import numpy as np import pandas as pd import torch from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForTokenClassification import json from tqdm import tqdm import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from torch.utils.data import Dataset import os import time # Load pre-trained model (weights) model = BertForTokenClassification.from_pretrained('bert-base-cased', num_labels=3) # Load pre-trained model tokenizer (vocabulary) tokenizer = BertTokenizer.from_pretrained('bert-base-cased') # print(model) print(tokenizer) print("vocab_size : {}\n".format(len(tokenizer.vocab))) print(tokenizer.convert_tokens_to_ids(['to', '[PAD]'])) df = pd.read_csv('../input/train.csv') # print(df) print("df key :", df.keys()) char_label_maxlen = 0 word_label_maxlen = 0 # check label length at char level char_length_dic = {}
train_data = TensorDataset(tr_inputs, tr_masks, tr_tags) train_sampler = RandomSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=bs) valid_data = TensorDataset(val_inputs, val_masks, val_tags) valid_sampler = SequentialSampler(valid_data) valid_dataloader = DataLoader(valid_data, sampler=valid_sampler, batch_size=bs) model = BertForTokenClassification.from_pretrained( "bert-base-cased", num_labels=len(tag2idx), output_attentions = False, output_hidden_states = False ) FULL_FINETUNING = False if FULL_FINETUNING: param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'gamma', 'beta'] optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0} ] else:
# Initialize the DataLoader data_loader = DataLoader(args.data_dir, args.bert_model_dir, params, token_pad_idx=0) # Load data test_data = data_loader.load_data('test') # Specify the test set size params.test_size = test_data['size'] params.eval_steps = params.test_size // params.batch_size test_data_iterator = data_loader.data_iterator(test_data, shuffle=False) logging.info("- done.") # Define the model config_path = os.path.join(args.bert_model_dir, 'bert_config.json') config = BertConfig.from_json_file(config_path) model = BertForTokenClassification(config, num_labels=len(params.tag2idx)) model.to(params.device) # Reload weights from the saved file utils.load_checkpoint(os.path.join(args.model_dir, args.restore_file + '.pth.tar'), model) if args.fp16: model.half() if params.n_gpu > 1 and args.multi_gpu: model = torch.nn.DataParallel(model) logging.info("Starting evaluation...") test_metrics = evaluate(model, test_data_iterator, params, mark='Test', verbose=True)
def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument( "--data_dir", default='/home/adzuser/user_achyuta/BERT_NER_Test/BERT-NER/NERdata/', type=str, required=True, help= "The input data dir. Should contain the .tsv files (or other data files) for the task." ) parser.add_argument( "--bert_model", default=None, type=str, required=True, help="Bert pre-trained model selected in the list: bert-base-uncased, " "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, " "bert-base-multilingual-cased, bert-base-chinese.") parser.add_argument("--task_name", default='NER', type=str, required=True, help="The name of the task to train.") parser.add_argument( "--output_dir", default='ner_output', type=str, required=True, help= "The output directory where the model predictions and checkpoints will be written." ) ## Other parameters parser.add_argument( "--cache_dir", default="", type=str, help= "Where do you want to store the pre-trained models downloaded from s3") parser.add_argument( "--max_seq_length", default=128, type=int, help= "The maximum total input sequence length after WordPiece tokenization. \n" "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--do_train", action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--do_test", action='store_true', help="Whether to run test on the test set.") parser.add_argument("--do_pred", action='store_true', help="Whether to run pred on the pred set.") parser.add_argument( "--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") parser.add_argument("--eval_batch_size", default=8, type=int, help="Total batch size for eval.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument( "--num_train_epochs", default=10.0, #3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument( "--warmup_proportion", default=0.1, type=float, help= "Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10%% of training.") parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument('--clip', type=float, default=0.5, help="gradient clipping") parser.add_argument( '--gradient_accumulation_steps', type=int, default=1, help= "Number of updates steps to accumulate before performing a backward/update pass." ) parser.add_argument( '--fp16', action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument( '--loss_scale', type=float, default=0, help= "Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" "0 (default value): dynamic loss scaling.