Neural-cherche

- ColBERT Retriever is now available (complementary to the ColBERT ranker).
- Improved default settings for every models.
- Attention mask added to models.
- ColBERT and SparseEmbed pre-trained checkpoints on HuggingFace: raphaelsty/neural-cherche-colbert and raphaelsty/neural-cherche-sparse-embed.
- Improved ranking loss.
- Addition of benchmarks.

Overall, this version makes easier to fine-tune ColBERT, SparseEmbed and Splade and achieve excellent results without default parameters.

**Introducing Neural-Cherche 1.0.0: The Evolution of Sparsembed**

I'm thrilled to announce the launch of Neural-Cherche 1.0.0, a significant upgrade from Sparsembed, packed with innovative features and enhancements:

- **ColBERT Fine-Tuning & Ranking:** Enhance your search capabilities with fine-tuned ColBERT for more precise and efficient ranking.

- **Revamped Retrievers with Enhanced API:** Experience our newly optimized retrievers. They now come with an improved API that enables users to comprehensively capture and analyze all model outputs.

- **Optimized Training with Refined Hyperparameters:** Benefit from our enhanced training procedure, featuring good default hyperparameters for better performance.

- **Efficiency Boost with Splade and SparseEmbed:** These components have been upgraded to utilize more efficient Sparse Matrices, boosting overall effectiveness.

- **Intelligent Embedding Management:** Once computed, embeddings are now transferred to the CPU, remaining there until needed again. This approach enables extensive, large-scale offline neural searching without overwhelming GPU resources.

- **Comprehensive Documentation:** Get up to speed quickly with the documentation.

- **Improved Evaluation API**

- **A Fresh, New Look with a cool Logo**

Embrace the future of neural search with Neural-Cherche 1.0.0 – a giant leap forward from Sparsembed!

Avoid intersection errors with Sparsembed

Flops scheduler and max aggregation over language models logins.