我正在使用 DistilBERT 对我的数据集进行情绪分析。数据集包含文本和每行的标签，用于标识文本是正面还是负面的电影评论（例如：1 = 正面和 0 = 负面）。这是来自 huggingface 文档的代码 ( https://huggingface.co/transformers/custom_datasets.html?highlight=imdb )
#This dataset can be explored in the Hugging Face model hub (IMDb), and can be alternatively downloaded with the Datasets library with load_dataset("imdb").
wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
tar -xf aclImdb_v1.tar.gz
#This data is organized into pos and neg folders with one text file per example. Let’s write a function that can read this in.
from pathlib import Path
def read_imdb_split(split_dir):
split_dir = Path(split_dir)
texts = []
labels = []
for label_dir in ["pos", "neg"]:
for text_file in (split_dir/label_dir).iterdir():
texts.append(text_file.read_text())
labels.append(0 if label_dir is "neg" else 1)
return texts, labels
train_texts, train_labels = read_imdb_split('aclImdb/train')
test_texts, test_labels = read_imdb_split('aclImdb/test')
from sklearn.model_selection import train_test_split
train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)
from transformers import DistilBertTokenizerFast
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
train_encodings = tokenizer(train_texts, truncation=True, padding=True)
val_encodings = tokenizer(val_texts, truncation=True, padding=True)
test_encodings = tokenizer(test_texts, truncation=True, padding=True)
import torch
class IMDbDataset(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)
train_dataset = IMDbDataset(train_encodings, train_labels)
val_dataset = IMDbDataset(val_encodings, val_labels)
test_dataset = IMDbDataset(test_encodings, test_labels)
#Now that our datasets our ready, we can fine-tune a model either #with the Trainer/TFTrainer or with native PyTorch/TensorFlow. See #training.
#Fine-tuning with Trainer
#The steps above prepared the datasets in the way that the trainer is #expected. Now all we need to do is create a model to fine-tune, #define the TrainingArguments/TFTrainingArguments and instantiate a #Trainer/TFTrainer.
from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=3, # total number of training epochs
per_device_train_batch_size=16, # batch size per device during training
per_device_eval_batch_size=64, # batch size for evaluation
warmup_steps=500, # number of warmup steps for learning rate scheduler
weight_decay=0.01, # strength of weight decay
logging_dir='./logs', # directory for storing logs
logging_steps=10,
)
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
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
)
trainer.train()
#We can also train with Pytorch/Tensorflow
from torch.utils.data import DataLoader
from transformers import DistilBertForSequenceClassification, AdamW
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
model.to(device)
model.train()
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
optim = AdamW(model.parameters(), lr=5e-5)
for epoch in range(3):
for batch in train_loader:
optim.zero_grad()
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
labels = batch['labels'].to(device)
outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
loss = outputs[0]
loss.backward()
optim.step()
model.eval()
我想知道在一条新数据上测试这个模型。所以，我有一个数据框，其中包含每行的一段文本/评论，我想预测标签。有谁知道我会怎么做？我很抱歉，我对此很陌生，非常感谢任何帮助！我试着接收文本，清理它，然后做
prediction = model.predict(text)
我收到一条错误消息，说 DistilBERT 没有属性 .predict。