Update README.md

README.md

@@ -90,6 +90,12 @@ you built your model off of, and at least two other comparison models of similar
 In a text paragraph, as you did in your second project check in, describe the benchmark evaluation tasks you chose and why you chose them. Next, briefly state why you
 chose each comparison model. Last, include a summary sentence(s) describing the performance of your model relative to the comparison models you chose. 
 
+| Model                                                     | HumanEval | SQuADv2 | E2E NLG Challenge | Testing Split of Training Dataset                                                          |
+|-----------------------------------------------------------|-----------|---------|-------------------|--------------------------------------------------------------------------------------------|
+| Base Model: Qwen/Qwen2.5-7B-Instruct                      | 0.652     | 9.81    | 6.68              | Bert Score Mean Precision: 0.829, Bert Score Mean Recall: 0.852, Bert Score Mean F1: 0.841 |
+| My Model: Qwen/Qwen2.5-7B-Instruct Trained and Finetuned  | 0.598     | 21.57   | 5.04              | Bert Score Mean Precision: 0.813, Bert Score Mean Recall: 0.848, Bert Score Mean F1: 0.830 |
+| Similar Size Model: meta-llama/Meta-Llama-3-8B-Instruct   | 0.280     | 20.33   | 2.26              | Bert Score Mean Precision: 0.814, Bert Score Mean Recall: 0.848, Bert Score Mean F1: 0.830 |
+| Similar Size Model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B | 0.634     | 5.81    | 3.63              | Bert Score Mean Precision: 0.803, Bert Score Mean Recall: 0.831, Bert Score Mean F1: 0.817 |
 
 # Usage and Intended Use