Frameworks and Tools Used
Here’s a detailed list of the frameworks and tools utilized in the solution: Frameworks
PyTorch
For building and training the Siamese Networks and fine-tuning BioBERT/ClinicalBERT embeddings.
Provides flexibility in developing neural network architectures.
Hugging Face Transformers
For leveraging pre-trained BioBERT and ClinicalBERT models, which are optimized for biomedical and clinical text.
FAISS (Facebook AI Similarity Search)
For efficient dense vector similarity search in the query retrieval pipeline.
Allows fast approximate nearest neighbor searches for embedding-based retrieval.
RAG (Retrieval-Augmented Generation)
Combines dense retrieval (FAISS) with generative models like GPT or T5 for producing context-aware explanations.
SHAP (SHapley Additive exPlanations)
For model explainability, highlighting key features influencing similarity decisions.
Tools
Python Libraries
scikit-learn: For evaluation metrics like precision, recall, and MAP.
Pandas and NumPy: For data manipulation and preprocessing.
Matplotlib and Seaborn: For visualizing results and embeddings.
BioBERT/ClinicalBERT Models
Domain-specific versions of BERT, fine-tuned for understanding biomedical and clinical text.
TSNE/UMAP
For dimensionality reduction and visualization of embedding spaces.
Jupyter Notebooks
For interactive experimentation, prototyping, and iterative development of the pipeline.
Docker
To containerize the pipeline for reproducibility and scalability.
Frameworks and Tools Used (Updated) Frameworks
PyTorch
For building and training the Siamese Networks and fine-tuning embeddings from BioBERT/ClinicalBERT and LLaMA.
Flexible support for custom neural network architectures.
Hugging Face Transformers
To integrate LLaMA as an alternative or complementary LLM for text generation.
Enables fine-tuning and efficient inference with pre-trained LLaMA weights.
FAISS (Facebook AI Similarity Search)
For dense vector similarity search in the query retrieval pipeline.
Efficiently matches embeddings retrieved using LLaMA-enhanced pipelines.
RAG (Retrieval-Augmented Generation)
Combines LLaMA as the generative component with dense retrievers (e.g., FAISS) to produce detailed, context-aware explanations.
SHAP (SHapley Additive exPlanations)
For explainability, highlighting how features like outcomes and criteria contribute to similarity decisions.
Tools
LLaMA LLM
Fine-tuned for biomedical text tasks, providing state-of-the-art generative capabilities for augmenting retrieved data.
Used to generate summaries, insights, and detailed contextual explanations based on query results.
BioBERT/ClinicalBERT Models
Pretrained embeddings for domain-specific understanding of biomedical and clinical text.
Combined with LLaMA for richer representations and generative outputs.
Python Libraries
scikit-learn: For evaluation metrics like precision, recall, and MAP.
Pandas and NumPy: For data manipulation and preprocessing.
Matplotlib and Seaborn: For visualizing model results.
TSNE/UMAP
For dimensionality reduction to visualize embedding spaces from LLaMA and other models.
Docker
To containerize the pipeline, ensuring reproducibility and scalability.