问题描述
我正在使用BERT进行文本分类的项目
我得到约。使用简单的变压器即可达到90％的精度。 但是，使用我自己的针对循环的训练（此处未发布）或使用来自变压器库的训练器模块，我只能得到60％的收益。 两者都是使用简单转换器的默认参数完成的。
我真的很难理解为什么性能会有如此差异
数据集来自kaggle：https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news
进口：
from transformers import BertForSequenceClassification,AdamW,BertTokenizer,get_linear_schedule_with_warmup,Trainer,TrainingArguments
import torch
from torch.utils.data import DataLoader,RandomSampler,SequentialSampler,TensorDataset
import pandas as pd
from pathlib import Path
import sklearn
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score,precision_recall_fscore_support
import numpy as np
from torch.nn import functional as F
from collections import defaultdict
import random
from simpletransformers.classification import ClassificationModel
数据预处理：
#loading phrase bank dataset
phrase_bank_dataset = "all-data.csv"
phrase_bank_dataset_file = Path(phrase_bank_dataset)
file_loaded = False
while not file_loaded:
if phrase_bank_dataset_file.exists():
phrase_bank_dataset = pd.read_csv(phrase_bank_dataset,encoding='latin-1')
phrase_bank_dataset = phrase_bank_dataset.values.tolist()
file_loaded = True
print("Dataset Loaded")
else:
print("File not Found")
#correcting the format of phrase bank dataset
phrase_dataset = pd.DataFrame(columns=["news","sentiment"])
for ele in phrase_bank_dataset:
news = ele[1]
#converting sentiment text into numbers
sentiment = 0 if ele[0] == 'negative' else 1 if ele[0] == 'neutral' else 2
row = [news,sentiment]
phrase_dataset.loc[len(phrase_dataset)] = row
print(phrase_dataset)
简单的变形金刚代码：
model = ClassificationModel('bert','bert-base-cased',num_labels=3,use_cuda=True)
train,eva = train_test_split(labeled_dataset,test_size = 0.2)
train_df = pd.DataFrame({
'text': train['news'],'label': train['sentiment']
})
eval_df = pd.DataFrame({
'text': eva['news'],'label': eva['sentiment']
})
model.train_model(train_df)
result,model_outputs,wrong_predictions = model.eval_model(eval_df)
lst = []
for arr in model_outputs:
lst.append(np.argmax(arr))
true = eval_df['label'].tolist()
predicted = lst
sklearn.metrics.accuracy_score(true,predicted)
变形金刚培训师代码：
tokenizer = BertTokenizer.from_pretrained('bert-base-cased',stride = 0.8)
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)
#custom dataset class
class 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 list
def tokenize_headlines(headlines,labels,tokenizer):
encodings = tokenizer.batch_encode_plus(
headlines,add_special_tokens = True,pad_to_max_length = True,return_attention_mask = True
)
dataset = NewsSentimentDataset(encodings,labels)
return dataset
#splitting dataset into training and validation set
all_headlines = phrase_dataset['news'].tolist()
all_labels = phrase_dataset['sentiment'].tolist()
train_headlines,val_headlines,train_labels,val_labels = train_test_split(phrase_headlines,phrase_labels,test_size=.2)
val_dataset = tokenize_headlines(val_headlines,val_labels,tokenizer)
train_dataset = tokenize_headlines(train_headlines,tokenizer)
#data loader
train_batch_size = 8
val_batch_size = 8
train_data_loader = DataLoader(train_dataset,batch_size = train_batch_size,sampler=RandomSampler(train_dataset))
val_data_loader = DataLoader(val_dataset,batch_size = val_batch_size,sampler=SequentialSampler(val_dataset))
#optimizer and scheduler
num_epochs = 1
num_steps = len(train_data_loader) * num_epochs
optimizer = AdamW(model.parameters(),lr=4e-5,eps=1e-8)
scheduler = get_linear_schedule_with_warmup(optimizer,num_warmup_steps=30,num_training_steps=num_steps)
#training and evaluation with trainer moduel from huggingfaces
def compute_metrics(pred):
labels = pred.label_ids
preds = pred.predictions.argmax(-1)
precision,recall,f1,_ = precision_recall_fscore_support(labels,preds,average='micro')
acc = accuracy_score(labels,preds)
return {
'accuracy': acc,'f1': f1,'precision': precision,'recall': recall
}
training_args = TrainingArguments(
output_dir='./results',# output directory
num_train_epochs=1,# total number of training epochs
per_device_train_batch_size=8,# batch size per device during training
per_device_eval_batch_size=8,# batch size for evaluation
warmup_steps=0,# number of warmup steps for learning rate scheduler
weight_decay=0,# strength of weight decay
logging_dir='./logs',# directory for storing logs
logging_steps=10,)
trainer = Trainer(
model=model,# the instantiated ? Transformers model to be trained
args=training_args,# training arguments,defined above
train_dataset=train_dataset,# training dataset
eval_dataset=val_dataset,# evaluation dataset
compute_metrics=compute_metrics
)
trainer.train()
trainer.evaluate()
解决方法
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