metadata
license: mit
library_name: sentence-transformers
pipeline_tag: text-ranking
tags:
- cross-encoder
- reranker
- climate-fact-checking
- comp90042
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.