我在一个金融新闻数据集上对BERT进行微调。不幸的是，BERT似乎陷入了局部最小值。它满足于总是预测同一类别。
- 平衡数据集不起作用
- 调整参数也同样不起作用
老实说，我不确定是什么导致了这个问题。使用simpletransformers库时，我得到了非常好的结果。如果有人能帮我，我将非常感激。非常感谢！
完整代码在GitHub上：https://github.com/Bene939/BERT_News_Sentiment_Classifier
代码：
from transformers import BertForSequenceClassification, AdamW, BertTokenizer, get_linear_schedule_with_warmup, Trainer, TrainingArgumentsimport torchfrom torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDatasetimport pandas as pdfrom pathlib import Pathimport sklearnfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score, precision_recall_fscore_supportimport numpy as npfrom torch.nn import functional as Ffrom collections import defaultdictimport random#defining tokenizer, model and optimizertokenizer = BertTokenizer.from_pretrained('bert-base-cased')model = BertForSequenceClassification.from_pretrained('bert-base-cased', num_labels=3)if torch.cuda.is_available(): print("\nUsing: ", torch.cuda.get_device_name(0)) device = torch.device('cuda')else: print("\nUsing: CPU") device = torch.device('cpu')model = model.to(device)#loading datasetlabeled_dataset = "news_headlines_sentiment.csv"labeled_dataset_file = Path(labeled_dataset)file_loaded = Falsewhile not file_loaded: if labeled_dataset_file.exists(): labeled_dataset = pd.read_csv(labeled_dataset_file) file_loaded = True print("Dataset Loaded") else: print("File not Found")print(labeled_dataset)#counting sentimentsnegative = 0neutral = 0positive = 0for idx, row in labeled_dataset.iterrows(): if row["sentiment"] == 0: negative += 1 elif row["sentiment"] == 1: neutral += 1 else: positive += 1print("Unbalanced Dataset")print("negative: ", negative)print("neutral: ", neutral)print("positive: ", positive)#balancing dataset to 1/3 per sentimentfor idx, row in labeled_dataset.iterrows(): if row["sentiment"] == 0: if negative - neutral != 0: index_name = labeled_dataset[labeled_dataset["news"] == row["news"]].index labeled_dataset.drop(index_name, inplace=True) negative -= 1 elif row["sentiment"] == 2: if positive - neutral != 0: index_name = labeled_dataset[labeled_dataset["news"] == row["news"]].index labeled_dataset.drop(index_name, inplace=True) positive -= 1#custom dataset classclass NewsSentimentDataset(torch.utils.data.Dataset): def __init__(self, encodings, labels): self.encodings = encodings self.labels = labels def __getitem__(self, idx): item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()} item['labels'] = torch.tensor(self.labels[idx]) return item def __len__(self): return len(self.labels)#method for tokenizing dataset listdef tokenize_headlines(headlines, labels, tokenizer): encodings = tokenizer.batch_encode_plus( headlines, add_special_tokens = True, truncation = True, padding = 'max_length', return_attention_mask = True, return_token_type_ids = True ) dataset = NewsSentimentDataset(encodings, labels) return dataset#splitting dataset into training and validation set#load news sentiment datasetall_headlines = labeled_dataset['news'].tolist()all_labels = labeled_dataset['sentiment'].tolist()train_headlines, val_headlines, train_labels, val_labels = train_test_split(all_headlines, all_labels, test_size=.2)val_dataset = tokenize_headlines(val_headlines, val_labels, tokenizer)train_dataset = tokenize_headlines(train_headlines, val_labels, tokenizer)#data loadertrain_batch_size = 8val_batch_size = 8train_data_loader = DataLoader(train_dataset, batch_size = train_batch_size, shuffle=True)val_data_loader = DataLoader(val_dataset, batch_size = val_batch_size, sampler=SequentialSampler(val_dataset))#optimizer and schedulernum_epochs = 1num_steps = len(train_data_loader) * num_epochsoptimizer = AdamW(model.parameters(), lr=5e-5, eps=1e-8)scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_steps*0.06, num_training_steps=num_steps)#training and evaluationseed_val = 64random.seed(seed_val)np.random.seed(seed_val)torch.manual_seed(seed_val)torch.cuda.manual_seed_all(seed_val)for epoch in range(num_epochs): print("\n###################################################") print("Epoch: {}/{}".format(epoch+1, num_epochs)) print("###################################################\n") #training phase average_train_loss = 0 average_train_acc = 0 model.train() for step, batch in enumerate(train_data_loader): input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) token_type_ids = batch['token_type_ids'].to(device) outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids = token_type_ids) loss = F.cross_entropy(outputs[0], labels) average_train_loss += loss if step % 40 == 0: print("Training Loss: ", loss) logits = outputs[0].detach().cpu().numpy() label_ids = labels.to('cpu').numpy() average_train_acc += sklearn.metrics.accuracy_score(label_ids, np.argmax(logits, axis=1)) print("predictions: ",np.argmax(logits, axis=1)) print("labels: ",label_ids) print("#############") optimizer.zero_grad() loss.backward() #maximum gradient clipping torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) optimizer.step() scheduler.step() model.zero_grad() average_train_loss = average_train_loss / len(train_data_loader) average_train_acc = average_train_acc / len(train_data_loader) print("======Average Training Loss: {:.5f}======".format(average_train_loss)) print("======Average Training Accuracy: {:.2f}%======".format(average_train_acc*100)) #validation phase average_val_loss = 0 average_val_acc = 0 model.eval() for step,batch in enumerate(val_data_loader): input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) token_type_ids = batch['token_type_ids'].to(device) pred = [] with torch.no_grad(): outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) loss = F.cross_entropy(outputs[0], labels) average_val_loss += loss logits = outputs[0].detach().cpu().numpy() label_ids = labels.to('cpu').numpy() print("predictions: ",np.argmax(logits, axis=1)) print("labels: ",label_ids) print("#############") average_val_acc += sklearn.metrics.accuracy_score(label_ids, np.argmax(logits, axis=1)) average_val_loss = average_val_loss / len(val_data_loader) average_val_acc = average_val_acc / len(val_data_loader) print("======Average Validation Loss: {:.5f}======".format(average_val_loss)) print("======Average Validation Accuracy: {:.2f}%======".format(average_val_acc*100))###################################################Epoch: 1/1###################################################Training Loss: tensor(1.1006, device='cuda:0', grad_fn=<NllLossBackward>)predictions: [1 0 2 0 0 0 2 0]labels: [2 0 1 1 0 1 0 1]#############predictions: [2 2 0 0 0 2 0 0]labels: [1 2 1 0 2 0 1 2]#############predictions: [0 0 0 0 1 0 0 1]labels: [0 1 1 0 1 1 2 0]#############predictions: [0 0 0 2 0 1 0 0]labels: [0 0 0 2 0 0 2 1]#############predictions: [1 0 0 0 0 0 2 0]labels: [0 2 2 1 0 0 0 0]#############predictions: [0 0 0 0 0 1 0 0]labels: [1 0 2 2 2 1 1 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 2 2 2 2 0 2 0]#############predictions: [0 1 0 0 0 0 0 0]labels: [2 2 0 2 0 0 0 1]#############predictions: [0 0 0 0 0 2 0 1]labels: [0 1 0 2 2 0 1 2]#############predictions: [0 0 2 0 0 0 1 0]labels: [0 0 0 1 2 1 1 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 2 1 0 1 0 1 1]#############predictions: [0 2 0 0 0 0 0 0]labels: [2 2 0 1 0 1 2 1]#############predictions: [0 1 0 0 0 0 1 2]labels: [2 2 1 0 2 0 0 