Update README.md

README.md

@@ -63,7 +63,33 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-
+### Performance Metrics
+AI2SQL's performance was rigorously evaluated post-training. The key metrics used to assess the model were:
+
+- **Accuracy**: The percentage of queries where the model-generated SQL matched the expected SQL.
+- **Precision**: The proportion of correctly generated SQL queries out of all queries generated by the model.
+- **Recall**: The ability of the model to generate all relevant SQL queries corresponding to the input natural language questions.
+- **F1-Score**: The harmonic mean of precision and recall, providing a balance between the two.
+
+**Results:**
+- Accuracy: TBD
+- Precision: TBD
+- Recall: TBD
+- F1-Score: TBD
+
+### Insights and Observations
+- **Handling Complex Queries**: AI2SQL demonstrated a high proficiency in handling complex queries involving multiple SQL clauses and parameters.
+- **Contextual Understanding**: The model showed a notable capability in understanding context and generating SQL queries that accurately reflect nuanced natural language instructions.
+- **Performance on Diverse Data**: AI2SQL maintained consistent performance across various domains present in the training dataset, indicating its robustness and general applicability.
+
+### Limitations Observed
+- **Handling Ambiguous Questions**: The model sometimes struggled with ambiguous natural language inputs where the intent was not clear.
+- **Query Specificity**: In cases of highly specific queries, the model occasionally generated SQL that was syntactically correct but did not completely align with the nuanced requirements of the question.
+
+### Future Improvements
+Based on the training results and observed limitations, future improvements could include:
+- Enhanced training on ambiguous natural language inputs to improve the model's interpretative capabilities.
+- Further fine-tuning with a broader range of specific and complex SQL queries to enhance the model's accuracy in generating nuanced SQL statements.
 
 ### Framework versions