config.py

import torch

class Args():
    def __init__(self):
        self.input_dir = 'data/save/few_shot.pickle'
        self.output_dir = 'output-small'
        self.model_type = 'gpt2'
        self.model_name_or_path = 'microsoft/DialoGPT-small'
        self.config_name = 'microsoft/DialoGPT-small'
        self.tokenizer_name = 'microsoft/DialoGPT-small'
        self.cache_dir = 'cached'
        self.block_size = 512
        self.do_eval = True
        self.per_gpu_train_batch_size = 4
        self.per_gpu_eval_batch_size = 4
        self.gradient_accumulation_steps = 1
        self.learning_rate = 5e-5
        self.weight_decay = 0.0
        self.adam_epsilon = 1e-8
        self.max_grad_norm = 1.0
        self.num_train_epochs = 3
        self.max_steps = -1
        self.warmup_steps = 0
        self.logging_steps = 1000
        self.save_steps = 3500
        self.save_total_limit = None
        self.eval_all_checkpoints = False
        self.no_cuda = False
        self.overwrite_output_dir = True
        self.overwrite_cache = True
        self.should_continue = False
        self.local_rank = -1
        self.fp16 = False
        self.fp16_opt_level = 'O1'
        self.device = torch.device("cuda")
        # self.n_gpu = torch.cuda.device_count()
        self.n_gpu = 1
        self.nli = {"model_card": "tals/albert-xlarge-vitaminc-mnli", "entailment_idx": 0, "contradiction_idx": 1}
        self.do_train = True
        self.seed = 4
        self.evaluate_during_training = False   # Model-Agnostic Meta-Learning (MAML)
        self.constractive = True    # Contrastive Learning
        self.constractive_lambada = 0.1
        self.zero_shot = False
        self.use_prompts = False
        self.oracle = False