Demo.py

import torch
from transformers import RobertaTokenizer, RobertaConfig, RobertaModel, AdamW
import numpy as np
from dataSet import PhpDataset
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
from pathlib import Path
from pretreatment.code_pre import code_pre

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('/home/xxxx/Document/data/aclImdb/train')
test_texts, test_labels = read_imdb_split('/home/xxxx/Document/data/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)

import torch
from transformers import RobertaTokenizer, RobertaConfig, RobertaModel
import numpy as np

from transformers import DistilBertTokenizerFast
tokenizer = RobertaTokenizer.from_pretrained("microsoft/codebert-base")
# 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)


a = tokenizer.save_pretrained(save_directory='./aaa')




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)

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=4,  # batch size per device during training
    per_device_eval_batch_size=4,   # 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 = RobertaModel.from_pretrained("microsoft/codebert-base")
# 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()