name update

README.md

@@ -32,9 +32,9 @@ import torch
 from transformers import BertForSequenceClassification, BertConfig, BertTokenizer
 import numpy as np
 
-# 加载TaiYi 中文 text encoder
-text_tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/TaiYi-CLIP-Roberta-102M-Chinese")
-text_encoder = BertForSequenceClassification.from_pretrained("IDEA-CCNL/TaiYi-CLIP-Roberta-102M-Chinese").eval()
+# 加载Taiyi 中文 text encoder
+text_tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Taiyi-CLIP-Roberta-102M-Chinese")
+text_encoder = BertForSequenceClassification.from_pretrained("IDEA-CCNL/Taiyi-CLIP-Roberta-102M-Chinese").eval()
 text = text_tokenizer(["一只猫", "一只狗",'两只猫', '两只老虎','一只老虎'], return_tensors='pt', padding=True)['input_ids']
 
 # 加载CLIP的image encoder
@@ -61,14 +61,14 @@ with torch.no_grad():
 ### Zero-Shot Classification
 |  model   | dataset  | Top1 | Top5 |
 |  ----  | ----  | ---- | ---- |
-| TaiYi-CLIP-ViT-B-32-Roberta-Chinese  | ImageNet1k-CN | 41.00% | 69.19% |
+| Taiyi-CLIP-ViT-B-32-Roberta-Chinese  | ImageNet1k-CN | 41.00% | 69.19% |
 
 ### Zero-Shot Text-to-Image Retrieval
 
 |  model   | dataset  | Top1 | Top5 | Top10 |
 |  ----  | ----  | ---- | ---- | ---- |
-| TaiYi-CLIP-ViT-B-32-Roberta-Chinese  | COCO-CN | 25.47 % | 51.70%  | 63.07% |
-| TaiYi-CLIP-ViT-B-32-Roberta-Chinese  | wukong50k | 48.67 % | 81.77% | 90.09% |
+| Taiyi-CLIP-ViT-B-32-Roberta-Chinese  | COCO-CN | 25.47 % | 51.70%  | 63.07% |
+| Taiyi-CLIP-ViT-B-32-Roberta-Chinese  | wukong50k | 48.67 % | 81.77% | 90.09% |
 
 
 # Citation