2]#############predictions: [0 0 1 1 1 1 0 1]labels: [1 2 1 1 1 1 2 2]#############predictions: [1 0 0 0 0 1 2 1]labels: [1 0 1 1 0 0 0 2]#############predictions: [0 1 1 1 1 0 2 1]labels: [2 2 1 2 2 1 1 2]#############predictions: [0 0 1 0 1 1 0 0]labels: [1 0 0 1 0 1 0 2]#############predictions: [1 2 0 0 1 2 0 0]labels: [0 2 2 1 2 0 1 0]#############predictions: [0 2 1 1 0 1 1 0]labels: [2 2 0 1 1 0 1 2]#############predictions: [1 0 1 1 1 1 1 0]labels: [0 2 0 1 0 1 2 2]#############predictions: [0 2 1 2 0 0 1 1]labels: [2 1 1 1 1 2 2 0]#############predictions: [0 1 2 2 2 1 1 2]labels: [2 2 1 1 2 1 0 1]#############predictions: [2 2 2 1 2 1 1 1]labels: [0 1 1 0 0 2 2 1]#############predictions: [1 2 2 2 1 2 1 2]labels: [0 0 0 0 2 0 1 2]#############predictions: [2 1 1 1 2 2 2 2]labels: [1 0 2 2 1 0 0 0]#############predictions: [2 1 2 2 2 1 2 2]labels: [2 1 1 1 1 1 2 2]#############predictions: [1 1 0 2 1 2 1 2]labels: [2 2 0 2 0 1 2 0]#############predictions: [0 1 1 2 0 1 2 1]labels: [2 2 2 1 2 2 0 1]#############predictions: [2 1 1 1 1 2 1 1]labels: [0 1 1 2 1 0 0 2]#############predictions: [1 2 2 0 1 1 1 2]labels: [0 1 2 1 2 1 0 1]#############predictions: [0 1 1 1 1 1 1 0]labels: [0 2 0 1 1 2 2 2]#############predictions: [1 2 1 1 2 1 1 0]labels: [0 2 2 2 0 0 1 0]#############predictions: [2 2 2 1 2 1 1 2]labels: [2 2 1 2 1 0 0 0]#############predictions: [2 2 1 2 2 2 1 2]labels: [1 1 2 2 2 0 2 1]#############predictions: [2 2 2 2 2 0 2 2]labels: [2 2 1 2 0 1 1 2]#############predictions: [1 1 2 1 2 2 0 1]labels: [2 1 1 1 0 0 2 2]#############predictions: [2 1 2 2 2 2 1 0]labels: [0 2 0 2 0 0 0 0]#############predictions: [2 2 2 2 2 2 2 2]labels: [1 1 0 2 0 1 2 1]#############predictions: [2 2 2 2 1 2 2 2]labels: [1 0 0 1 1 0 0 0]#############predictions: [2 2 2 1 2 2 2 2]labels: [1 0 1 1 0 2 2 0]#############Training Loss: tensor(1.1104, device='cuda:0', grad_fn=<NllLossBackward>)predictions: [2 0 1 2 1 2 2 0]labels: [2 2 0 0 1 0 0 2]#############predictions: [0 2 2 0 2 1 1 1]labels: [0 0 0 1 0 0 1 0]#############predictions: [0 2 2 0 1 1 1 2]labels: [2 1 1 1 2 2 1 0]#############predictions: [2 1 1 2 2 0 2 0]labels: [1 2 1 2 1 0 2 1]#############predictions: [0 2 2 0 0 2 1 2]labels: [0 0 2 2 0 0 2 0]#############predictions: [0 0 1 2 2 0 2 2]labels: [0 0 0 0 0 0 0 0]#############predictions: [1 1 2 1 2 0 1 2]labels: [0 0 2 0 0 0 1 1]#############predictions: [0 0 2 1 0 2 0 1]labels: [1 1 2 1 1 0 2 0]#############predictions: [0 0 0 0 1 0 0 0]labels: [2 2 1 1 2 1 1 1]#############predictions: [0 0 0 0 1 0 0 0]labels: [1 1 2 2 1 1 2 0]#############predictions: [0 0 0 0 0 1 1 1]labels: [2 0 1 1 0 1 2 2]#############predictions: [0 0 1 0 0 1 2 1]labels: [1 2 0 2 2 0 2 1]#############predictions: [1 1 1 1 0 1 0 1]labels: [2 0 1 0 1 0 1 2]#############predictions: [1 2 2 0 0 0 1 1]labels: [2 0 0 2 1 2 2 2]#############predictions: [1 0 2 1 0 2 2 0]labels: [0 0 2 1 2 1 1 1]#############predictions: [0 0 0 1 1 1 1 1]labels: [1 2 1 0 0 0 1 0]#############predictions: [1 1 1 0 1 1 0 1]labels: [0 2 1 2 1 2 2 0]#############predictions: [2 1 0 1 1 2 0 0]labels: [0 1 0 0 1 2 0 2]#############predictions: [0 1 1 0 0 1 0 1]labels: [1 0 0 2 2 1 1 2]#############predictions: [1 1 1 1 1 1 1 1]labels: [2 0 1 0 2 0 0 2]#############predictions: [1 0 0 1 0 1 0 2]labels: [1 0 0 1 1 2 2 1]#############predictions: [1 1 1 1 1 1 0 0]labels: [1 1 0 2 1 0 2 0]#############predictions: [1 1 2 1 0 1 0 0]labels: [0 2 1 2 1 1 0 2]#############predictions: [1 1 0 0 1 2 1 1]labels: [0 2 1 0 2 2 0 1]#############predictions: [0 1 1 0 0 1 0 1]labels: [0 0 1 2 2 0 1 2]#############predictions: [1 0 2 2 2 1 1 0]labels: [2 2 1 