Update README.md

README.md

@@ -16,9 +16,9 @@ tags:
 
 ## 简介 Brief Introduction
 
-将自然语言理解任务转化为multiple choice任务，并且使用14个机器阅读理解数据进行预训练
+UniMC 核心思想是将自然语言理解任务转化为 multiple choice 任务，并且使用多个 NLU 任务来进行预训练。我们在英文数据集实验结果表明仅含有 2.35 亿参数的 [ALBERT模型](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English)的zero-shot性能可以超越众多千亿的模型。并在中文测评基准 FewCLUE 和 ZeroCLUE 两个榜单中，13亿的[二郎神](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese)获得了第一的成绩。
 
-Convert natural language understanding tasks into multiple choice tasks, and use 14 machine reading comprehension data for pre-training
+The core idea of UniMC is to convert natural language understanding tasks into multiple choice tasks and use multiple NLU tasks for pre-training. Our experimental results on the English dataset show that the zero-shot performance of a [ALBERT](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English) model with only 235 million parameters can surpass that of many hundreds of billions of models. And in the Chinese evaluation benchmarks FewCLUE and ZeroCLUE two lists, 1.3 billion [Erlangshen](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) won the first result.
 
 ## 模型分类 Model Taxonomy
 
@@ -49,10 +49,10 @@ avoiding problems in commonly used large generative models such as FLAN. It not
 
 ```python3
 import argparse
-from fengshen.pipelines.multiplechoice import UniMCPiplines
+from fengshen.pipelines.multiplechoice import UniMCPipelines
 
 total_parser = argparse.ArgumentParser("TASK NAME")
-total_parser = UniMCPiplines.piplines_args(total_parser)
+total_parser = UniMCPipelines.piplines_args(total_parser)
 args = total_parser.parse_args()
     
 pretrained_model_path = 'IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English'
@@ -62,7 +62,7 @@ args.max_length=512
 args.max_epochs=3
 args.batchsize=8
 args.default_root_dir='./'
-model = UniMCPiplines(args, model_path=pretrained_model_path)
+model = UniMCPipelines(args, model_path=pretrained_model_path)
 
 train_data = [] 
 dev_data = []