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Pytorch implementation of pQRNN

Project description


<center> Code style: black License: MIT </center>


Note: Because of recent pytorch change (>=1.7), it is not possible to run a QRNN layer without messing up the environment. See for details.

pip install -r requirements.txt

If you want to use a QRNN layer, please follow the instructions here to install python-qrnn first with downgraded torch <= 1.4.


Usage: [OPTIONS]

  --task [yelp2|yelp5|toxic]      [default: yelp5]
  --b INTEGER                     [default: 128]
  --d INTEGER                     [default: 96]
  --num_layers INTEGER            [default: 2]
  --batch_size INTEGER            [default: 512]
  --dropout FLOAT                 [default: 0.5]
  --lr FLOAT                      [default: 0.001]
  --nhead INTEGER                 [default: 4]
  --rnn_type [LSTM|GRU|QRNN|Transformer]
                                  [default: GRU]
  --data_path TEXT
  --help                          Show this message and exit.


  • yelp2(polarity): it will be downloaded w/ datasets(huggingface)
  • yelp5: json file should be downloaded to into data/
  • toxic: dataset should be downloaded and unzipped to into data/

Example: Yelp Polarity

python -W ignore --task yelp2 --b 128 --d 64 --num_layers 4

Benchmarks(not optimized)

Model Model Size Yelp Polarity (error rate) Yelp-5 (accuracy) Civil Comments (mean auroc) Command
PQRNN (this repo) 78K 6.3 70.4 TODO --b 128 --d 64 --num_layers 4 --rnn_type QRNN
PRNN (this repo) 90K 5.5 70.7 95.57 --b 128 --d 64 --num_layers 1 --rnn_type GRU
PTransformer (this repo) 617K 10.8 68 86.5 --b 128 --d 64 --num_layers 1 --rnn_type Transformer --nhead 2
PRADO1 175K 65.9
BERT 335M 1.81 70.58 98.8562
  1. Paper
  2. Best Kaggle Submission



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