| 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) |