luchayward / low_resource_lm

Repo containing only my code for Honours 2020 project at UCT on Low Resource language Modelling for African Languages with RNN/LSTM models

Project Extensions

Please see the low-lm repository to see the extensions made by Stuart Mesham and myself in order to put together a joint paper on this project.

Low-Resource Language Modelling

Includes the necessary commandline arguments for running each of the models as well as utilities for downloading and preprocessing training data and instructions of how to install the dependencies.

Included is a copy of a iPython notebook from google Colab which will allow for easy running of the project code.

Usage example:

Ensure that all scripts are run from the root directory, not /scripts/

Models require the same pytorch/CUDA versions as required by the original AWD-LSTM library (see awd_lstm/README.md). You may wish to strip out the tensorboardX references if the logigng is unimportant to you. If those installation instructions do not work we recommend using Conda and Ubuntu. You can install with either

A) conda create --name <env> --file <this file>

or B)

conda create --name lowlm
conda activate lowlm
conda install pytorch=0.4.1 cuda90 -c pytorch
conda install --file lstm_requirements.txt
conda install requests

Install requirements:

pip3 install -r requirements.txt

If using QRNN models, also install the following:

pip3 install cupy pynvrtc git+https://github.com/saurabh3949/pytorch-qrnn

Fetch training data:

./scripts/fetch_data.sh

The minimum arguments to run the program are:

python3 awd_lstm/main.py \
    --data data/nchlt/isizulu/ \
    --save "/content/drive/My Drive/Colab Notebooks/nchlt_zulu_bpe_ptbInspired.pt" \
    --descriptive_name "ptbInspired" \

Experiments

The following provide the needed parameters to recreate the top performing QRNN, AWD-LSTM and basic LSTM models on the NCHLT-isiZulu dataset. To run on alternate datasets the --data argument should be changed. Each of the models takes at least 3-4 hours to reach adequate performance and up to 10-12 to reach the best performance. Models were trained using a mix of Nvidia K80, P100 and V100 GPUs.

AWD-LSTM

python3 -u awd_lstm/main.py \
    --save "AWD_LSTM_Test.pt" \
    --descriptive_name "ExampleAWDLSTM" \
    --data data/nchlt/isizulu/ \
    --model "LSTM" \
    --emsize 800 \
    --nhid 1150 \
    --nlayers 3 \
    --lr 30.0 \
    --clip 0.25 \
    --epochs 750 \
    --batch_size 80 \
    --bptt 70 \
    --dropout 0.4 \
    --dropouth 0.2 \
    --dropouti 0.65 \
    --dropoute 0.1 \
    --wdrop 0.5 \
    --seed 1882 \
    --nonmono 8 \

QRNN

python -u awd_lstm/main.py \
    --dropouth 0.2 \
    --seed 1882 \
    --epoch 500 \
    --emsize 800 \
    --nonmono 8 \
    --clip 0.25 \
    --dropouti 0.4 \
    --dropouth 0.2 \
    --nhid 1550 \
    --nlayers 4 \
    --wdrop 0.1 \
    --batch_size 40 \
    --data data/nchlt/isizulu/ \
    --model QRNN \
    --save "QRNN_test.pt" \
    --descriptive_name "ExampleQRNN" 

Basic LSTM

python3 -u awd_lstm/main.py \
    --save "basicInputDrop.pt" \
    --descriptive_name "basicInputDrop_example" \
    --data data/nchlt/isizulu/ \
    --dropouti 0.25 \
    --model LSTM \
    --emsize 400 \
    --nhid 1550 \
    --nlayers 1 \
    --lr 5.0 \
    --clip 0.0 \
    --epochs 500 \
    --batch_size 40 \
    --bptt 70 \
    --dropout 0.0 \
    --dropouth 0.0 \
    --dropoute 0.0 \
    --wdrop 0.0 \
    --seed 4002 \
    --nonmono 5 \
    --alpha 0.0 \
    --beta 0.0 \
    --wdecay 0.0 \
    --chpc True \

AWD-LSTM Acknowledgements

Code accessed from https://github.com/salesforce/awd-lstm-lm

See the readme at /awd_lstm/README.md for further details

Merity, Stephen et al. "Regularizing and Optimizing LSTM Language Models". arXiv preprint arXiv:1708.02182. (2017).

Merity, Stephen et al. "An Analysis of Neural Language Modeling at Multiple Scales". arXiv preprint arXiv:1803.08240. (2018).