Update README.md

README.md

@@ -25,4 +25,48 @@ configs:
     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.
+
+# Leaderboard
+