Update app.py

app.py

@@ -172,7 +172,7 @@ def build_leaderboard_tab(leaderboard_table_file, text_recog_file, Inaccessible_
             # arena table
             with gr.Tab("OCRBench", id=0):
                 arena_table_vals = get_arena_table(model_table_df)
-                md = "OCRBench is a comprehensive evaluation benchmark designed to assess the OCR capabilities of Large Multimodal Models. It comprises five components: Text Recognition, SceneText-Centric VQA, Document-Oriented VQA, Key Information Extraction, and Handwritten Mathematical Expression Recognition. The benchmark includes 1000 question-answer pairs, and all the answers undergo manual verification and correction to ensure a more precise evaluation."
+                md = "OCRBench is a comprehensive evaluation benchmark designed to assess the OCR capabilities of Large Multimodal Models. It consists of five components: Text Recognition, SceneText-Centric VQA, Document-Oriented VQA, Key Information Extraction, and Handwritten Mathematical Expression Recognition. The benchmark includes 1000 question-answer pairs, with all answers containing no fewer than 4 symbols to reduce false positives. All answers undergo manual verification and correction to ensure a more precise evaluation."
                 gr.Markdown(md, elem_id="leaderboard_markdown")
                 gr.Dataframe(
                     headers=[
@@ -207,7 +207,7 @@ def build_leaderboard_tab(leaderboard_table_file, text_recog_file, Inaccessible_
                 )
             with gr.Tab("Text Recognition", id=1):
                 arena_table_vals = get_recog_table(recog_table_df)
-                md = "OCRBench is a comprehensive evaluation benchmark designed to assess the OCR capabilities of Large Multimodal Models. It comprises five components: Text Recognition, SceneText-Centric VQA, Document-Oriented VQA, Key Information Extraction, and Handwritten Mathematical Expression Recognition. The benchmark includes 1000 question-answer pairs, and all the answers undergo manual verification and correction to ensure a more precise evaluation."
+                md = "OCRBench is a comprehensive evaluation benchmark designed to assess the OCR capabilities of Large Multimodal Models. It consists of five components: Text Recognition, SceneText-Centric VQA, Document-Oriented VQA, Key Information Extraction, and Handwritten Mathematical Expression Recognition. The benchmark includes 1000 question-answer pairs, with all answers containing no fewer than 4 symbols to reduce false positives. All answers undergo manual verification and correction to ensure a more precise evaluation."
                 gr.Markdown(md, elem_id="leaderboard_markdown")
                 gr.Dataframe(
                     headers=[
@@ -281,7 +281,7 @@ def build_leaderboard_tab(leaderboard_table_file, text_recog_file, Inaccessible_
         pass
     md_tail = f"""
     # Notice
-    To reduce false positives, we filter out questions that have answers containing fewer than 4 symbols from all datasets. Sometimes, API calls to closed-source models may not succeed. In such cases, we will repeat the calls for unsuccessful samples until it becomes impossible to obtain a successful response. It is important to note that due to rigorous security reviews by OpenAI, GPT4V refuses to provide results for the 84 samples in OCRBench.
+    Sometimes, API calls to closed-source models may not succeed. In such cases, we will repeat the calls for unsuccessful samples until it becomes impossible to obtain a successful response. It is important to note that due to rigorous security reviews by OpenAI, GPT4V refuses to provide results for the 84 samples in OCRBench.
     If you would like to include your model in the OCRBench leaderboard, please follow the evaluation instructions provided on [GitHub](https://github.com/Yuliang-Liu/MultimodalOCR), [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) or [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) and feel free to contact us via email at [email protected]. We will update the leaderboard in time."""
     gr.Markdown(md_tail, elem_id="leaderboard_markdown")