训练准确率几乎为零，测试准确率低

Question

我真的是机器学习的新手，总体上我对编码不是很精通。然而，我们需要查看我们商店的客户反馈，平均每年相当多，但我们不能区分积极，消极和中性的百分比。

目前，我正在尝试训练一个Bert模型来进行简单的多标签情感分析。输入是我们商店的顾客反馈。客户反馈并不总是如此明确，因为客户确实倾向于长篇大论地讲述他们的经验，他们的情绪并不总是那么明确。然而，我们设法得到了积极的，消极的和中性的，每组2247个样本。

但当我尝试训练它时，训练准确率约为0.4%，这是非常低的。验证分数约为60%。F1-每个标签的分数都在60%左右。我想知道可以做些什么来提高训练的准确性。我已经被困了一段时间了。请看一下我的代码，帮我解决这个问题。

我尝试了改变学习率(尝试了Bert建议的所有学习率和1e-5)，改变了最大镜头，改变了时长，改变了辍读率(0.1，0.2，0.3，0.4，0.5)，但到目前为止都没有结果。

#read dataset
df = pd.read_csv("data.csv",header=None, names=['content', 'sentiment'], sep='\;', lineterminator='\r',encoding = "ISO-8859-1",engine="python")

from sklearn.utils import shuffle

df = shuffle(df)

df['sentiment'] = df['sentiment'].replace(to_replace = [-1, 0, 1], value = [0, 1, 2])

df.head()

#Load pretrained FinBert model and get bert tokenizer from it
PRE_TRAINED_MODEL_NAME = 'TurkuNLP/bert-base-finnish-cased-v1'
tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)

#Choose sequence Length
token_lens = []

for txt in df.content:

 tokens = tokenizer.encode(txt, max_length=512)

 token_lens.append(len(tokens))

sns.distplot(token_lens)

plt.xlim([0, 256]);

plt.xlabel('Token count');

MAX_LEN = 260


#Make a PyTorch dataset
class FIDataset(Dataset):

 def __init__(self, texts, targets, tokenizer, max_len):

   self.texts = texts

   self.targets = targets

   self.tokenizer = tokenizer

   self.max_len = max_len

 def __len__(self):

   return len(self.texts)

 def __getitem__(self, item):

   text = str(self.texts[item])

   target = self.targets[item]

   encoding = self.tokenizer.encode_plus(

     text,

     add_special_tokens=True,

     max_length=self.max_len,

     return_token_type_ids=False,

     pad_to_max_length=True,

     return_attention_mask=True,

     return_tensors='pt',

   )

   return {

     'text': text,

     'input_ids': encoding['input_ids'].flatten(),

     'attention_mask': encoding['attention_mask'].flatten(),

     'targets': torch.tensor(target, dtype=torch.long)

   }

#split test and train
df_train, df_test = train_test_split(

 df,

 test_size=0.1,

 random_state=RANDOM_SEED

)

df_val, df_test = train_test_split(

 df_test,

 test_size=0.5,

 random_state=RANDOM_SEED

)

df_train.shape, df_val.shape, df_test.shape

#data loader function
def create_data_loader(df, tokenizer, max_len, batch_size):

 ds = FIDataset(

   texts=df.content.to_numpy(),

   targets=df.sentiment.to_numpy(),

   tokenizer=tokenizer,

   max_len=max_len

 )

 return DataLoader(

   ds,

   batch_size=batch_size,

   num_workers=4

 )

#Load data into train, test, val
BATCH_SIZE = 16

train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)

val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)

test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)


# Sentiment Classifier based on Bert model just loaded
class SentimentClassifier(nn.Module):

 def __init__(self, n_classes):

   super(SentimentClassifier, self).__init__()

   self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)

   self.drop = nn.Dropout(p=0.1)

   self.out = nn.Linear(self.bert.config.hidden_size, n_classes)

 def forward(self, input_ids, attention_mask):
   returned = self.bert(
       
       input_ids=input_ids,
       attention_mask=attention_mask
   )
   pooled_output = returned["pooler_output"]
   output = self.drop(pooled_output)
   
   return self.out(output)


#Create a Classifier instance and move to GPU
model = SentimentClassifier(3)

model = model.to(device)


#Optimize with AdamW
EPOCHS = 5

optimizer = AdamW(model.parameters(), lr= 2e-5, correct_bias=False)

total_steps = len(train_data_loader) * EPOCHS

scheduler = get_linear_schedule_with_warmup(

 optimizer,

 num_warmup_steps=0,

 num_training_steps=total_steps

)

loss_fn = nn.CrossEntropyLoss().to(device)

#Train each Epoch function
def train_epoch(

 model,

 data_loader,

 loss_fn,

 optimizer,

 device,

 scheduler,

 n_examples

):
 
 model = model.train()

 losses = []

 correct_predictions = 0

 for d in data_loader:

   input_ids = d["input_ids"].to(device)

   attention_mask = d["attention_mask"].to(device)

   targets = d["targets"].to(device)

   outputs = model(

     input_ids=input_ids,

     attention_mask=attention_mask

   )

   _, preds = torch.max(outputs, dim=1)

   loss = loss_fn(outputs, targets)

   correct_predictions += torch.sum(preds == targets)

   losses.append(loss.item())

   loss.backward()

   nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

   optimizer.step()

   scheduler.step()

   optimizer.zero_grad()
   
   return correct_predictions.double() / n_examples, np.mean(losses)

#Eval model function
def eval_model(model, data_loader, loss_fn, device, n_examples):

 model = model.eval()

 losses = []

 correct_predictions = 0

 with torch.no_grad():
       
   torch.cuda.empty_cache()

   for d in data_loader:

     input_ids = d["input_ids"].to(device)

     attention_mask = d["attention_mask"].to(device)

     targets = d["targets"].to(device)

     outputs = model(

       input_ids=input_ids,

       attention_mask=attention_mask

     )

     _, preds = torch.max(outputs, dim=1)

     loss = loss_fn(outputs, targets)

     correct_predictions += torch.sum(preds == targets)

     losses.append(loss.item())
       
   
   
 return correct_predictions.double() / n_examples, np.mean(losses)

#training loop through each epochs

import torch

torch.cuda.empty_cache()

history = defaultdict(list)

best_accuracy = 0

if __name__ == '__main__':  

   for epoch in range(EPOCHS):

     print(f'Epoch {epoch + 1}/{EPOCHS}')

     print('-' * 10)

     train_acc, train_loss = train_epoch(

       model,

       train_data_loader,

       loss_fn,

       optimizer,

       device,

       scheduler,

       len(df_train)

     )

     print(f'Train loss {train_loss} accuracy {train_acc}')

     val_acc, val_loss = eval_model(

       model,

       val_data_loader,

       loss_fn,

       device,

       len(df_val)

     )

     print(f'Val   loss {val_loss} accuracy {val_acc}')

     print()

     history['train_acc'].append(train_acc)

     history['train_loss'].append(train_loss)

     history['val_acc'].append(val_acc)

     history['val_loss'].append(val_loss)

     if val_acc > best_accuracy:

       torch.save(model.state_dict(), 'best_model_state.bin')

       best_accuracy = val_acc

--编辑:我已经打印出了preds和目标，以及训练和val精度

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Answer 1

在这里，_, preds = torch.max(outputs, dim=1)，您可能需要argmax，而不是max

打印出preds和targets，以便更好地了解正在发生的情况。

在打印出preds和目标后进行编辑。对于周期4和5，preds与targets完全匹配，因此训练精度应该是1。我认为问题是精度除以n_examples，这是整个训练数据集中的示例数量，而它应该除以纪元中的示例数量。