
#task_categories-text-classification #task_ids-multi-class-classification #task_ids-topic-classification #size_categories-10K<n<100K #language-English #long context #region-us 
