Dataset generation #164
Draft
Dataset generation #164
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Signed-off-by: Kannav02 <[email protected]>
Signed-off-by: Kannav02 <[email protected]>
…ation Signed-off-by: Kannav02 <[email protected]>
Signed-off-by: Kannav02 <[email protected]>
Signed-off-by: Jack Luar <[email protected]>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR introduces a complete dataset generation and evaluation pipeline for creating high-quality question-answer pairs from OpenROAD documentation. The system includes automated QA pair generation using Gemini Pro and comprehensive quality evaluation metrics
The reference for all of the code and logic has been taken from the following link:
https://huggingface.co/learn/cookbook/en/rag_evaluation
The following files have been added under the folder
dataset_gen_evaleval_dataset.py: Evaluation script that loads generated QA pairs and applies quality metricsgenerate_qa_pairs.py: Automated QA pair generation using Gemini Pro 2.5 to create factoid questions from domain-specific document chunksingest_doc.py: Document processing pipeline that chunks and indexes PDF/Markdown files into FAISSquality_agents.py: Custom DeepEval metrics implementation with three quality assessment classes