New-Methodology finetuned rerankers

Cross-encoder / reranker checkpoints produced by scripts/02_train_reranker.py and related experiments.

Subfolders

HF path	Local source
`finetuned_reranker/`	`outputs/retrieval/models/finetuned_reranker`
`ce_minilm_hybrid_hard_epochs2/`	`outputs/retrieval/models/ce_cross-encoder__ms-marco-MiniLM-L6-v2__hybrid_hard__epochs2`
`finetuned_bge_reranker/`	`outputs/retrieval/models/finetuned_bge_reranker`
`finetuned_minilm_hardneg/`	`outputs/retrieval/models/finetuned_minilm_hardneg`
`finetuned_reranker_s60/`	`outputs/retrieval/models/finetuned_reranker_s60`
`pairwise_reranker/`	`outputs/reranker/pairwise/pairwise_reranker`

Load example (sentence-transformers)

from sentence_transformers import CrossEncoder

model = CrossEncoder("jasperyeoh2/new-methodology-rerankers", trust_remote_code=True)
# For a specific checkpoint, clone and point model_name_or_path to the subfolder.

Recommended for MMR pipeline

Production MMR (λ=0.5) uses zero-shot cross-encoder/ms-marco-MiniLM-L6-v2, not these finetuned weights. Finetuned checkpoints are kept for reproducibility and ablation.

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