Update README.md

README.md

@@ -62,14 +62,14 @@ Also we provide Colab notebooks for you to better perceive the procedures. Click
 * 2022.4.28: Add support of inference on **huggingface transformers**. For how to use it, please refer to the doc [transformers.md](transformers.md) and our [huggingface models](https://huggingface.co/OFA-Sys). 
 * 2022.4.16: Released lightweight pretrained models **OFA-Medium** (~93M params) and **OFA-Tiny** (~33M params) in [checkpoints.md](checkpoints.md). To use them, you just need to load the corresponding checkpoint and set `--arch=ofa_medium` or `--arch=ofa_tiny` in the scripts.
 * 2022.3.23: Added [Encouraging Loss](https://arxiv.org/pdf/2110.06537.pdf) as a feature. See [README_EncouragingLoss.md](README_EncouragingLoss.md). Leveraging this feature, OFA-Large has achieved improved results in both VQA (**test-std acc: 80.67**) and Image Classification (**test acc: 85.6**) recently.
-* 2022.3.21: Released codes for pretraining OFA.
-* 2022.3.18: Released the finetuned **OFA-Base** (~180M parameters) checkpoints and running scripts for vision & language tasks, including: **Caption (146.4 CIDEr), VQA (78.07 on test-std), SNLI-VE (89.3 on dev), RefCOCO (90.67 on testA), RefCOCO+ (87.15 on testA) and RefCOCOg (82.31 on test-u)** .
-* 2022.3.11: Released the finetuning & inference code/checkpoints for **Gigaword**.
-* 2022.3.08: Released the pretrained checkpoint of **OFA-Base** in [checkpoints.md](checkpoints.md). To use OFA-Base, you just need to load `ofa_base.pt` and change `--arch=ofa_large` to `--arch=ofa_base` in the training scripts.
 <details>
     <summary><b>More News</b></summary>
     <p>
         <ul>
+            <li>2022.3.21: Released codes for pretraining OFA.</li>
+            <li>2022.3.18: Released the finetuned <b>OFA-Base</b> (~180M parameters) checkpoints and running scripts for vision & language tasks, including: <b>Caption (146.4 CIDEr), VQA (78.07 on test-std), SNLI-VE (89.3 on dev), RefCOCO (90.67 on testA), RefCOCO+ (87.15 on testA) and RefCOCOg (82.31 on test-u)</b>.</li>
+            <li>2022.3.11: Released the finetuning & inference code/checkpoints for <b>Gigaword</b>.</li>
+            <li>2022.3.08: Released the pretrained checkpoint of <b>OFA-Base</b> in <a href="https://github.com/OFA-Sys/OFA/blob/main/checkpoints.md">checkpoints.md</a>. To use OFA-Base, you just need to load <code>ofa_base.pt</code> and change <code>--arch=ofa_large</code> to <code>--arch=ofa_base</code> in the training scripts.</li>
             <li>2022.3.07: Released the finetuning & inference code/checkpoints for <b>Image Classification</b>, which achieves <b>85.0</b> accuracy on ImageNet-1K, slightly better than reported in OFA paper.</li>
             <li>2022.3.04: Released the finetuning & inference code/checkpoints for <b>Text-to-Image Generation</b>.</li>
             <li>2022.3.03: Released the finetuning & inference code/checkpoints for <b>SNLI-VE</b> and <b>GLUE</b>.</li>