Update README.md

README.md

@@ -14,7 +14,7 @@ widget:
 
 ## Model description
 
-This is a Japanese BigBird base model pretrained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of oscar.
+This is a Japanese BigBird base model pretrained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR.
 
 ## How to use
 
@@ -41,7 +41,7 @@ The vocabulary consists of 32000 tokens including words ([JumanDIC](https://gith
 
 ## Training procedure
 
-This model was trained on Japanese Wikipedia (as of 20221101), the Japanese portion of CC-100, and the and the Japanese portion of oscar. It took two weeks using 16 NVIDIA A100 GPUs using [transformers](https://github.com/huggingface/transformers) and [DeepSpeed](https://github.com/microsoft/DeepSpeed).
+This model was trained on Japanese Wikipedia (as of 20221101), the Japanese portion of CC-100, and the and the Japanese portion of OSCAR. It took two weeks using 16 NVIDIA A100 GPUs using [transformers](https://github.com/huggingface/transformers) and [DeepSpeed](https://github.com/microsoft/DeepSpeed).
 
 The following hyperparameters were used during pretraining:
 - learning_rate: 1e-4
@@ -59,7 +59,7 @@ The following hyperparameters were used during pretraining:
 We fine-tuned the following models and evaluated them on the dev set of JGLUE.
 We tuned learning rate and training epochs for each model and task following [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja).
 
-For tasks other than MARC-ja, the maximum length is short, so the attention_type was set to "original_full" and fine-tuning was performed for tasks other than MARC-ja. For MARC-ja, both "block_sparse" and "original_full" were used.
+For the tasks other than MARC-ja, the maximum length is short, so the attention_type was set to "original_full", and fine-tuning was performed. For MARC-ja, both "block_sparse" and "original_full" were used.
 
 | Model                         | MARC-ja/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc |
 |-------------------------------|--------------|---------------|----------|-----------|-----------|------------|------------|