def wikicont_bert(): hp = hyperparams.HPBert() e = Experiment(hp) e_config = ExperimentConfig() e_config.name = "WikiContrv2009_only_wiki" e_config.num_epoch = 1 e_config.save_interval = 60 * 60 # 1 minutes e_config.load_names = ['bert'] e_config.valid_freq = 100 vocab_size = 30522 vocab_filename = "bert_voca.txt" data_loader = Ams18.DataLoader(hp.seq_max, vocab_filename, vocab_size) data_loader.source_collection.collection_type = 'wiki' load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt') e.train_wiki_contrv(e_config, data_loader, load_id)
def train_ukp_with_nli(load_id, exp_name): step_per_epoch = 24544 + 970 hp = hyperparams.HPBert() e_config = ExperimentConfig() e_config.num_steps = step_per_epoch e_config.save_interval = 100 * 60 # 30 minutes e_config.voca_size = 30522 e_config.num_dev_batches = 30 e_config.load_names = ['bert'] e_config.valid_freq = 500 encode_opt = "is_good" nli_setting = NLI() nli_setting.vocab_size = 30522 nli_setting.vocab_filename = "bert_voca.txt" num_class_list = [3, 3] f1_list = [] for topic in data_generator.argmining.ukp_header.all_topics: e = Experiment(hp) print(exp_name) e_config.name = "argmix_{}_{}_{}".format(exp_name, topic, encode_opt) arg_data_loader = BertDataLoader(topic, True, hp.seq_max, "bert_voca.txt", option=encode_opt) nli_data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True) shared_data_loader = SharedFeeder([arg_data_loader, nli_data_loader], [1, 5], ["Arg", "NLI"], hp.batch_size) save_path = e.train_shared(e_config, shared_data_loader, num_class_list, load_id) print(topic) f1_last = e.eval_ukp_on_shared(e_config, arg_data_loader, num_class_list, save_path) f1_list.append((topic, f1_last)) print(exp_name) print(encode_opt) print(f1_list) for key, score in f1_list: print("{0}\t{1:.03f}".format(key, score))