#neural retrieval

SPLADE utilizes BERT to build sparse models that enhance the first-stage ranking in information retrieval tasks. With the adoption of sparse representations, the models achieve efficiency gains and clarity in lexical matching. Recent improvements include static pruning for neural retrievers and advanced training techniques. The models are versatile across various domains. Pre-trained versions are accessible on Hugging Face, allowing for efficient performance comparable to traditional methods, with reduced latency.

Designed for scalable neural retrieval, this toolkit facilitates efficient model training and inference. It integrates parameter-efficient methods such as LoRA and advanced technologies like DeepSpeed and flash attention. Users can access and finetune top pre-trained models, including BGE-Embedding and Instruct-E5, via HuggingFace. Self-contained datasets support various tasks, ensuring efficient training on billion-scale LLMs with GPUs and TPUs. This makes it an excellent choice for researchers seeking to enhance retrieval systems using sophisticated techniques.