Update README.md

README.md

@@ -44,7 +44,7 @@ The following table shows the hyper-paramters we used in our training process.
 | Batch Size            | 4M          |
 | Weight Decay          | 0.1         |
 
-**Second phase**: We further adjusted the training corpus ratio, incorporating more domain-specific datasets(Math、Coding), and continued training for 50B tokens.
+**Second phase**: We further adjusted the training corpus ratio, incorporating more domain-specific datasets(Math, Coding), and continued training for 50B tokens.
 
 | Hyper-parameters      |             |
 | --------------------- | ----------- |
@@ -59,8 +59,7 @@ The following table shows the hyper-paramters we used in our training process.
 Our evaluation is based on the framework lm-evaluation-harness and opencompass. The evaluation details are listed as follows:
 
 - Huggingface LLM Leaderboard tasks.
-- Commonsense: We report the average of PIQA, SIQA,  ARC easy and challenge and  CommonsenseQA.
-- Other Popular Benchmarks: We report the average accuracies on Big Bench Hard (BBH) (3-shot), HumanEval, MBPP, MATH.
+- Other Popular Benchmarks: We report the average accuracies on Big Bench Hard (BBH) (3-shot), HumanEval.
 
 |         | MMLU   | Winogrande | TruthfulQA | Hellaswag | GSM8K  | Arc-C  | HumanEval | BBH  | Average |
 | ------- | ------ | ---------- | ---------- | --------- | ------ | ------ | --------- | ---- | ------- |