\n" "Positive power of 2: static loss scaling value.\n") parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.") parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.") parser.add_argument('--text_a', type=str, default='', help="input text_a.") parser.add_argument('--text_b', type=str, default='', help="input text_b.") args = parser.parse_args() if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() processors = {"ner": NerProcessor} num_labels_task = {"ner": 12} if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') if args.gradient_accumulation_steps < 1: raise ValueError( "Invalid gradient_accumulation_steps parameter: {}, should be >= 1" .format(args.gradient_accumulation_steps)) args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if n_gpu > 0: torch.cuda.manual_seed_all(args.seed) if not args.do_train and not args.do_eval and not args.do_pred: raise ValueError( "At least one of `do_train` or `do_eval` must be True.") if os.path.exists(args.output_dir) and os.listdir( args.output_dir) and args.do_train: raise ValueError( "Output directory ({}) already exists and is not empty.".format( args.output_dir)) if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) task_name = args.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() num_labels = num_labels_task[task_name] label_list = processor.get_labels() tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) train_examples = None num_train_optimization_steps = None if args.do_train: train_examples = processor.get_train_examples(args.data_dir) #print("train_examples :: ",len(list(train_examples))) num_train_optimization_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs if args.local_rank != -1: num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size( ) # Prepare model cache_dir = args.cache_dir if args.cache_dir else os.path.join( PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format( args.local_rank)) #imodel = BertForSequenceClassification.from_pretrained(args.bert_model, # cache_dir=cache_dir, # num_labels = num_labels) model = BertForTokenClassification.from_pretrained(args.bert_model, cache_dir=cache_dir, num_labels=num_labels) if args.fp16: model.half() model.to(device) if args.local_rank != -1: try: from apex.parallel import DistributedDataParallel as DDP except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training." ) model = DDP(model) elif n_gpu > 1: model = torch.nn.DataParallel(model) # Prepare optimizer param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [{ 'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01 }, { 'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0 }] if args.fp16: try: from apex.optimizers import FP16_Optimizer from apex.optimizers import FusedAdam except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training." ) optimizer = FusedAdam(optimizer_grouped_parameters, lr=args.learning_rate, bias_correction=False, max_grad_norm=1.0) if args.loss_scale == 0: optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) else: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) else: optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=num_train_optimization_steps) global_step = 0 nb_tr_steps = 0 tr_loss = 0 if args.do_train: train_features = convert_examples_to_features(train_examples, label_list, args.max_seq_length, tokenizer) all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long) train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) if args.local_rank == -1: train_sampler = RandomSampler(train_data) else: train_sampler = DistributedSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) model.train() for _ in trange(int(args.num_train_epochs), desc="Epoch"): tr_loss = 0 nb_tr_examples, nb_tr_steps = 0, 0 for step, batch in enumerate( tqdm(train_dataloader, desc="Iteration")): batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, label_ids = batch #print(input_ids.shape,input_mask.shape,segment_ids.shape,label_ids.shape) #print(input_ids[0]) #print(label_ids[0]) #logits = model(input_ids, segment_ids, input_mask) #import pdb;pdb.set_trace() #print(logits.view(-1, num_labels).shape, label_ids.view(-1).shape) loss = model(input_ids, segment_ids, input_mask, label_ids) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: optimizer.backward(loss) else: loss.backward() # added clip if args.clip is