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-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)作为语言的编码器，并将[CLIP](https://github.com/openai/CLIP)中的**ViT-L-14**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。我们先在悟空数据集上预训练了10轮，然后接着在悟空数据集和zero数据集上预训练12轮。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-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)作为语言的编码器，并将[CLIP](https://github.com/openai/CLIP)中的**ViT-L-14**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。我们先在悟空数据集上预训练了10轮，然后接着在悟空数据集和zero数据集上预训练12轮， 在A100x16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-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) for the language encoder, and apply the **ViT-L-14** 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. The model was first trained 10 epochs on wukong and then train another 12 epochs on wukong and zero. 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-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) for the language encoder, and apply the **ViT-L-14** 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. The model was first trained 10 epochs on wukong and then train another 12 epochs on wukong and zero, which takes about 14 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