0 0 1 1 2]#############predictions: [1 2 2 1 1 2 1 1]labels: [1 0 0 1 0 0 0 0]#############predictions: [0 2 0 2 2 0 2 2]labels: [2 0 0 0 2 1 1 2]#############predictions: [0 0 1 0 1 0 2 2]labels: [0 0 1 0 1 0 2 0]#############predictions: [0 2 0 1 1 2 2 0]labels: [0 2 0 2 0 2 0 0]#############predictions: [2 2 2 2 2 2 2 1]labels: [2 2 1 1 0 0 2 2]#############predictions: [2 0 0 2 2 1 1 0]labels: [1 0 0 1 0 2 1 2]#############predictions: [2 0 0 2 0 2 2 0]labels: [2 2 2 2 0 1 1 1]#############predictions: [0 2 2 0 2 2 0 0]labels: [1 0 1 2 0 1 1 1]#############predictions: [0 0 0 0 0 0 0 2]labels: [2 1 1 0 0 0 1 2]#############predictions: [2 0 2 0 2 1 0 2]labels: [2 1 1 2 1 1 0 0]#############predictions: [1 1 2 0 2 0 2 2]labels: [0 2 1 2 1 2 1 0]#############predictions: [2 0 1 1 0 2 0 0]labels: [2 1 0 1 1 0 2 0]#############predictions: [2 0 0 2 0 2 1 0]labels: [0 0 0 0 2 1 0 1]#############predictions: [1 2 1 0 0 2 0 2]labels: [2 0 2 1 0 0 1 1]#############Training Loss: tensor(1.1162, device='cuda:0', grad_fn=<NllLossBackward>)predictions: [2 0 0 1 1 1 0 1]labels: [0 1 1 1 1 2 2 1]#############predictions: [0 2 0 1 2 0 0 1]labels: [2 2 1 0 1 0 0 0]#############predictions: [0 0 1 0 0 0 0 1]labels: [1 0 2 0 0 2 2 0]#############predictions: [2 1 2 2 0 1 2 0]labels: [2 0 1 0 2 1 0 1]#############predictions: [1 0 0 2 0 0 1 1]labels: [2 2 0 2 0 2 0 0]#############predictions: [0 0 1 0 0 0 0 0]labels: [2 2 2 1 2 2 2 2]#############predictions: [0 0 1 1 0 1 1 0]labels: [2 1 1 1 0 2 1 0]#############predictions: [0 0 0 1 0 0 1 0]labels: [2 0 2 2 0 0 1 2]#############predictions: [1 0 1 0 0 2 0 0]labels: [1 1 2 0 0 1 0 0]#############predictions: [2 1 0 0 0 1 0 0]labels: [1 2 0 0 0 0 0 0]#############predictions: [0 2 0 0 0 0 0 0]labels: [2 0 1 1 2 2 1 1]#############predictions: [0 1 0 0 0 1 0 2]labels: [0 2 1 1 0 0 1 2]#############predictions: [0 2 1 0 0 1 1 1]labels: [1 1 0 2 0 1 1 0]#############predictions: [0 1 1 0 0 0 1 0]labels: [0 0 1 0 1 2 1 1]#############predictions: [0 1 1 0 1 0 0 0]labels: [0 1 1 1 2 2 2 0]#############predictions: [0 0 0 0 1 1 0 0]labels: [2 0 2 2 1 2 2 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 2 0 2 2 0 1 1]#############predictions: [0 1 0 0 0 0 0 0]labels: [0 2 0 1 1 2 0 2]#############predictions: [1 1 0 1 0 1 0 2]labels: [1 2 0 0 2 2 2 1]#############predictions: [1 1 0 0 0 1 2 1]labels: [0 0 1 2 2 1 2 2]#############predictions: [1 1 1 0 1 1 2 0]labels: [0 0 0 2 0 1 0 2]#############predictions: [0 1 0 0 1 1 2 1]labels: [2 0 0 1 2 2 1 2]#############predictions: [1 0 0 0 1 0 0 1]labels: [1 2 2 2 2 1 0 1]#############predictions: [2 0 0 0 0 0 0 0]labels: [1 2 0 2 2 1 1 1]#############predictions: [2 0 1 1 0 0 1 0]labels: [0 0 0 0 2 2 1 1]#############predictions: [2 0 0 1 0 0 1 1]labels: [2 2 1 1 0 0 1 0]#############predictions: [1 1 1 1 1 2 0 0]labels: [0 0 2 1 0 0 0 0]#############predictions: [1 1 2 0 1 2 0 1]labels: [0 2 1 0 2 0 0 1]#############predictions: [0 0 2 1 0 2 0 1]labels: [1 2 0 2 2 1 0 0]#############predictions: [0 0 2 0 2 1 1 2]labels: [2 2 1 2 2 2 0 0]#############predictions: [0 1 0 0 0 0 2 1]labels: [1 1 0 1 1 1 0 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 1 0 0 2 0 0 2]#############predictions: [2 2 2 0 1 1 1 0]labels: [1 0 2 1 1 2 0 0]#############predictions: [0 0 1 0 0 0 2 0]labels: [0 1 2 1 1 0 0 0]#############predictions: [0 2 0 1 0 2 0 0]labels: [0 0 2 1 1 0 2 2]#############predictions: [0 0 1 2 0 2 0 1]labels: [2 2 0 0 0 2 2 2]#############predictions: [1 0 0 0 2 0 0 1]labels: [2 0 1 1 1 0 0 1]#############predictions: [0 1 0 0 0 0 0 2]labels: [1 1 1 0 0 0 2 2]#############predictions: [0 2 0 1 0 2 0 