not None: _ = torch.nn.utils.clip_grad_norm(model.parameters(), args.clip) tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: # modify learning rate with special warm up BERT uses # if args.fp16 is False, BertAdam is used that handles this automatically lr_this_step = args.learning_rate * warmup_linear( global_step / num_train_optimization_steps, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step() optimizer.zero_grad() global_step += 1 if args.do_train: # Save a trained model and the associated configuration model_to_save = model.module if hasattr( model, 'module') else model # Only save the model it-self output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME) torch.save(model_to_save.state_dict(), output_model_file) output_config_file = os.path.join(args.output_dir, CONFIG_NAME) with open(output_config_file, 'w') as f: f.write(model_to_save.config.to_json_string()) # Load a trained model and config that you have fine-tuned config = BertConfig(output_config_file) #model = BertForSequenceClassification(config, num_labels=num_labels) model = BertForTokenClassification(config, num_labels=num_labels) model.load_state_dict(torch.load(output_model_file)) else: #model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels) # Load a trained model and config that you have fine-tuned print('for eval only......................') output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME) output_config_file = os.path.join(args.output_dir, CONFIG_NAME) config = BertConfig(output_config_file) #model = BertForSequenceClassification(config, num_labels=num_labels) model = BertForTokenClassification(config, num_labels=num_labels) model.load_state_dict(torch.load(output_model_file)) model.to(device) if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = processor.get_dev_examples(args.data_dir) #import pdb;pdb.set_trace() #print("dev_eaxmples :: ",len(list(eval_examples))) eval_features = convert_examples_to_features_pred( eval_examples, label_list, args.max_seq_length, tokenizer) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 predictions, true_labels = [], [] #predictions1 , true_labels1 = [], [] for input_ids, input_mask, segment_ids, label_ids in tqdm( eval_dataloader, desc="Evaluating"): input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids) logits = model(input_ids, segment_ids, input_mask) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() # get index till '[SEP]' #print("label_list index SEP : ",label_list.index('[SEP]')) pred_xx = [list(p) for p in np.argmax(logits, axis=2)] pred_xx = [i[:i.index(label_list.index('[SEP]'))] for i in pred_xx] label_ids_xx = [ i[:i.index(label_list.index('[SEP]'))] for i in label_ids.tolist() ] #print(label_ids_xx) #print(pred_xx) # new add tmp_s = [ max(len(i), len(j)) for i, j in zip(label_ids_xx, pred_xx) ] tmp_u = [(i + [31] * (k - len(i)) if len(i) != k else i, j + [31] * (k - len(j)) if len(j) != k else j) for i, j, k in zip(label_ids_xx, pred_xx, tmp_s)] tmp_d1 = [h[0] for h in tmp_u] tmp_d2 = [h[1] for h in tmp_u] #print([list(p) for p in np.argmax(logits, axis=2)][:5]) #tmp_eval_accuracy = flat_accuracy(logits, label_ids) tmp_eval_accuracy = flat_accc(pred_xx, label_ids_xx) #tmp_eval_accuracy = flat_accc(tmp_d1, tmp_d2) predictions.extend(tmp_d2) true_labels.append(tmp_d1) #predictions1.extend(pred_xx) #true_labels1.append(label_ids_xx) #print("tmp accuracy : ",tmp_eval_accuracy) eval_loss += tmp_eval_loss.mean().item() eval_accuracy += tmp_eval_accuracy nb_eval_examples += input_ids.size(0) nb_eval_steps += 1 eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_steps loss = tr_loss / nb_tr_steps if args.do_train else None pred_tags = [[label_list[p_i] if p_i != 31 else 'XXX' for p_i in p] for p in predictions] valid_tags = [[ label_list[l_ii] if l_ii != 31 else 'YYY' for l_ii in l_i ] for l in true_labels for l_i in l] print("valid_tags : ", valid_tags[:10]) print("pred_tags : ", pred_tags[:10]) print("Validation F1-Score: {}".format(f1_score(valid_tags, pred_tags))) print("Validation accuracy_score : {}".format( accuracy_score(valid_tags, pred_tags))) print("Validation classification_report : {}".format( classification_report(valid_tags, pred_tags))) #print("X Validation F1-Score: {}".format(f1_score(true_labels1, predictions1))) #print("X Validation accuracy_score : {}".format(accuracy_score(true_labels1, predictions1))) #print("X Validation classification_report : {}".format(classification_report(true_labels1, predictions1))) result = { 'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'global_step': global_step, 'loss': loss } print(result) output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: for key in sorted(result.keys()): writer.write("%s = %s\n" % (key, str(result[key]))) if args.do_test and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = processor.get_test_examples(args.data_dir) #import pdb;pdb.set_trace() eval_features = convert_examples_to_features_pred( eval_examples, label_list, args.max_seq_length, tokenizer) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() test_loss, test_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 predictions, true_labels = [], [] for input_ids, input_mask, segment_ids, label_ids in tqdm( eval_dataloader, desc="Evaluating"): input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids) logits = model(input_ids, segment_ids, input_mask) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() # get index till '[SEP]' #print("label_list index SEP : ",label_list.index('[SEP]')) pred_xx = [list(p) for p in np.argmax(logits, axis=2)] pred_xx = [i[:i.index(label_list.index('[SEP]'))] for i in pred_xx] label_ids_xx = [ i[:i.index(label_list.index('[SEP]'))] for i in label_ids.tolist() ] #print(label_ids_xx) #print(pred_xx) # new add tmp_s = [ max(len(i), len(j)) for i, j in zip(label_ids_xx, pred_xx) ] tmp_u = [(i + [31] * (k - len(i)) if len(i) != k else i, j + [31] * (k - len(j)) if len(j) != k else j) for i, j, k in zip(label_ids_xx, pred_xx, tmp_s)] tmp_d1 = [h[0] for h in tmp_u] tmp_d2 = [h[1] for h in tmp_u] #print([list(p) for p in np.argmax(logits, axis=2)][:5]) #tmp_eval_accuracy = flat_accuracy(logits, label_ids) tmp_eval_accuracy = flat_accc(pred_xx, label_ids_xx) #tmp_eval_accuracy = flat_accc(tmp_d1, tmp_d2) predictions.extend(tmp_d2) true_labels.append(tmp_d1) #print("tmp accuracy : ",tmp_eval_accuracy) test_loss += tmp_eval_loss.mean().item() test_accuracy += tmp_eval_accuracy nb_eval_examples += input_ids.size(0) nb_eval_steps += 1 test_loss = test_loss / nb_eval_steps test_accuracy = test_accuracy / nb_eval_steps loss = tr_loss / nb_tr_steps if args.do_train else None pred_tags = [[label_list[p_i] if p_i != 31 else 'XXX' for p_i in p] for p in predictions] valid_tags = [[ label_list[l_ii] if l_ii != 31 else 'YYY' for l_ii in l_i ] for l in true_labels for l_i in l] print("valid_tags : ", valid_tags[:10]) print("pred_tags : ", pred_tags[:10]) print("Test F1-Score: {}".format(f1_score(valid_tags, pred_tags))) print("Test accuracy_score : {}".format( accuracy_score(valid_tags, pred_tags))) print("Test classification_report : {}".format( classification_report(valid_tags, pred_tags))) #print("X Test F1-Score: {}".format(f1_score(true_labels, predictions))) #print("X Test accuracy_score : {}".format(accuracy_score(true_labels, predictions))) #print("X Test classification_report : {}".format(classification_report(true_labels, predictions))) result = { 'test_loss': test_loss, 'test_accuracy': test_accuracy, 'global_step': global_step, 'loss': loss } print(result) output_test_file = os.path.join(args.output_dir, "test_results.txt") with open(output_test_file, "w") as writer: for key in sorted(result.keys()): writer.write("%s = %s\n" % (key, str(result[key]))) if args.do_pred and (args.local_rank == -1 or torch.distributed.get_rank() == 0): #eval_examples = processor.get_dev_examples(args.data_dir) model.eval() while True: print( 'Enter a text to get NER. otherwise press Ctrl+C to close session.' ) text_a = input('>>>') #"Japan began the defence of their Asian Cup title with a lucky 2-1 win against Syria in a Group C championship match on Friday . ." eval_examples = { 'text_a': text_a, 'text_b': "The foodservice pie business does not fit our long-term growth strategy .", 'label': '1', 'guid': '12345' } eval_features = convert_examples_to_features_test( eval_examples, label_list, args.max_seq_length, tokenizer) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor( [f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor( [f.segment_ids for f in eval_features], dtype=torch.long) all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) #model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 predictions, true_labels = [], [] for input_ids, input_mask, segment_ids, label_ids in tqdm( eval_dataloader, desc="Evaluating"): input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids) logits = model(input_ids, segment_ids, input_mask) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() pred_xx = [list(p) for p in np.argmax(logits, axis=2)] pred_xx = [ i[:i.index(label_list.index('[SEP]'))] for i in pred_xx ] print(pred_xx) print([[label_list[p_i] if p_i != 31 else 'XXX' for p_i in p] for p in pred_xx])