Update README.md

README.md

@@ -45,22 +45,26 @@ The DocuMint model can be used directly to generate high-quality docstrings for
 
 The training data consists of 100,000 Python functions and their docstrings extracted from popular open-source repositories in the FLOSS ecosystem. Repositories were filtered based on metrics such as number of contributors (> 50), commits (> 5k), stars (> 35k), and forks (> 10k) to focus on well-established and actively maintained projects. 
 
-An abstract syntax tree (AST) based parser was used to extract functions and docstrings. Challenges in the data sampling process included syntactic errors, multi-language repositories, computational expense, repository size discrepancies, and ensuring diversity while avoiding repetition.
-
 #### Training Hyperparameters
 
-The model was fine-tuned using Low-Rank Adaptation (LoRA) for 4 epochs with a batch size of 8 and gradient accumulation steps of 16. The initial learning rate was 2e-4. In total, there were 78,446,592 LoRA parameters and 185,040,896 training tokens. The full hyperparameter configuration is provided in Table 2 of the paper.
+| Hyperparameter                | Value         |
+|-------------------------------|---------------|
+| Fine-tuning Method            | LoRA          |
+| Epochs                        | 4             |
+| Batch Size                    | 8             |
+| Gradient Accumulation Steps   | 16            |
+| Initial Learning Rate         | 2e-4          |
+| LoRA Parameters               | 78,446,592    |
+| Training Tokens               | 185,040,896   |
 
-Fine-tuning was performed using an Intel 12900K CPU, an Nvidia RTX-3090 GPU, and 64 GB RAM. Total fine-tuning time was 48 GPU hours.
 
+Fine-tuning was performed using an Intel 12900K CPU, an Nvidia RTX-3090 GPU, and 64 GB RAM. Total fine-tuning time was 48 GPU hours.
 
 
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
-
 #### Metrics
 
 - **Accuracy:** Measures the coverage of the generated docstring on code elements like input/output variables. Calculated using cosine similarity between the generated and expert docstring embeddings.