except: step = 0 test_abs(args, device_id, cp, step) elif (args.mode == 'test_text'): cp = args.test_from try: step = int(cp.split('.')[-2].split('_')[-1]) except: step = 0 test_text_abs(args, device_id, cp, step) elif (args.task == 'ext'): if (args.mode == 'train'): train_ext(args, device_id) elif (args.mode == 'validate'): validate_ext(args, device_id) if (args.mode == 'test'): cp = args.test_from try: step = int(cp.split('.')[-2].split('_')[-1]) except: step = 0 test_ext(args, device_id, cp, step) elif (args.mode == 'test_text'): cp = args.test_from try: step = int(cp.split('.')[-2].split('_')[-1]) except: step = 0 test_text_abs(args, device_id, cp, step)
elif args.mode == 'test': checkpoint = args.load_model # Print or save summaries and probas for test mode logging.info("Processing files...") with open('../results/patents_analysis.csv', mode='w') as file: writer = csv.writer(file, delimiter='?', quotechar='"', quoting=csv.QUOTE_MINIMAL) writer.writerow([ 'Ref patent', 'Domain', 'Probability of contradiction', 'First part Contradiction', 'Second part Contradiction', 'Context' ]) for ref_patents, summaries, output_probas, prediction_contradiction, str_context in test_ext( args, device_id, checkpoint, 0): for ref_patent, summary, output_proba, prediction_c, context in zip( ref_patents, summaries, output_probas, prediction_contradiction, str_context): # print(summary) # Get patents domain if possible try: with open( '../data_patents/input_data/test_directory/' + ref_patent + '/' + ref_patent + '.DOMAIN', mode='r') as domain_f: name_domain = domain_f.read() except: name_domain = 'unknown'
step = 0 test_abs(args, device_id, cp, step) elif (args.mode == 'test_text'): cp = args.test_from try: step = int(cp.split('.')[-2].split('_')[-1]) except: step = 0 test_text_abs(args, device_id, cp, step) elif (args.task == 'ext'): if (args.mode == 'train'): # print('joint: ' + str(args.section_prediction)) train_ext(args, device_id, args.section_prediction) elif (args.mode == 'validate'): validate_ext(args, device_id) if (args.mode == 'test'): cp = args.test_from try: step = int(cp.split('.')[-2].split('_')[-1]) except: step = 0 test_ext(args, device_id, cp, step, args.section_prediction) elif (args.mode == 'test_text'): cp = args.test_from try: step = int(cp.split('.')[-2].split('_')[-1]) except: step = 0 test_text_abs(args, device_id, cp, step)
def run(args, hpspace): if (args.task == 'abs'): if (args.mode == 'train'): if (args.hyperopt): t = time.time() newT = str(t).split(".") args.model_path = "../models/" + newT[0] args.log_file = "../logs/abs_bert_abs_" + newT[0] args.lr_bert = hpspace['lr_bert'] args.lr_dec = hpspace['lr_dec'] args.accum_count = int(hpspace['accum_count']) args.beta1 = hpspace['beta1'] args.beta2 = hpspace['beta2'] args.visible_gpus = '0' args.bert_model = '..temp/bert-base-danish-uncased-v2' args.vocab = '..temp/bert-base-danish-uncased-v2' train_stats = train_abs(args, device_id) x = train_stats.x ppl = train_stats.perplexity acc = train_stats.acc print(x) return { 'loss': x, 'eval_time': time.time(), 'status': STATUS_OK, 'other_stuff': { 'ppl': ppl, 'acc': acc } } elif (args.mode == 'validate'): validate_abs(args, device_id) elif (args.mode == 'lead'): baseline(args, cal_lead=True) elif (args.mode == 'oracle'): baseline(args, cal_oracle=True) if (args.mode == 'test'): cp = args.test_from try: step = int(cp.split('.')[-2].split('_')[-1]) except: step = 0 test_abs(args, device_id, cp, step) elif (args.mode == 'test_text'): cp = args.test_from try: step = int(cp.split('.')[-2].split('_')[-1]) except: step = 0 test_text_abs(args, device_id, cp, step) elif (args.task == 'ext'): if (args.mode == 'train'): train_ext(args, device_id) elif (args.mode == 'validate'): validate_ext(args, device_id) if (args.mode == 'test'): cp = args.test_from try: step = int(cp.split('.')[-2].split('_')[-1]) except: step = 0 test_ext(args, device_id, cp, step) elif (args.mode == 'test_text'): cp = args.test_from try: step = int(cp.split('.')[-2].split('_')[-1]) except: step = 0 test_text_abs(args, device_id, cp, step) if (args.mode == "sent_label"): step = 0 sent_label_ext(args, device_id)