def init_training_args(self, model_path: str) -> TrainingArguments: r""" 构造训练参数. """ training_args = TrainingArguments(output_dir=model_path) training_args.logging_steps = 5000 training_args.save_steps = 5000 training_args.learning_rate = 2e-5 training_args.num_train_epochs = 3 training_args.per_device_train_batch_size = 32 training_args.fp16 = self.fp16 training_args.fp16_opt_level = "O1" return training_args
def fit(self, train_df, dev_df): """ fitting the model based on the train set. validation is done using the dev set Parameters ---------- :param train_df: dataframe a pandas dataframe containing data to be trained on :param dev_df: dataframe a pandas dataframe containing data to validate on :return: None all relevant results are saved under the the location provided to save the model in. Next a prediction can be done """ train_labels = Counter(train_df[self.label_col_name]).keys() num_labels = len(train_labels) dev_labels = Counter(train_df[self.label_col_name]).keys() if num_labels != len(dev_labels): raise IOError("train and dev datasets contain different number of labels") # creating a DF for train/test with relevant columns. # Not clear why the 'alpha' column is needed, but as written here # (https://towardsdatascience.com/https-medium-com-chaturangarajapakshe-text-classification-with-transformer-models-d370944b50ca) - it is required train_df = pd.DataFrame({ 'id': range(len(train_df)), 'label': train_df[self.label_col_name], 'alpha': ['a'] * train_df.shape[0], 'text': train_df["text"].replace(r'\n', ' ', regex=True) }) dev_df = pd.DataFrame({ 'id': range(len(dev_df)), 'label': dev_df[self.label_col_name], 'alpha': ['a'] * dev_df.shape[0], 'text': dev_df["text"].replace(r'\n', ' ', regex=True) }) # saving the DF to the new/old folder train_df.to_csv(os.path.join(self.saving_data_folder, "train.tsv"), index=False, columns=train_df.columns, sep='\t', header=False) dev_df.to_csv(os.path.join(self.saving_data_folder, "dev.tsv"), index=False, columns=dev_df.columns, sep='\t', header=False) config = AutoConfig.from_pretrained(self.model_name, num_labels=num_labels, output_attentions=True) ##needed for the visualizations # loading the actual model to memory model = BertForSequenceClassification.from_pretrained(self.model_name, config=config) # Now we need to convert the examples in the dataset to features that the model can understand # this is a ready made class, provided by HuggingFace train_dataset = SingleSentenceClassificationProcessor(mode='classification') dev_dataset = SingleSentenceClassificationProcessor(mode='classification') # now adding examples (from the DF we created earlier) to the objects we created in the cell above) _ = train_dataset.add_examples(texts_or_text_and_labels=train_df['text'], labels=train_df[self.label_col_name], overwrite_examples=True) _ = dev_dataset.add_examples(texts_or_text_and_labels=dev_df['text'], labels=dev_df[self.label_col_name], overwrite_examples=True) train_features = train_dataset.get_features(tokenizer=self.tokenizer, max_length=self.max_length) test_features = dev_dataset.get_features(tokenizer=self.tokenizer, max_length=self.max_length) training_args = TrainingArguments("./train") training_args.do_train = True # setting the params of the BERT classifier for cur_param in self.bert_model_params.keys(): try: training_args.__dict__[cur_param] = eval(self.bert_model_params[cur_param]) except TypeError: training_args.__dict__[cur_param] = self.bert_model_params[cur_param] 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.output_dir = self.saving_model_folder training_args.eval_steps = 100 # training_args.logging_dir = "gs://" from torch.utils.tensorboard import SummaryWriter supports google cloud storage trainer = Trainer(model=model, args=training_args, train_dataset=train_features, eval_dataset=test_features, compute_metrics=self.compute_metrics) trainer.train() # saving the model self.save_model(model=trainer.model, folder_name='bert_based_model')
def generate_training_args(args, inoculation_step): training_args = TrainingArguments("tmp_trainer") training_args.no_cuda = args.no_cuda training_args.seed = args.seed training_args.do_train = args.do_train training_args.do_eval = args.do_eval training_args.output_dir = os.path.join(args.output_dir, str(inoculation_step)+"-sample") training_args.evaluation_strategy = args.evaluation_strategy # evaluation is done after each epoch training_args.metric_for_best_model = args.metric_for_best_model training_args.greater_is_better = args.greater_is_better training_args.logging_dir = args.logging_dir training_args.task_name = args.task_name training_args.learning_rate = args.learning_rate training_args.per_device_train_batch_size = args.per_device_train_batch_size training_args.per_device_eval_batch_size = args.per_device_eval_batch_size training_args.num_train_epochs = args.num_train_epochs # this is the maximum num_train_epochs, we set this to be 100. training_args.eval_steps = args.eval_steps training_args.logging_steps = args.logging_steps training_args.load_best_model_at_end = args.load_best_model_at_end if args.save_total_limit != -1: # only set if it is specified training_args.save_total_limit = args.save_total_limit import datetime date_time = "{}-{}".format(datetime.datetime.now().month, datetime.datetime.now().day) run_name = "{0}_{1}_{2}_{3}_mlen_{4}_lr_{5}_seed_{6}_metrics_{7}".format( args.run_name, args.task_name, args.model_type, date_time, args.max_seq_length, args.learning_rate, args.seed, args.metric_for_best_model ) training_args.run_name = run_name training_args_dict = training_args.to_dict() # for PR _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] if args.model_path == "": args.model_path = args.model_type if args.model_type == "": assert False # you have to provide one of them. # Set seed before initializing model. set_seed(training_args.seed) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if is_main_process(training_args.local_rank) else logging.WARN, ) # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() logger.info(f"Training/evaluation parameters {training_args}") return training_args
