Update README.md

README.md

@@ -35,7 +35,7 @@ For a complete overview of our dataset, methodology, and benchmarks, please refe
 | **Multi-turn Capability** | | | | | |
 | MT-Bench-EN | 8.4 | 8.4 | 8.3 | 7.8 | **8.5** |
 | MT-Bench-TH | **8.1** | 8.0 | **8.1** | 6.9 | 8.0 |
-| **Long-context** | | | | | |
+| **Long-context Understanding** | | | | | |
 | MRCR | **18.9** | 18.3 | 16.9 | 16.9 | 16.2 |
 | LongBench-v2 | **33.6** | 32.4 | 29.2 | **33.6** | 28.8 |
 | **Tool Calling** | | | | | |
@@ -133,7 +133,7 @@ print(responses)
 
 ## Processing Long Texts
 
-OpenJAI-v1.0 was trained for robust performance on context lengths up to **120,000 tokens**. The model operates optimally within its native 32,768-token window. To process contexts exceeding this limit, applying a context extension technique like YaRN or Dynamic RoPE scaling is necessary.
+OpenJAI-v1.0 was trained for robust performance on input context lengths up to **120,000 tokens**. The model operates optimally within its native 32,768-token window. To process contexts exceeding this limit, applying a context extension technique like YaRN or Dynamic RoPE scaling is necessary.
 
 Frameworks like `vLLM` and `SGLang` support passing command-line arguments to enable RoPE scaling.