Add models README
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README.md
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---
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license: mit
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library_name: sentence-transformers
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pipeline_tag: text-ranking
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tags:
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- cross-encoder
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- reranker
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- climate-fact-checking
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- comp90042
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---
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# New-Methodology finetuned rerankers
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Cross-encoder / reranker checkpoints produced by `scripts/02_train_reranker.py` and related experiments.
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## Subfolders
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| HF path | Local source |
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|---------|--------------|
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| `finetuned_reranker/` | `outputs/retrieval/models/finetuned_reranker` |
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| `ce_minilm_hybrid_hard_epochs2/` | `outputs/retrieval/models/ce_cross-encoder__ms-marco-MiniLM-L6-v2__hybrid_hard__epochs2` |
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| `finetuned_bge_reranker/` | `outputs/retrieval/models/finetuned_bge_reranker` |
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| `finetuned_minilm_hardneg/` | `outputs/retrieval/models/finetuned_minilm_hardneg` |
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| `finetuned_reranker_s60/` | `outputs/retrieval/models/finetuned_reranker_s60` |
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| `pairwise_reranker/` | `outputs/reranker/pairwise/pairwise_reranker` |
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## Load example (sentence-transformers)
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```python
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from sentence_transformers import CrossEncoder
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model = CrossEncoder("jasperyeoh2/new-methodology-rerankers", trust_remote_code=True)
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# For a specific checkpoint, clone and point model_name_or_path to the subfolder.
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```
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## Recommended for MMR pipeline
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Production MMR (位=0.5) uses **zero-shot** `cross-encoder/ms-marco-MiniLM-L6-v2`, not these finetuned weights.
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Finetuned checkpoints are kept for reproducibility and ablation.
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