def fit(self, train_df, dev_df): """ fitting the model based on the train set. validation is done using the dev set Parameters ---------- :param train_df: dataframe a pandas dataframe containing data to be trained on :param dev_df: dataframe a pandas dataframe containing data to validate on :return: None all relevant results are saved under the the location provided to save the model in. Next a prediction can be done """ train_labels = Counter(train_df[self.label_col_name]).keys() num_labels = len(train_labels) dev_labels = Counter(dev_df[self.label_col_name]).keys() if num_labels != len(dev_labels): raise IOError( "train and dev datasets contain different number of labels") # creating a DF for train/test with relevant columns. # Not clear why the 'alpha' column is needed, but as written here # (https://towardsdatascience.com/https-medium-com-chaturangarajapakshe-text-classification-with-transformer-models-d370944b50ca) - it is required train_df = pd.DataFrame({ 'id': range(len(train_df)), 'label': train_df[self.label_col_name], 'alpha': ['a'] * train_df.shape[0], 'text': train_df["text"].replace(r'\n', ' ', regex=True) }) dev_df = pd.DataFrame({ 'id': range(len(dev_df)), 'label': dev_df[self.label_col_name], 'alpha': ['a'] * dev_df.shape[0], 'text': dev_df["text"].replace(r'\n', ' ', regex=True) }) # saving the DF to the new/old folder train_df.to_csv(os.path.join(self.saving_data_folder, "train.tsv"), index=False, columns=train_df.columns, sep='\t', header=False) dev_df.to_csv(os.path.join(self.saving_data_folder, "dev.tsv"), index=False, columns=dev_df.columns, sep='\t', header=False) config = AutoConfig.from_pretrained( self.model_name, num_labels=num_labels, output_attentions=True) ##needed for the visualizations # loading the actual model to memory model = BertForSequenceClassification.from_pretrained(self.model_name, config=config) # Now we need to convert the examples in the dataset to features that the model can understand # this is a ready made class, provided by HuggingFace train_dataset = SingleSentenceClassificationProcessor( mode='classification') dev_dataset = SingleSentenceClassificationProcessor( mode='classification') # now adding examples (from the DF we created earlier) to the objects we created in the cell above) _ = train_dataset.add_examples( texts_or_text_and_labels=train_df['text'], labels=train_df[self.label_col_name], overwrite_examples=True) _ = dev_dataset.add_examples(texts_or_text_and_labels=dev_df['text'], labels=dev_df[self.label_col_name], overwrite_examples=True) train_features = train_dataset.get_features(tokenizer=self.tokenizer, max_length=self.max_length) dev_features = dev_dataset.get_features(tokenizer=self.tokenizer, max_length=self.max_length) # idea about a self-trainer is taken from here - https://huggingface.co/transformers/main_classes/trainer.html class MyTrainer(Trainer): def __init__(self, loss_func=torch.nn.CrossEntropyLoss(), **kwargs): self.loss_func = loss_func super().__init__(**kwargs) def compute_loss(self, model, inputs): labels = inputs.pop("labels") outputs = model(**inputs) logits = outputs[0] return self.loss_func(logits, labels) class FocalLoss(nn.modules.loss._WeightedLoss): def __init__(self, weight=None, gamma=2, reduction='mean'): super(FocalLoss, self).__init__(weight, reduction=reduction) self.gamma = gamma self.weight = weight # weight parameter will act as the alpha parameter to balance class weights def forward(self, input, target): ce_loss = F.cross_entropy(input, target, reduction=self.reduction, weight=self.weight) pt = torch.exp(-ce_loss) focal_loss = ((1 - pt)**self.gamma * ce_loss).mean() return focal_loss class_weights = compute_class_weight(class_weight='balanced', classes=np.unique( list(train_labels)), y=train_df['label']) #my_loss_func = torch.nn.CrossEntropyLoss(weight=torch.tensor(class_weights, dtype=torch.float)) my_loss_func = FocalLoss( weight=torch.tensor(class_weights, dtype=torch.float)) # how to define a trainer and all its arguments is taken from here - https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb args = TrainingArguments( "arabic_nlp_model", evaluation_strategy="epoch", #learning_rate=1e-5, learning_rate=1e-4, per_device_train_batch_size=16, per_device_eval_batch_size=8, num_train_epochs=5, weight_decay=0.01, load_best_model_at_end=True, #metric_for_best_model="macro_f1_PN", ) # setting the params of the BERT classifier for cur_param in self.bert_model_params.keys(): try: args.__dict__[cur_param] = eval( self.bert_model_params[cur_param]) except TypeError: args.__dict__[cur_param] = self.bert_model_params[cur_param] args.logging_steps = (len(train_features) - 1) // args.per_device_train_batch_size + 1 args.save_steps = args.logging_steps args.output_dir = self.saving_model_folder #training_args.compute_metrics = f1_score #training_args.compute_metrics = self.compute_metrics # training_args.logging_dir = "gs://" from torch.utils.tensorboard import SummaryWriter supports google cloud storage trainer = MyTrainer(loss_func=my_loss_func, model=model, args=args, train_dataset=train_features, eval_dataset=dev_features, compute_metrics=self.compute_metrics) #trainer = Trainer(model=model, # args=args, # train_dataset=train_features, # eval_dataset=dev_features, # #compute_metrics = compute_metrics) # compute_metrics=self.compute_metrics) trainer.train() # saving the model self.save_model(model=trainer.model)