我正在使用 DistilBERT 對我的資料集進行情感分析。資料集包含文本和每行的標簽，用于標識文本是正面還是負面的電影評論（例如：1 = 正面和 0 = 負面）。這是 Huggingface 檔案中的代碼（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()
我想知道在新資料上測驗這個模型。所以，我有一個資料框，其中包含每一行的一段文本/評論，我想預測標簽。有誰知道我會怎么做？我很抱歉，我對此很陌生，非常感謝您的幫助！我試著接收文本，清理它，然后做
prediction = model.predict(text)
我收到一條錯誤訊息，說 DistilBERT 沒有屬性 .predict。
uj5u.com熱心網友回復：
您從檔案中共享的代碼基本上涵蓋了訓練和評估回圈。請注意，您的共享代碼包含兩種微調方法，一種是使用訓練器進行微調，其中還包括評估，另一種是使用原生 Pytorch/TF，其中僅包含訓練部分而不包含評估部分。
以下是如何調整本機方法以在測驗集上生成預測：
# Put model in evaluation mode
model.eval()
# Tracking variables for storing ground truth and predictions
predictions , true_labels = [], []
# Prediction Loop
for batch in test_dataset:
# Unpack the inputs from our dataloader and move to GPU/accelerator
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
labels = batch['labels'].to(device)
# Telling the model not to compute or store gradients, saving memory and
# speeding up prediction
with torch.no_grad():
# Forward pass, calculate logit predictions
outputs = model(input_ids, attention_mask=attention_mask,
labels=labels)
logits = outputs[0]
# Move logits and labels to CPU
logits = logits.detach().cpu().numpy()
label_ids = labels.to('cpu').numpy()
# Store predictions and true labels
predictions.append(logits)
true_labels.append(label_ids)
執行此回圈后，預測將包含對數，即任何形式的歸一化之前模型的概率分布。您可以使用以下方法從 logits 中選擇得分最高的標簽，并生成分類報告
from sklearn.metrics import classification_report, accuracy_score
# Combine the results across all batches.
flat_predictions = np.concatenate(predictions, axis=0)
# For each sample, pick the label (0 or 1) with the higher score.
flat_predictions = np.argmax(flat_predictions, axis=1).flatten()
# Combine the correct labels for each batch into a single list.
flat_true_labels = np.concatenate(true_labels, axis=0)
# Accuracy
print(accuracy_score(flat_true_labels, flat_predictions))
# Classification Report
report = classification_report(flat_true_labels, flat_predictions)
為了更優雅地執行預測，您可以創建一個 BERTModel 類，該類將包含用于處理標記化、創建資料加載器、運行預測等的不同方法和變數。
uj5u.com熱心網友回復：
如果只是想使用模型，可以使用對應的pipeline：
from transformers import pipeline
classifier = pipeline('sentiment-analysis')
然后你可以使用它：
classifier("I hate this book")
uj5u.com熱心網友回復：
你可以試試這個例子的代碼：Link-BERT
您將根據 BERT 模型排列資料集。此鏈接中的 D 部分，您只需更改模型名稱和資料集即可。
轉載請註明出處，本文鏈接：https://www.uj5u.com/qukuanlian/339251.html