• Python 3.6+
• PyTorch 1.7+
• NumPy
• tqdm
• transformers

To begin, you will need to prepare datasets with data in data/{dataset name}-{range} directory as follows:

$ python processRaw.py --dn={dataset name} --rn={range}
$ python prepare_datasets.py --dn={dataset name} --rn={range}

The data directory consists of json files end with "train.json", "val.json" and "test.json". An example element is as follows:

{
    "src": "X0,{;c;c},0",
    "tgt": "0,{c;c;},0#@,@,@"
}

To train the model with the specified dataset and range with args in cofig/config.json:

$  python train --dn={dataset name} --rn={range}

for more notification about the arguments, simply consult

$  python train -h

To predict a satisfying trace from the ltl formulas in test.json using the best model in bestModel/ (which is retrieved by copying the best model specified in training log and renamed as {dataset name}-{range}.pth):

$ python translate.py --dn={dataset name} --rn={range} --tdn={target dataset name}

It will give you the prediction of all range in target dataset and output the prediction at data/preiction-{dataset name}-{range}.

To evaluate the prediction produced by the model mentioned above:

$ python evaluate.py --dn={dataset name} --rn={range} --tdn={target dataset name}

It will output the syntactic accuracy and semantic accuracy of prediction at all range of target dataset.

Parts of code/scripts are borrowed/inspired from:

• https://github.com/pytorch/examples/tree/master/word_language_model

• https://github.com/dreamgonfly/transformer-pytorch

• https://github.com/jadore801120/attention-is-all-you-need-pytorch