| --- |
| dataset_info: |
| features: |
| - name: repo_name |
| dtype: string |
| - name: repo_commit |
| dtype: string |
| - name: repo_content |
| dtype: string |
| - name: repo_readme |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 29227644 |
| num_examples: 158 |
| - name: test |
| num_bytes: 8765331 |
| num_examples: 40 |
| download_size: 12307532 |
| dataset_size: 37992975 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: test |
| path: data/test-* |
| license: apache-2.0 |
| task_categories: |
| - summarization |
| tags: |
| - code |
| size_categories: |
| - n<1K |
| --- |
| |
| # Generate README Eval |
|
|
| The generate-readme-eval is a dataset (train split) and benchmark (test split) to evaluate the effectiveness of LLMs |
| when summarizing entire GitHub repos in form of a README.md file. The datset is curated from top 400 real Python repositories |
| from GitHub with at least 1000 stars and 100 forks. The script used to generate the dataset can be found [here](_script_for_gen.py). |
| For the dataset we restrict ourselves to GH repositories that are less than 100k tokens in size to allow us to put the entire repo |
| in the context of LLM in a single call. The `train` split of the dataset can be used to fine-tune your own model, the results |
| reported here are for the `test` split. |
|
|
| To evaluate a LLM on the benchmark we can use the evaluation script given [here](_script_for_eval.py). During evaluation we prompt |
| the LLM to generate a structured README.md file using the entire contents of the repository (`repo_content`). We evaluate the output |
| response from LLM by comparing it with the actual README file of that repository across several different metrics. |
|
|
| In addition to the traditional NLP metircs like BLEU, ROUGE scores and cosine similarity, we also compute custom metrics |
| that capture structural similarity, code consistency, readbility and information retrieval (from code to README). The final score |
| is generated between by taking a weighted average of the metrics. The weights used for the final score are shown below. |
|
|
| ``` |
| weights = { |
| 'bleu': 0.1, |
| 'rouge-1': 0.033, |
| 'rouge-2': 0.033, |
| 'rouge-l': 0.034, |
| 'cosine_similarity': 0.1, |
| 'structural_similarity': 0.1, |
| 'information_retrieval': 0.2, |
| 'code_consistency': 0.2, |
| 'readability': 0.2 |
| } |
| ``` |
|
|
| At the end of evaluation the script will print the metrics and store the entire run in a log file. If you want to add your model to the |
| leaderboard please create a PR with the log file of the run and details about the model. |
|
|
| If we use the existing README.md files in the repositories as the golden output, we would get a score of 56.6 on this benchmark. |
| We can validate it by running the evaluation script with `--oracle` flag. |
| The oracle run log is available [here](oracle_results_20240912_155859.log). |
|
|
| # Leaderboard |
|
|
| bleu: 0.0164 |
| rouge-1: 0.1546 |
| rouge-2: 0.0385 |
| rouge-l: 0.1484 |
| cosine_similarity: 0.4057 |
| structural_similarity: 0.2381 |
| information_retrieval: 0.7250 |
| code_consistency: 0.0477 |
| readability: 0.4481 |
| weighted_score: 0.3216 |
| |
| | Model | Score | BLEU | ROUGE-1 | ROUGE-2 | ROUGE-l | Cosine-Sim | Structural-Sim | Info-Ret | Code-Consistency | Readability | Logs | |
| |:-----:|:-----:|:----:|:-------:|:-------:|:-------:|:----------:|:--------------:|:--------:|:----------------:|:-----------:|:----:| |
| | gpt-4o-mini-2024-07-18 | 32.16 | 1.64 | 15.46 | 3.85 | 14.84 | 40.57 | 23.81 | 72.50 | 4.77 | 44.81 | [link](gpt-4o-mini-2024-07-18_results_20240912_161045.log) | |
| | gpt-4o-2024-08-06 | 33.13 | 1.68 | 15.36 | 3.59 | 14.81 | 40.00 | 23.91 | 74.50 | **8.36** | 44.33 | [link](gpt-4o-2024-08-06_results_20240912_155645.log) | |
| | gemini-1.5-flash-8b-exp-0827 | 32.12 | 1.36 | 14.66 | 3.31 | 14.14 | 38.31 | 23.00 | 70.00 | 7.43 | **46.47** | [link](gemini-1.5-flash-8b-exp-0827_results_20240912_134026.log) | |
| | **gemini-1.5-flash-exp-0827** | **33.43** | 1.66 | **16.00** | 3.88 | **15.33** | **41.87** | 23.59 | **76.50** | 7.86 | 43.34 | [link](gemini-1.5-flash-exp-0827_results_20240912_144919.log) | |
| | gemini-1.5-pro-exp-0827 | 32.51 | **2.55** | 15.27 | **4.97** | 14.86 | 41.09 | **23.94** | 72.82 | 6.73 | 43.34 | [link](gemini-1.5-pro-exp-0827_results_20240912_141225.log) | |
| | oracle-score | 56.79 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 98.24 | 59.00 | 11.01 | 14.84 | [link](oracle_results_20240912_155859.log) | |
