Update README.md

README.md

@@ -32,9 +32,9 @@ The first open source Chinese CLIP, pre-training on 123M image-text pairs, the t
 
 ## 模型信息 Model Information
 
-我们遵循CLIP的实验设置，以获得强大的视觉-语言表征。在训练中文版的CLIP时，我们使用[chinese-roberta-wwm](https://huggingface.co/hfl/chinese-roberta-wwm-ext)作为语言的编码器，并将[CLIP](https://github.com/openai/CLIP)中的ViT-B-32应用于视觉的编码器。为了快速且稳定地进行预训练，我们冻结了视觉编码器并且只微调语言编码器。此外，我们将[Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/)数据集(100M)和[Zero](https://zero.so.com/)数据集(23M)用作预训练的数据集。据我们所知，我们的Taiyi-CLIP是目前Huggingface社区中首个的开源中文CLIP。
+我们遵循CLIP的实验设置，以获得强大的视觉-语言表征。在训练中文版的CLIP时，我们使用[chinese-roberta-wwm](https://huggingface.co/hfl/chinese-roberta-wwm-ext)作为语言的编码器，并将[CLIP](https://github.com/openai/CLIP)中的ViT-B-32应用于视觉的编码器。为了快速且稳定地进行预训练，我们冻结了视觉编码器并且只微调语言编码器。此外，我们将[Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/)数据集(100M)和[Zero](https://zero.so.com/)数据集(23M)用作预训练的数据集，训练了24个epoch，在在A100x32上训练了7天。据我们所知，我们的Taiyi-CLIP是目前Huggingface社区中首个的开源中文CLIP。
 
-We follow the experimental setup of CLIP to obtain powerful visual-language intelligence. To obtain the CLIP for Chinese, we employ [chinese-roberta-wwm](https://huggingface.co/hfl/chinese-roberta-wwm-ext) for the language encoder, and apply the ViT-B-32 in [CLIP](https://github.com/openai/CLIP) for the vision encoder. We freeze the vision encoder and tune the language encoder to speed up and stabilize the pre-training process. Moreover, we apply [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/) dataset (100M) and [Zero](https://zero.so.com/) dataset (23M) as the pre-training datasets. To the best of our knowledge, our TaiyiCLIP is currently the only open-sourced Chinese CLIP in the huggingface community.
+We follow the experimental setup of CLIP to obtain powerful visual-language intelligence. To obtain the CLIP for Chinese, we employ [chinese-roberta-wwm](https://huggingface.co/hfl/chinese-roberta-wwm-ext) for the language encoder, and apply the ViT-B-32 in [CLIP](https://github.com/openai/CLIP) for the vision encoder. We freeze the vision encoder and tune the language encoder to speed up and stabilize the pre-training process. Moreover, we apply [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/) dataset (100M) and [Zero](https://zero.so.com/) dataset (23M) as the pre-training datasets. We train 24 epochs, which takes 7 days to train on A100x16. To the best of our knowledge, our TaiyiCLIP is currently the only open-sourced Chinese CLIP in the huggingface community.
 
 ### 下游效果 Performance