| --- |
| license: mit |
| library_name: transformers |
| tags: |
| - biomedical |
| - entity-linking |
| - retrieval |
| - reranking |
| - lora |
| language: |
| - en |
| --- |
| |
| # PILOT checkpoints |
|
|
| Trained artifacts for **PILOT: Neighborhood-Aware Dual Biomedical Entity Linking**. |
|
|
| Code (anonymized for review): https://anonymous.4open.science/r/PILOT-92B4 |
|
|
| PILOT links a biomedical mention, in its sentence context, to an entity in a large ontological knowledge |
| base, in three stages: neighborhood-aware retrieval → dual reranking → score fusion. |
|
|
| ## Contents |
|
|
| | folder | what it is | size | |
| |---|---|---| |
| | `retriever/<ds>/` | SapBERT fine-tuned on that dataset's training mentions. The encoder `φ(·)` used by both the retriever and the surface-form reranker. | 499 MB | |
| | `reranker/<ds>/` | LoRA adapter for the contextual reranker. Load on top of the base model below. | 151 MB (4B) / 47 MB (0.6B) | |
|
|
| `<ds>` ∈ `ncbi`, `bc5cdr`, `cometa`, `mm`, `aap_fold0` … `aap_fold9` (AAP is 10-fold cross-validation). |
|
|
| | dataset | base reranker | |
| |---|---| |
| | ncbi, bc5cdr, cometa, aap_fold0..9 | `Qwen/Qwen3-Reranker-4B` | |
| | mm | `Qwen/Qwen3-Reranker-0.6B` | |
| |
| Adapters rather than merged models: the merge is deterministic, and 14 merged 4B checkpoints would be |
| ~127 GB against ~2 GB of adapters. |
| |
| ## Results |
| |
| Test R@1 (top-1 accuracy); AAP is the 10-fold mean. |
| |
| | NCBI | BC5CDR | COMETA | AAP | MM-ST21pv | |
| |---|---|---|---|---| |
| | 93.44 | 95.75 | 87.98 | 91.72 | 74.93 | |
| |
| ## Usage |
| |
| ```bash |
| pip install -U huggingface_hub |
| hf download anon4papersubmission/PILOT-checkpoints --local-dir checkpoints |
| ``` |
| |
| The retriever is a plain HF encoder: |
| |
| ```python |
| from transformers import AutoTokenizer, AutoModel |
| tok = AutoTokenizer.from_pretrained("checkpoints/retriever/ncbi") |
| enc = AutoModel.from_pretrained("checkpoints/retriever/ncbi") # CLS pooling, 768-d |
| ``` |
| |
| The reranker is a LoRA adapter — merge it, or load it on the base model: |
| |
| ```bash |
| python -c "from swift.pipelines import export_main; export_main()" \ |
| --adapters checkpoints/reranker/ncbi --merge_lora true \ |
| --output_dir models/rerank/ncbi_merged --use_hf true |
| ``` |
| |
| See the code repository for the full pipeline (data preparation through evaluation) and for how these |
| plug into stages 5 and 7. |
| |
| ## License |
| |
| MIT for the checkpoints. The underlying benchmarks and UMLS retain their own licenses; UMLS requires a |
| free NLM account. |
| |
