Datasets:
File size: 2,414 Bytes
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# Standard
import random
import json
# PIP
import numpy as np
import torch
import torch.nn as nn
import pytorch_lightning as pl
# Custom
class Config:
# User Setting
SEED = 94
if_arabic = False
def __init__(self, filename, SEED=None):
if SEED:
self.SEED = SEED
self.set_random_seed()
self.read_json(filename)
self.model = self.config_df['model']
self.max_length = self.config_df['max_length']
self.checkpoint_filename = self.config_df['checkpoint_filename']
self.best_filename = self.config_df['best_filename']
self.additional_tokens = self.config_df['additional_tokens']
self.remove_special_tokens = self.config_df['remove_special_tokens']
self.get_special_tokens()
self.train_data = self.config_df['train_data']
self.val_data = self.config_df['val_data']
self.test_data = self.config_df['test_data']
self.test_res = self.config_df['test_res']
self.sent_col = self.config_df['sent_col']
self.label_col = self.config_df['label_col']
self.num_labels = self.config_df['num_labels']
self.test_res_col = self.config_df['test_res_col']
self.test_col = self.config_df['test_col']
self.batch_size = self.config_df['batch_size']
self.num_workers = self.config_df['num_workers']
self.distributed=self.config_df['distributed']
self.train = self.config_df['train']
self.evaluate = self.config_df['evaluate']
self.test = self.config_df['test']
self.resume = self.config_df['resume']
self.resume_model = self.config_df['resume_model']
self.start_epoch = 0
self.epochs = 6
def get_special_tokens(self,filename='{ANY_SPECIAL_TOKENS}.json'):
with open(filename,'r') as f:
self.additional_tokens += list(json.load(f).values())
def read_json(self,filename):
with open(filename,'r') as f:
self.config_df = json.load(f)
def set_random_seed(self):
print(f'=> SEED : {self.SEED}')
random.seed(self.SEED)
np.random.seed(self.SEED)
torch.manual_seed(self.SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.cuda.manual_seed_all(self.SEED)
pl.seed_everything(self.SEED)
#
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