def stance_fine_tune(): # importing the required module import matplotlib.pyplot as plt for lr in [1e-3, 5e-4, 2e-4, 1e-4]: hp = HPFineTunePair() topic = "hillary" e = Experiment(hp) hp.lr = lr hp.num_epochs = 100 preload_id = ("LM_pair_tweets_hillary_run2", 1247707) setting = shared_setting.TopicTweets2Stance(topic) stance_data = stance_detection.FineLoader(topic, hp.seq_max, setting.vocab_filename, hp.sent_max) valid_history = e.train_stance(setting.vocab_size, stance_data, preload_id) e.clear_run() l_acc, l_f1 = zip(*valid_history) plt.plot(l_acc, label="{} / ACC".format(lr)) plt.plot(l_f1, label="{} / F1".format(lr)) plt.legend(loc='lower right') # giving a title to my graph plt.title('learning rate - dev !') # function to show the plot plt.show()
def predict_rf_tune(): hp = hyperparams.HPBert() hp.batch_size = 256 e = Experiment(hp) nli_setting = NLI() nli_setting.vocab_size = 30522 nli_setting.vocab_filename = "bert_voca.txt" target_label = 'match' #data_id = 'test_conflict' data_id = "{}_1000".format(target_label) e_config = ExperimentConfig() l = [(0.1, 12039), (0.2, 12245), (0.3, 12063), (0.4, 12250), (0.6, 12262), (0.7, 12253)] l = [(0.5, 12175), (0.8, 12269), (0.9, 12259)] for del_g, step in l: e_config.name = "X_match_del_{}".format(del_g) e_config.load_names = ['bert', 'cls_dense', 'aux_conflict'] data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True) load_id = ("NLIEx_match_del_{}".format(del_g), "model-{}".format(step)) e.clear_run() e.predict_rf(nli_setting, e_config, data_loader, load_id, data_id, 5)