configuration_ganbert.py

from transformers import PretrainedConfig
import logging
import datasets
from datasets import load_dataset
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datasets import load_metric
import transformers
import torch
import io
import torch.nn.functional as F
import random
import numpy as np
import time
import math
import datetime
import torch.nn as nn
from torch.utils.data import Dataset,TensorDataset, DataLoader, RandomSampler, SequentialSampler
from transformers import (
    AutoModel,
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    default_data_collator,
    set_seed,
    get_constant_schedule_with_warmup,
    Trainer,TrainingArguments,EarlyStoppingCallback)
from datasets import Dataset
import torch.nn as nn
import torch.nn.functional as F
import sys

class GanBertConfig(PretrainedConfig):
    model_type = "ganbert"

    def __init__(
        self,
        out_dropout_rate = 0.4,
        num_hidden_layers_g = 2,
        num_hidden_layers_d = 1,
        pos_class_weight = 10,
        batch_size = 64,
        noise_size = 100,
        num_train_examples = 77450,
        epochs = 10,
        epsilon = 1e-08,
        learning_rate_discriminator = 1e-05,
        learning_rate_generator = 1e-05,
        warmup_proportion= 0.1,      
        model_number = -2,
        device ='cuda',
        **kwargs,
    ):
        self.out_dropout_rate=out_dropout_rate
        self.num_hidden_layers_g=num_hidden_layers_g
        self.num_hidden_layers_d=num_hidden_layers_d
        self.pos_class_weight=pos_class_weight
        self.model_number = model_number
        self.learning_rate_discriminator=learning_rate_discriminator
        self.learning_rate_generator=learning_rate_generator
        self.warmup_proportion=warmup_proportion
        self.epsilon=epsilon
        self.num_train_examples=num_train_examples
        self.epochs = epochs
        self.batch_size=batch_size
        self.noise_size = noise_size
        if torch.cuda.is_available():
          self.device = 'cuda'
        else:
          self.device = 'cpu'
        super().__init__(**kwargs)