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# 1 Prepate dataset
# 2 Load pretrained Tokenizer, call it with dataset -> encoding
# 3 Build PyTorch Dataset with encodings
# 4 Load pretrained model
# 5 a) Load Trainer and train it
# b) or use native Pytorch training pipeline
from pathlib import Path
from sklearn.model_selection import train_test_split
import torch
from torch.utils.data import Dataset
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
from transformers import Trainer, TrainingArguments
model_name = "distilbert-base-uncased"
def read_imdb_split(split_dir): # helper function to get text and label
split_dir = Path(split_dir)
texts = []
labels = []
for label_dir in ["pos", "neg"]:
thres = 0
for text_file in (split_dir/label_dir).iterdir():
if thres < 100:
f = open(text_file, encoding='utf8')
texts.append(f.read())
labels.append(0 if label_dir == "neg" else 1)
thres += 1
return texts, labels
train_texts, train_labels = read_imdb_split("aclImdb/train")
test_texts, test_labels = read_imdb_split("aclImdb/test")
train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)
class IMDBDataset(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)
tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
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)
train_dataset = IMDBDataset(train_encodings, train_labels)
val_dataset = IMDBDataset(val_encodings, val_labels)
test_dataset = IMDBDataset(test_encodings, test_labels)
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=2,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
warmup_steps=500,
learning_rate=5e-5,
weight_decay=0.01,
logging_dir='./logs',
logging_steps=10
)
model = DistilBertForSequenceClassification.from_pretrained(model_name)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=val_dataset
)
trainer.train()
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