def load_classification_model(): global trainer global tokenizer mod = 'mtn_models/pytorch_model.bin' tok = 'mtn_models/vocab.txt' conf = 'mtn_models/config.json' tokenizer = BertTokenizer.from_pretrained(tok, do_lower_case=False, do_basic_tokenize=True, never_split=never_split_tokens, truncation=True) config = PretrainedConfig.from_pretrained(conf, num_labels=6) model = BertForSequenceClassification.from_pretrained(mod, config=config) training_args = TrainingArguments("./train") training_args.do_train = True training_args.evaluate_during_training = True training_args.adam_epsilon = 1e-8 training_args.learning_rate = 2e-5 training_args.warmup_steps = 0 training_args.per_gpu_train_batch_size = 16 training_args.per_gpu_eval_batch_size = 16 training_args.num_train_epochs = 3 #training_args.logging_steps = (len(train_features) - 1) // training_args.per_gpu_train_batch_size + 1 training_args.save_steps = training_args.logging_steps training_args.seed = 42 trainer = Trainer(model=model, args=training_args)
help="The embedding file to swap.") try: get_ipython().run_line_magic('matplotlib', 'inline') args = parser.parse_args([]) except: args = parser.parse_args() # os.environ["WANDB_DISABLED"] = "false" if args.is_tensorboard else "true" os.environ["TRANSFORMERS_CACHE"] = "../huggingface_cache/" # if cache does not exist, create one if not os.path.exists(os.environ["TRANSFORMERS_CACHE"]): os.makedirs(os.environ["TRANSFORMERS_CACHE"]) training_args = TrainingArguments("tmp_trainer") training_args.no_cuda = args.no_cuda training_args.per_device_eval_batch_size = args.per_device_eval_batch_size training_args.per_gpu_eval_batch_size = args.per_device_eval_batch_size training_args_dict = training_args.to_dict() _n_gpu = training_args_dict["_n_gpu"] del training_args_dict["_n_gpu"] training_args_dict["n_gpu"] = _n_gpu HfParser = HfArgumentParser((TrainingArguments)) training_args = HfParser.parse_dict(training_args_dict)[0] TASK_CONFIG = {"classification": ("text", None)} # Load pretrained model and tokenizer NUM_LABELS = 3 MAX_SEQ_LEN = 128 config = AutoConfig.from_pretrained(args.model_type, num_labels=3, finetuning_task=args.task_name,