update readme

README.md

@@ -44,7 +44,6 @@ TencentPretrain has the following features:
 * argparse
 * packaging
 * regex
-* For the mixed precision training you will need apex from NVIDIA
 * For the pre-trained model conversion (related with TensorFlow) you will need TensorFlow
 * For the tokenization with sentencepiece model you will need [SentencePiece](https://github.com/google/sentencepiece)
 * For developing a stacking model you will need LightGBM and [BayesianOptimization](https://github.com/fmfn/BayesianOptimization)
@@ -135,7 +134,7 @@ The above content provides basic ways of using TencentPretrain to pre-process, p
 <br/>
 
 ## Pre-training data
-This section provides links to a range of :arrow_right: [__pre-training data__](https://github.com/Tencent/TencentPretrain/wiki/Pretraining-data) :arrow_left: .
+This section provides links to a range of :arrow_right: [__pre-training data__](https://github.com/Tencent/TencentPretrain/wiki/Pretraining-data) :arrow_left: . TencentPretrain can load these pre-training data directly.
 
 <br/>
 
@@ -145,7 +144,7 @@ This section provides links to a range of :arrow_right: [__downstream datasets__
 <br/>
 
 ## Modelzoo
-With the help of TencentPretrain, we pre-trained models of different properties (e.g. models based on different modalities, encoders, and targets). Detailed introduction of pre-trained models and their download links can be found in :arrow_right: [__modelzoo__](https://github.com/Tencent/TencentPretrain/wiki/Modelzoo) :arrow_left: . All pre-trained models can be loaded by TencentPretrain directly. More pre-trained models will be released in the future.
+With the help of TencentPretrain, we pre-trained models of different properties (e.g. models based on different modalities, encoders, and targets). Detailed introduction of pre-trained models and their download links can be found in :arrow_right: [__modelzoo__](https://github.com/Tencent/TencentPretrain/wiki/Modelzoo) :arrow_left: . All pre-trained models can be loaded by TencentPretrain directly.
 
 <br/>
 
@@ -183,7 +182,7 @@ TencentPretrain/
 
 ```
 
-The code is well-organized. Users can use and extend upon it with little efforts.
+The code is organized based on components (e.g. embeddings, encoders). Users can use and extend upon it with little efforts.
 
 Comprehensive examples of using TencentPretrain can be found in :arrow_right: [__instructions__](https://github.com/Tencent/TencentPretrain/wiki/Instructions) :arrow_left: , which help users quickly implement pre-training models such as BERT, GPT-2, ELMo, T5, CLIP and fine-tune pre-trained models on a range of downstream tasks.
 

README_ZH.md

@@ -41,7 +41,6 @@ TencentPretrain有如下几方面优势:
 * argparse
 * packaging
 * regex
-* 如果使用混合精度，需要安装英伟达的apex
 * 如果涉及到TensorFlow模型的转换，需要安装TensorFlow
 * 如果在tokenizer中使用sentencepiece模型，需要安装[SentencePiece](https://github.com/google/sentencepiece)
 * 如果使用模型集成stacking，需要安装LightGBM和[BayesianOptimization](https://github.com/fmfn/BayesianOptimization)
@@ -132,7 +131,7 @@ python3 inference/run_classifier_infer.py --load_model_path models/finetuned_mod
 <br>
 
 ## 预训练数据
-我们提供了链接，指向一系列开源的 :arrow_right: [__预训练数据__](https://github.com/Tencent/TencentPretrain/wiki/预训练数据) :arrow_left: 。
+我们提供了链接，指向一系列开源的 :arrow_right: [__预训练数据__](https://github.com/Tencent/TencentPretrain/wiki/预训练数据) :arrow_left: 。TencentPretrain可以直接加载这些预训练数据。
 
 <br>
 
@@ -142,7 +141,7 @@ python3 inference/run_classifier_infer.py --load_model_path models/finetuned_mod
 <br>
 
 ## 预训练模型仓库
-借助TencentPretrain，我们训练不同性质的预训练模型（例如基于不同模态、编码器、目标任务）。用户可以在 :arrow_right: [__预训练模型仓库__](https://github.com/Tencent/TencentPretrain/wiki/预训练模型仓库) :arrow_left: 中找到各种性质的预训练模型以及它们对应的描述和下载链接。所有预训练模型都可以由TencentPretrain直接加载。将来我们会发布更多的预训练模型。
+借助TencentPretrain，我们训练不同性质的预训练模型（例如基于不同模态、编码器、目标任务）。用户可以在 :arrow_right: [__预训练模型仓库__](https://github.com/Tencent/TencentPretrain/wiki/预训练模型仓库) :arrow_left: 中找到各种性质的预训练模型以及它们对应的描述和下载链接。所有预训练模型都可以由TencentPretrain直接加载。
 
 <br>