---
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 (from code to README), readability ([FRES](https://simple.wikipedia.org/wiki/Flesch_Reading_Ease)) and information retrieval. 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 56.79 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

The current SOTA model on this benchmark in zero shot setting is **Gemini-1.5-Flash-Exp-0827**. 
It scores the highest across a number of different metrics.

| Model | Score | BLEU | ROUGE-1 | ROUGE-2 | ROUGE-l | Cosine-Sim | Structural-Sim | Info-Ret | Code-Consistency | Readability | Logs |  
|:-----:|:-----:|:----:|:-------:|:-------:|:-------:|:----------:|:--------------:|:--------:|:----------------:|:-----------:|:----:|
| llama3.1-8b-instruct | 24.43 | 0.72 | 11.96 | 1.69 | 11.51 | 30.29 | 24.16 | 44.50 | 7.96 | 37.90 | [link](llama3.1-8b-instruct-fp16_results_20240912_185437.log) |
| mistral-nemo-instruct-2407 | 25.62 | 1.09 | 11.24 | 1.70 | 10.94 | 26.62 | 24.26 | 52.00 | **8.80** | 37.30 | [link](mistral-nemo-12b-instruct-2407-fp16_results_20240912_182234.log) |
| 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) | 
| o1-mini-2024-09-12 | 33.05 | **3.13** | 15.39 | 3.51 | 14.81 | **42.49** | **27.55** | **80.00** | 7.78 | 35.27 | [link](o1-mini_results_20240913_183521.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) | 

## Few-Shot

This benchmark is interesting because it is not that easy to few-shot your way to improve performance. There are couple of reasons for that:

1) The average context length required for each item can be up to 100k tokens which makes it out of the reach of most
models except Google Gemini which has a context legnth of up to 2 Million tokens.

2) There is a trade-off in accuracy inherit in the benchmark as adding more examples makes some of the metrics like `information_retrieval`
and `readability` worse. At larger contexts models do not have perfect recall and may miss important information. 

Our experiments with few-shot prompts confirm this, the maximum overall score is at 1-shot and adding more examples doesn't help after that.

| Model | Score | BLEU | ROUGE-1 | ROUGE-2 | ROUGE-l | Cosine-Sim | Structural-Sim | Info-Ret | Code-Consistency | Readability | Logs |  
|:-----:|:-----:|:----:|:-------:|:-------:|:-------:|:----------:|:--------------:|:--------:|:----------------:|:-----------:|:----:|
| 0-shot-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) | 
| **1-shot-gemini-1.5-flash-exp-0827** | **35.40** | **21.81** | **34.00** | **24.97** | **33.61** | **61.53** | **37.60** | 61.00 | 12.89 | 27.22 | [link](1-shot-gemini-1.5-flash-exp-0827_results_20240912_183343.log) | 
| 3-shot-gemini-1.5-flash-exp-0827 | 33.10 | 20.02 | 32.70 | 22.66 | 32.21 | 58.98 | 34.54 | 60.50 | **13.09** | 20.52 | [link](3-shot-gemini-1.5-flash-exp-0827_results_20240912_191049.log) | 
| 5-shot-gemini-1.5-flash-exp-0827 | 33.97 | 19.24 | 32.31 | 21.48 | 31.74 | 61.49 | 33.17 | 59.50 | 11.48 | 27.65 | [link](5-shot-gemini-1.5-flash-exp-0827_results_20240912_180343.log) | 
| 7-shot-gemini-1.5-flash-exp-0827 | 33.00 | 15.43 | 28.52 | 17.18 | 28.07 | 56.25 | 33.55 | 63.50 | 12.40 | 24.15 | [link](7-shot-gemini-1.5-flash-exp-0827_results_20240912_203632.log) |