ccdv/patent-classification
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:… See the full description on the dataset page: https://huggingface.co/datasets/ccdv/patent-classification.
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 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