Update README.md

README.md

@@ -38,19 +38,16 @@ The model's deep understanding of SEC filings and related financial data makes i
 
 To ensure the robustness and effectiveness of Llama-3-SEC, the model has undergone rigorous evaluation on both domain-specific and general benchmarks. Key evaluation metrics include:
 
-- Domain-specific perplexity, measuring the model's performance on SEC-related data
+<table>
+  <tr>
+    <td><img src="https://i.ibb.co/xGHRfLf/Screenshot-2024-06-11-at-10-23-59-PM.png" alt="Domain Specific Perplexity of Model Variants" width="300"></td>
+    <td><img src="https://i.ibb.co/2v6PdDx/Screenshot-2024-06-11-at-10-25-03-PM.png" alt="Domain Specific Evaluations of Model Variants" width="300"></td>
+  </tr>
+  <tr>
+    <td colspan="2" style="text-align:center;"><img src="https://i.ibb.co/K5d0wMh/Screenshot-2024-06-11-at-10-23-18-PM.png" alt="General Evaluations of Model Variants" width="600"></td>
+  </tr>
+</table>
 
-![Domain Specific Perplexity of Model Variants](https://i.ibb.co/xGHRfLf/Screenshot-2024-06-11-at-10-23-59-PM.png)
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-- Extractive numerical reasoning tasks, using subsets of TAT-QA and ConvFinQA datasets
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-![Domain Specific Evaluations of Model Variants](https://i.ibb.co/2v6PdDx/Screenshot-2024-06-11-at-10-25-03-PM.png)
-
-- General evaluation metrics, such as BIG-bench, AGIEval, GPT4all, and TruthfulQA, to assess the model's performance on a wide range of tasks
-
-![General Evaluations of Model Variants](https://i.ibb.co/K5d0wMh/Screenshot-2024-06-11-at-10-23-18-PM.png)
-
-- General perplexity on various datasets, including bigcode/starcoderdata, open-web-math/open-web-math, allenai/peS2o, mattymchen/refinedweb-3m, and Wikitext
 
 The evaluation results demonstrate significant improvements in domain-specific performance while maintaining strong general capabilities, thanks to the use of advanced CPT and model merging techniques.