Update README.md

README.md

@@ -104,7 +104,7 @@ We are currently unable to produce accurate benchmark templates for non-QA tasks
 |----------------------|------:|--------|-----:|---|-----:|
 |jcommonsenseqa-1.1-0.6|    1.1|acc     |0.8213|±  |0.0115|
 
-*jcommonsenseqa benchmark result is very, very close to [Japanese Stable LM Gamma 7B (83.47)](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable), current SOTA Japanese LM. However, our model was not trained on a particularly large amount of text in Japanese. This seems to reflect the cross-language transferability of metalinguistics.*
+*JCommonsenseQA benchmark result is very, very close to [Japanese Stable LM Gamma 7B (83.47)](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable), current SOTA Japanese LM. However, our model was not trained on a particularly large amount of text in Japanese. This seems to reflect the cross-language transferability of metalinguistics.*
 
 # 中文说明
 
@@ -175,4 +175,4 @@ STEM准确率：66.71
 |----------------------|------:|--------|-----:|---|-----:|
 |jcommonsenseqa-1.1-0.6|    1.1|acc     |0.8213|±  |0.0115|
 
-*jcommonsenseqa 基准测试结果非常非常接近 [Japanese Stable LM Gamma 7B (83.47)](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable)，当前 SOTA 日文 LM 。然而，我们的模型并未在日文上进行特别的大量文本训练。这似乎能体现元语言的跨语言迁移能力。*
+*JCommonsenseQA 基准测试结果非常非常接近 [Japanese Stable LM Gamma 7B (83.47)](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable)，当前 SOTA 日文 LM 。然而，我们的模型并未在日文上进行特别的大量文本训练。这似乎能体现元语言的跨语言迁移能力。*