task_categories:text-classification task_ids:multi-class-classification task_ids:topic-classification size_categories:10K<n<100K language:en long context region:us
Arxiv Classification Dataset: a classification of Arxiv Papers (11 classes).
 It contains 11 slightly unbalanced classes, 33k Arxiv Papers divided into 3 splits: train (23k), val (5k) and test (5k).
 Copied from "Long Document Classification From Local Word Glimpses via Recurrent Attention Learning" by JUN HE LIQUN WANG LIU LIU, JIAO FENG AND HAO WU
 See: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8675939
 See: https://github.com/LiqunW/Long-document-dataset
---
language: en
task_categories:
- text-classification
tags:
- long context
task_ids:
- multi-class-classification
- topic-classification
size_categories: 10K<n<100K
---

**Arxiv Classification: a classification of Arxiv Papers (11 classes).** 

This dataset is intended for long context classification (documents have all > 4k tokens). \
Copied from "Long Document Classification From Local Word Glimpses via Recurrent Attention Learning"
```
@ARTICLE{8675939,
  author={He, Jun and Wang, Liqun and Liu, Liu and Feng, Jiao and Wu, Hao},
  journal={IEEE Access}, 
  title={Long Document Classification From Local Word Glimpses via Recurrent Attention Learning}, 
  year={2019},
  volume={7},
  number={},
  pages={40707-40718},
  doi={10.1109/ACCESS.2019.2907992}
  }
```
 * See: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8675939 
 * See: https://github.com/LiqunW/Long-document-dataset 

It contains 11 slightly unbalanced classes, 33k Arxiv Papers divided into 3 splits: train (28k), val (2.5k) and test (2.5k). 

2 configs:
  * default
  * no_ref, removes references to the class inside the document (eg: [cs.LG] -> [])

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/arxiv-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/arxiv
```