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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) |