release v0.2.0 models

README.md

@@ -62,7 +62,7 @@ print("Label probs:", text_probs)  # prints: [[1.0, 0.0, 0.0]]
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 # Model architecture
-The model was trained  a ViT-B/16 Transformer architecture as an image encoder and uses a 12-layer RoBERTa as a text encoder. It was initialized with [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) as the image encoder and the Japanese pre-trained RoBERTa model [rinna/japanese-roberta-base](https://huggingface.co/rinna/japanese-roberta-base) with the same sentencepiece tokenizer as the text encoder.
+The model was trained  a ViT-B/16 Transformer architecture as an image encoder and uses a 12-layer BERT as a text encoder. The image encoder was initialized from the [AugReg `vit-base-patch16-224` model](https://github.com/google-research/vision_transformer).
 
 # Training
 The model was trained on [CC12M](https://github.com/google-research-datasets/conceptual-12m) translated the captions to Japanese.