To run a generation experiment (either conceptnet or atomic), follow these instructions:

First Steps

First clone, the repo:

git clone https://github.com/atcbosselut/comet-commonsense.git

Then run the setup scripts to acquire the pretrained model files from OpenAI, as well as the ATOMIC and ConceptNet datasets

bash scripts/setup/get_atomic_data.sh
bash scripts/setup/get_conceptnet_data.sh
bash scripts/setup/get_model_files.sh

Then install dependencies (assuming you already have Python 3.6 and Pytorch >= 1.0:

pip install tensorflow
pip install ftfy==5.1
conda install -c conda-forge spacy
python -m spacy download en
pip install tensorboardX
pip install tqdm
pip install pandas
pip install ipython

Making the Data Loaders

Run the following scripts to pre-initialize a data loader for ATOMIC or ConceptNet:

python scripts/data/make_atomic_data_loader.py
python scripts/data/make_conceptnet_data_loader.py

For the ATOMIC KG, if you'd like to make a data loader for only a subset of the relation types, comment out any relations in lines 17-25. For ConceptNet if you'd like to map the relations to natural language analogues, set opt.data.rel = "language" in line 26. If you want to initialize unpretrained relation tokens, set opt.data.rel = "relation"

Setting the ATOMIC configuration files

Open config/atomic/changes.json and set which categories you want to train, as well as any other details you find important. Check src/data/config.py for a description of different options. Variables you may want to change: batch_size, learning_rate, categories. See config/default.json and config/atomic/default.json for default settings of some of these variables.

Setting the ConceptNet configuration files

Open config/conceptnet/changes.json and set any changes to the degault configuration that you may want to vary in this experiment. Check src/data/config.py for a description of different options. Variables you may want to change: batch_size, learning_rate, etc. See config/default.json and config/conceptnet/default.json for default settings of some of these variables.

Running the ATOMIC experiment

Training

For whichever experiment # you set in ```config/atomic/changes.json``` (e.g., 0, 1, 2, etc.), run:

python src/main.py --experiment_type atomic --experiment_num #

Evaluation

Once you've trained a model, run the evaluation script:

python scripts/evaluate/evaluate_atomic_generation_model.py --split $DATASET_SPLIT --model_name /path/to/model/file

Generation

Once you've trained a model, run the generation script for the type of decoding you'd like to do:

python scripts/generate/generate_atomic_beam_search.py --beam 10 --split $DATASET_SPLIT --model_name /path/to/model/file
python scripts/generate/generate_atomic_greedy.py --split $DATASET_SPLIT --model_name /path/to/model/file
python scripts/generate/generate_atomic_topk.py --k 10 --split $DATASET_SPLIT --model_name /path/to/model/file

Running the ConceptNet experiment

Training

For whichever experiment # you set in config/conceptnet/changes.json (e.g., 0, 1, 2, etc.), run:

python src/main.py --experiment_type conceptnet --experiment_num #

Development and Test set tuples are automatically evaluated and generated with greedy decoding during training

Generation

If you want to generate with a larger beam size, run the generation script

python scripts/generate/generate_conceptnet_beam_search.py --beam 10 --split $DATASET_SPLIT --model_name /path/to/model/file

Playing Around in Interactive Mode

First, download the pretrained models from the following link:

https://drive.google.com/open?id=1FccEsYPUHnjzmX-Y5vjCBeyRt1pLo8FB

Then untar the file:

tar -xvzf pretrained_models.tar.gz

Then run the following script to interactively generate arbitrary ATOMIC event effects:

python scripts/interactive/atomic_single_example.py --model_file pretrained_models/atomic_pretrained_model.pickle

Or run the following script to interactively generate arbitrary ConceptNet tuples:

python scripts/interactive/conceptnet_single_example.py --model_file pretrained_models/conceptnet_pretrained_model.pickle