fast-bert：是否有可能在最终预测之前获得标签概率

如何解决fast-bert：是否有可能在最终预测之前获得标签概率

我正在使用fastBert使用camemBert进行二进制文本分类。

是否可以在对二进制分类进行最终预测之前获得标签概率？为了绘制roc曲线，我需要概率预测。

下面是我的代码：

from fast_bert.data_cls import BertDataBunch
from fast_bert.learner_cls import BertLearner

databunch = BertDataBunch(DATA_PATH,LABEL_PATH,tokenizer='camembert-base',train_file='train.csv',val_file='val.csv',label_file='labels.csv',text_col='text',label_col='label',batch_size_per_gpu=8,max_seq_length=512,multi_gpu=multi_gpu,multi_label=False,model_type='camembert-base')

learner = BertLearner.from_pretrained_model(
                        databunch,pretrained_path='camembert-base',#'/content/drive/My Drive/model/model_out'
                        metrics=metrics,device=device_cuda,logger=logger,output_dir=OUTPUT_DIR,finetuned_wgts_path=None,#WGTS_PATH
                        warmup_steps=300,is_fp16=True,logging_steps=50)

learner.fit(epochs=10,lr=9e-5,validate=True,schedule_type="warmup_cosine",optimizer_type="adamw")

from fast_bert.prediction import BertClassificationPredictor

predictor = BertClassificationPredictor(
                model_path='/content/drive/My Drive/fituned_model/model_out',label_path=LABEL_PATH,model_type='camembert-base',do_lower_case=False)

谢谢