- Created a new JSON file containing evaluation results for the AVAP knowledge models, including scores for faithfulness, answer relevancy, context recall, and context precision.
- Updated the evaluation notebook to use a new embedding model and fixed execution counts for code cells.
- Created `n00 Beir Analysis_cosqa.ipynb` for analyzing CoSQA dataset with BEIR.
- Created `n00 first Analysis.ipynb` for initial analysis with embeddings.
- Implemented `evaluate_embeddings_pipeline.py` to evaluate embedding models across CodexGlue, CoSQA, and SciFact benchmarks.
- Added adapters for Ollama and HuggingFace embeddings to ensure compatibility with BEIR.
- Enhanced error handling and data normalization in embedding processes.
- Included functionality to load datasets from local cache or download if not present.
- Introduced a new notebook for generating synthetic datasets for AVAP, including loading AVAP and MBPP data, and creating prompts for LLM interactions.
pseco