AI BriefingSparse Embedding Model Training Guide
Key point
A detailed guide on training and fine-tuning Sparse Embedding models using the Sentence Transformers library.
Details
Provides a detailed guide to training and fine-tuning Sparse Embedding models using the Sentence Transformers library. Sparse Embedding models are notable for delivering high performance at low cost in Hybrid Search or Retrieve and Rerank scenarios.
Explains the following core components needed for training, along with practical examples:
- Models: Splade, Inference-free Splade, CSR, etc.
- Datasets: How to use Hugging Face Hub data and local data (CSV, JSON, Parquet, etc.)
- Training Configuration: Loss Function, Training Arguments, Evaluator, Trainer class
- Others: Vector Database integration and practical training tips
It also covers how to utilize the various pretrained Sparse Encoder models available on the Hugging Face Hub, including the SPLADE model collection.
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