def calculate_selectional_preferences(config_path, model_path, list_preds, vob,
output):
'''
:param config_path:
:param model_path:
:param list_preds:
:param vob: dictionary with key of preds, values are list of words
:param output:
:return:
'''
cfg = read_config(config_path)
mdl, fm1, fm2 = loadSemLM21(model_path, cfg['train'], cfg['valid'],
cfg['we_dict1'], cfg['we_dict2'], cfg['dep'],
cfg['hidden_size1'], cfg['hidden_size2'],
cfg['batch_size'], cfg['save_folder'])
for p in list_preds:
scores = get_probability_is_argument(mdl, fm1, fm2, p)
process_probability(fm1,
fm2,
scores,
p,
output + "/" + p + ".out.txt",
vobs=vob[p])
def trainModel11(config_path):
cfg = read_config(config_path)
trainSemLM11(cfg['train'],
cfg['valid'],
cfg['we_dict'],
cfg['dep'],
cfg['hidden_size'],
cfg['batch_size'],
cfg['save_folder'],
load_dt=cfg['load_data'])
def calculate_selectional_preferences(config_path, model_path, list_preds, vob,
output):
cfg = read_config(config_path)
mdl, fm = loadSemLM11(model_path, cfg['train'], cfg['valid'],
cfg['we_dict'], cfg['dep'], cfg['hidden_size'],
cfg['batch_size'], cfg['save_folder'])
for p in list_preds:
scores = get_probability_is_argument(mdl, fm, p)
process_probability(fm,
scores,
p,
output + "/" + p + ".out.txt",
vobs=vob[p])