0]labels: [1 1 1 1 2 2 1 0]#############predictions: [1 2 0 0 0 0 0 0]labels: [2 0 2 1 0 1 1 1]#############Training Loss: tensor(1.2082, device='cuda:0', grad_fn=<NllLossBackward>)predictions: [0 2 0 0 0 0 2 0]labels: [1 0 2 1 2 2 1 1]#############predictions: [2 0 0 0 0 0 1 0]labels: [1 0 0 0 0 2 1 0]#############predictions: [0 0 0 0 2 1 1 1]labels: [0 2 2 0 1 2 1 1]#############predictions: [2 1 0 1 0 0 2 0]labels: [1 0 2 1 0 2 1 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 1 0 0 0 0 1 0]#############predictions: [0 2 1 0 0 0 1 1]labels: [0 2 2 2 2 1 1 0]#############predictions: [0 0 0 1 1 0 0 1]labels: [0 1 0 1 2 2 2 2]#############predictions: [0 0 0 1 1 1 1 2]labels: [2 2 1 2 0 1 1 1]#############predictions: [0 1 2 0 0 1 0 0]labels: [0 2 1 0 0 1 0 0]#############predictions: [1 1 1 1 0 0 0 0]labels: [2 1 2 1 0 2 2 1]#############predictions: [0 1 2 0 0 1 1 0]labels: [2 0 2 1 1 1 2 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 0 0 0 1 1 0 0]#############predictions: [0 0 0 0 0 1 2 2]labels: [2 2 1 1 0 2 1 2]#############predictions: [0 1 0 0 1 1 0 1]labels: [0 1 0 2 1 0 0 1]#############predictions: [0 2 2 0 0 0 0 2]labels: [0 0 2 1 2 2 0 1]#############predictions: [2 0 0 2 2 0 2 0]labels: [2 1 0 2 2 0 1 0]#############predictions: [0 2 2 0 2 1 1 2]labels: [1 1 0 0 2 1 0 0]#############predictions: [1 1 2 2 0 0 1 2]labels: [2 0 2 0 1 1 1 1]#############predictions: [0 1 1 0 0 1 1 0]labels: [0 2 1 0 0 2 2 0]#############predictions: [2 1 0 0 0 0 1 1]labels: [0 2 0 2 0 0 1 1]#############predictions: [1 2 0 1 2 0 0 0]labels: [1 0 1 1 0 2 2 2]#############predictions: [0 0 0 0 2 2 1 2]labels: [2 2 2 1 1 1 1 0]#############predictions: [1 2 0 1 0 0 2 0]labels: [2 2 1 1 1 0 2 0]#############predictions: [2 0 0 0 0 2 1]labels: [0 1 1 2 2 0 2]#############======Average Training Loss: 1.11279============Average Training Accuracy: 33.77%======predictions: [0 0 0 0 0 0 0 0]labels: [0 2 0 1 1 0 1 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 1 0 2 1 0 0 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 2 2 2 1 2 0 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 1 1 2 0 1 2 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 0 2 0 0 1 2 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 1 0 1 2 1 0 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 2 1 2 0 2 1 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 2 1 2 2 1 1 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 1 0 2 2 0 0 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 0 0 0 2 0 1 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 1 0 1 1 2 1 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 1 1 2 2 0 2 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 0 0 0 1 2 2 1]#############predictions: [0 0 0 1 0 0 0 0]labels: [0 0 1 1 0 2 0 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 0 1 2 2 0 1 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 1 1 2 2 2 0 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 0 2 1 2 0 0 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 0 0 0 2 2 0 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 1 1 0 1 0 1 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 2 2 2 2 2 1 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 0 2 1 1 0 2 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 2 1 1 2 0 0 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 2 2 1 2 2 0 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 2 0 1 0 2 0 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 0 1 2 1 1 0 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 0 0 1 2 1 1 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 1 1 1 1 