问题描述
如何为ktrain库中的文本分类器使用其他预训练模型?使用时:
model = text.text_classifier('bert',(x_train,y_train), preproc = preproc)
This uses the multilangual pretrained model
但是,我也想尝试一种单语模型。即荷兰语:“ wietsedv / bert-base-dutch-cased”,也用于其他k列实现for example。
但是,当尝试在文本分类器中使用此命令时,它不起作用:
model = text.text_classifier('bert',(x_train,y_train),> preproc=preproc,bert_model='wietsedv/bert-base-dutch-cased')
或
model = text.text_classifier('wietsedv/bert-base-dutch-cased',preproc=preproc)
有人可以这样做吗?谢谢!
解决方法
ktrain 中有两个文本分类API。第一个是text_classifier
API,可用于选择数量的变压器模型和非变压器模型。第二个是Transformer
API,可与任何transformers
模型一起使用,包括您列出的模型。
后者在this tutorial notebook和this medium article中有详细说明。
例如,您可以在以下示例中将MODEL_NAME
替换为所需的任何模型:
示例:
# load text data
categories = ['alt.atheism','soc.religion.christian','comp.graphics','sci.med']
from sklearn.datasets import fetch_20newsgroups
train_b = fetch_20newsgroups(subset='train',categories=categories,shuffle=True)
test_b = fetch_20newsgroups(subset='test',shuffle=True)
(x_train,y_train) = (train_b.data,train_b.target)
(x_test,y_test) = (test_b.data,test_b.target)
# build,train,and validate model (Transformer is wrapper around transformers library)
import ktrain
from ktrain import text
MODEL_NAME = 'distilbert-base-uncased' # replace this with model of choice
t = text.Transformer(MODEL_NAME,maxlen=500,class_names=train_b.target_names)
trn = t.preprocess_train(x_train,y_train)
val = t.preprocess_test(x_test,y_test)
model = t.get_classifier()
learner = ktrain.get_learner(model,train_data=trn,val_data=val,batch_size=6)
learner.fit_onecycle(5e-5,4)
learner.validate(class_names=t.get_classes()) # class_names must be string values
# Output from learner.validate()
# precision recall f1-score support
#
# alt.atheism 0.92 0.93 0.93 319
# comp.graphics 0.97 0.97 0.97 389
# sci.med 0.97 0.95 0.96 396
#soc.religion.christian 0.96 0.96 0.96 398
#
# accuracy 0.96 1502
# macro avg 0.95 0.96 0.95 1502
# weighted avg 0.96 0.96 0.96 1502