https://github.com/huacenxu/embedding-models-for-ai-retrieval

This project develops a domain-specific embedding model to enhance document retrieval in AI-powered search systems. It incorporates techniques like synthetic data generation, model fine-tuning, and vector search using FAISS, evaluated with MRR@5 for performance.
https://github.com/huacenxu/embedding-models-for-ai-retrieval

document-retrieval embedding-models faiss machine-learning mrr nlp reallifeproject semantic-search

Last synced: 3 months ago
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This project develops a domain-specific embedding model to enhance document retrieval in AI-powered search systems. It incorporates techniques like synthetic data generation, model fine-tuning, and vector search using FAISS, evaluated with MRR@5 for performance.

• Host: GitHub
• URL: https://github.com/huacenxu/embedding-models-for-ai-retrieval
• Owner: huacenxu
• Created: 2024-12-29T15:31:29.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-12-29T15:50:21.000Z (5 months ago)
• Last Synced: 2025-01-19T14:59:10.606Z (5 months ago)
• Topics: document-retrieval, embedding-models, faiss, machine-learning, mrr, nlp, reallifeproject, semantic-search
• Language: Python
• Homepage:
• Size: 4.88 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
### Embedding-Models-for-AI-Retrieval

#### Overview

This project is inspired by a 2024 data science competition focused on improving document retrieval performance through advanced embedding models. Participants developed domain-specific embedding models to enhance retrieval accuracy for AI-powered search and retrieval systems. The competition emphasized creativity, collaboration, and innovation in building cutting-edge AI solutions.

#### Key Features

Custom Embedding Model:

Implements a fine-tuned embedding model using SentenceTransformers and open-source models.

Designed to optimize document retrieval accuracy in real-world scenarios.

Vector Search with FAISS:

Utilizes FAISS for efficient indexing and similarity-based document retrieval.

Evaluation Metrics:

Evaluates models using Mean Reciprocal Rank (MRR@5) to measure relevance and accuracy of retrieved results.

Data Augmentation:

Incorporates synthetic queries to expand training datasets and improve model robustness.

#### Workflow

Data Preparation:

Processes a corpus of documents (chunked) and query datasets for training and evaluation.

Model Training:

Fine-tunes pre-trained embedding models with advanced loss functions like MultipleNegativesSymmetricRankingLoss.

Document Indexing:

Encodes and indexes documents using FAISS for efficient search.

Query Retrieval:

Searches indexed documents using query embeddings.

Evaluation:

Measures retrieval accuracy with MRR@5 and reports metrics.

#### Key Takeaways

Our script placed us among the top 5 teams for MRR@5 out of approximately 50 participating teams.

Insights from the Top 3 Teams

Top Techniques:

1. Data augmentation with synthetic queries to expand the training dataset.

2. Experimenting with advanced loss functions and hyperparameter tuning.

3. Adding linear adapters and low-rank adaptation (LoRA).

4. Leveraging ensemble methods and fine-tuning retriever/re-ranker models.

Challenges:

1. Limited compute resources.

2. Time constraints for experimentation.

Future Scope:

1. Exploring advanced pre-processing techniques.

2. Utilizing masked language modeling.

3. Improving document chunking methods.

#### Usage

Clone Repository:

git clone https://github.com/yourusername/embedding-model-competition.git

cd embedding-model-competition

Prepare Data: 

Place your data in the data/ directory. The data should include two columns: query and positive, representing questions and corresponding answers. Ensure the following files are included:

document_corpus.csv for documents (original data).

train_queries.csv and test_queries.csv for training and testing queries.

Run the code:

Train and evaluate the model:

python src/run_pipeline.py

Output:

The script generates metrics and evaluation reports in the results/ directory.

Example Metrics

Metric	Train MRR@5	Test MRR@5

Baseline	0.713	0.762

Fine-tuned	0.790	0.809

#### Requirements

Python 3.8+

Required libraries: torch, transformers, faiss, sentence-transformers, datasets, numpy.