0 1 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 2 1 0 0 2 2 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 1 1 0 0 0 2 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 0 1 1 1 2 0 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 0 0 1 2 1 2 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 0 2 0 1 1 0 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 0 1 0 1 0 1 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 0 1 2 2 1 0 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 2 2 0 2 0 0 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 1 1 1 1 0 0 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 0 1 2 2 0 0 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 0 1 2 0 0 1 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 0 0 0 1 0 0 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 1 2 1 1 2 2 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 0 2 2 2 2 1 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 2 2 2 1 0 0 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 0 2 2 2 1 0 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 1 0 0 1 0 2 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 1 1 0 0 0 0 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 2 1 2 0 2 1 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 1 2 0 1 2 1 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 2 2 0 0 0 1 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 0 1 0 0 0 0 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 2 0 1 1 2 2 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 2 0 0 0 2 2 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 1 2 1 1 1 2 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 0 0 0 2 0 1 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 1 2 1 0 2 2 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 0 0 1 2 2 2 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 0 0 0 2 1 1 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 0 2 0 2 1 0 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 1 0 2 0 0 1 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 1 0 0 1 0 0 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 2 2 2 0 0 1 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 0 1 1 1 0 2 0]#############predictions: [0 0 0 0 0 0 0 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1]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 2 2 1 0 2 0 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 0 2 0 2 2 1 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 2 0 0 1 0 1 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 2 1 0 0 0 2 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 2 0 1 2 1 0 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 1 2 2 2 2 0 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 0 0 1 2 0 2 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 0 2 1 1 1 1 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 1 0 0 0 1 0 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 1 2 0 1 2 2 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 1 1 1 2 1 0 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [1 0 1 1 1 0 0 2]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 2 0 0 0 0 1 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [0 0 1 1 2 0 0 1]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 1 1 1 0 1 0 0]#############predictions: [0 0 0 0 0 0 0 0]labels: [2 0 2 2 2 0 0 1]#############predictions: [0 0 0 0 0 0 0]labels: [2 2 1 1 0 0 1]#############======Average Validation Loss: 1.09527============Average Validation Accuracy: 35.53%======回答：
对于使用BERT进行多类分类/情感分析，’neutral’类必须是2！它不能在’negative’ = 0和’positive’ = 2之间
