Model

class Manteia.Model.EarlyStopping(patience=2, delta=0, path=None, verbose=True)

Early stops the training if validation loss doesn’t improve after a given patience.

save_checkpoint(acc_validation, model, device_model)

Saves model when validation loss decrease.

class Manteia.Model.Model(model_name='bert', model_type=None, task='classification', num_labels=0, epochs=None, MAX_SEQ_LEN=128, early_stopping=False, path='./model/', verbose=True)

This is the class to construct model.

Args:

model_name (string, optional, defaults to ‘bert’):

give the name of a model.

num_labels (int, optional, defaults to ‘0’):

give the number of categorie for classification.

Example:

from Manteia.Preprocess import Preprocess
from Manteia.Model import Model,encode_text,encode_label,Create_DataLoader_train
from sklearn.model_selection import train_test_split

documents=['a text','text b']
labels=['a','b']
pp               = Preprocess(documents=documents,labels=labels)
model       = Model(model_name=model_name,num_labels=len(pp.list_labels))
model.load()

train_text, validation_text, train_labels, validation_labels = train_test_split(pp.documents, pp.labels, random_state=2018, test_size=0.1)

train_ids,train_masks           = encode_text(train_text,model.tokenizer,MAX_SEQ_LEN)
validation_ids,validation_masks = encode_text(validation_text,model.tokenizer,MAX_SEQ_LEN)
train_labels                    = encode_label(train_labels,pp.list_labels)
validation_labels               = encode_label(validation_labels,pp.list_labels)

dt_train          = Create_DataLoader_train(train_ids,train_masks,train_labels)
dt_validation     = Create_DataLoader_train(validation_ids,validation_masks,validation_labels)

model.configuration(dt_train)
model.fit(dt_train,dt_validation)

Attributes:

predict(predict_dataloader, p_type='class', mode='eval')

if self.early_stopping:

#by torch #pour charger uniquement la classe du modèle! print(‘test’) self.load_type() print(‘test’) self.load_class() print(‘test’) self.model.load_state_dict(torch.load(os.path.join(self.path,’state_dict_validation.pt’))) print(‘test’)

#by transformer #self.model.from_pretrained(self.path) if self.verbose==True:

print(‘loading model early…’)

Manteia.Model.format_time(elapsed)

Takes a time in seconds and returns a string hh:mm:ss