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@@ -48,7 +48,7 @@ the LLM to generate a structured README.md file using the entire contents of the
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  response from LLM by comparing it with the actual README file of that repository across several different metrics.
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  In addition to the traditional NLP metircs like BLEU, ROUGE scores and cosine similarity, we also compute custom metrics
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- that capture structural similarity, code consistency, readability ([FRES](https://simple.wikipedia.org/wiki/Flesch_Reading_Ease)) and information retrieval (from code to README). The final score
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  is generated between by taking a weighted average of the metrics. The weights used for the final score are shown below.
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  ```
 
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  response from LLM by comparing it with the actual README file of that repository across several different metrics.
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  In addition to the traditional NLP metircs like BLEU, ROUGE scores and cosine similarity, we also compute custom metrics
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+ 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
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  is generated between by taking a weighted average of the metrics. The weights used for the final score are shown below.
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  ```