# Preparing eval data eval_data = [ ["Theoden was the king of Rohan", "true"], ["Merry was the king of Rohan", "false"], ] eval_df = pd.DataFrame(eval_data) eval_df.columns = ["text", "labels"] model_args = ClassificationArgs() model_args.reprocess_input_data = True model_args.overwrite_output_dir = True model_args.evaluate_during_training = True model_args.manual_seed = 4 model_args.use_multiprocessing = True model_args.train_batch_size = 16 model_args.eval_batch_size = 8 model_args.labels_list = ["true", "false"] model_args.wandb_project = "Simple Sweep" def train(): # Initialize a new wandb run wandb.init() # Create a TransformerModel model = ClassificationModel("roberta", "roberta-base", use_cuda=True, args=model_args, sweep_config=wandb.config,) # Train the model model.train_model(train_df, eval_df=eval_df) # Evaluate the model
"save": model_saves[int(sys.argv[2])] } df = pd.read_csv("data.csv") train_df = df.iloc[:wandb_config["samples"], :] train_df.columns = ["text", "labels"] eval_df = df.iloc[wandb_config["samples"]:, :] eval_df.columns = ["text", "labels"] model_args = ClassificationArgs() model_args.num_train_epochs = wandb_config["epochs"] model_args.eval_batch_size = wandb_config["eval_batch_size"] model_args.train_batch_size = wandb_config["train_batch_size"] model_args.wandb_project = "transformer-aes" model_args.wandb_kwargs = { "name": "{}-{}".format(wandb_config["model"], wandb_config["samples"]) } model_args.learning_rate = wandb_config["lr"] model_args.model = wandb_config["model"] model_args.samples = wandb_config["samples"] # model_args.max_seq_length = wandb_config["max_seq_length"] model_args.regression = True model_args.no_save = True model_args.overwrite_output_dir = True model_args.logging_steps = 1 model_args.evaluate_during_training = True model_args.evaluate_during_training_verbose = True