- Code for running evaluations of word embeddings on extrinsic tasks from our COLING 2018 paper
For each task, run preprocess.py to load the preprocessed version of the dataset.
To train the model, run train.py
A pretrained word embedding text file is needed where every line has a word string followed by a space and the embedding vector.
For example, acrobat 0.6056159735 -0.1367940009 -0.0936380029 0.8406270146 0.2641879916 0.4209069908 0.0607739985 0.5985950232 -1.1451450586 -0.8666719794 -0.5021889806 0.4398249984 0.9671009779 0.7413169742 -0.0954160020 -1.1526989937 -0.3915260136 -0.1520590037 0.0893440023 -0.2578850091 -0.6204599738 -0.8789629936 0.3581469953 0.5509790182 0.1234730035
Data for NLI task can be found here
For the sequence labeling tasks(POS, NER and chunking), please refer to this repo