stage_4_gm

• Introduction
• Natural Language Inference (NLI) task
• The data (SNLI dataset)
• Command lines (How to use this git)
  • Pytorch lightning script

First of all make sure to use the environnement.

Path to $VENV should be saved in ~/.bashrc

# Specify path to venv
export VENV=path/to/venv
echo $VENV

# Create venv
python -m venv $VENV/bert

# Activate venv
source $VENV/bert/bin/activate

# Replicate on cpu
pip install -r python_env/requirements.cpu.txt --no-cache-dir

# Replicate on gpu
pip install -r python_env/requirements.gpu.txt --no-cache-dir

# Exit venv
deactivate

• if you are using conda you can use the two following command :

conda env create -f python_env/environment.yml
conda activate nlp

conda create --name nlp --file requirements.txt
conda activate nlp

WARNING: All the environments were exported on windows 11 -64 bits.

To download the snli and e-snli data the command line is the following :

python data_download.py

All the data downloaded in this part will be stored in the folder : .cache\raw_data

To run the training_bert.py for some tests we used the following command line :

python training_bert.py --epoch 3 --batch_size 4 --nb_data 16 --experiment bert --version 0

# Or by shorthand
python training_bert.py -e 3 -b 4 -n 16 --experiment bert --version 0

The objective was only to see the behaviour of the training with a small amount of data. (Spot some mistakes and see the
behaviour of the loss)

To visualize our training performance we used the tool tensorboard. The default logdir in
in .cache/logs/$EXPERIMENT
where $EXPERIMENT is specified in --experiment. The log could be changed using flag --logdir or shorthand -s

tensorboard --logdir .cache/logs/$EXPERIMENT