Text Classification
sentence-transformers
Safetensors
English
bert
cross-encoder
biomedical
gene-disease
Gene2Phenotype
LitDD
text-embeddings-inference
Instructions to use tmy100000001/LitDD_crossencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tmy100000001/LitDD_crossencoder with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("tmy100000001/LitDD_crossencoder") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
model card: revised pipeline is screen -> gene gate -> LLM; cross-encoder off the critical path
Browse files
README.md
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Fine-tuned [`ncbi/MedCPT-Cross-Encoder`](https://huggingface.co/ncbi/MedCPT-Cross-Encoder)
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that scores a PubMed **title + abstract** against a **Gene2Phenotype (G2P) LGMDE thread**
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(one gene–disease entry rendered as a 15-field string) and returns a sigmoid relevance score
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in [0, 1]. It
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([biomedicalinformaticsgroup/LitDD_mining](https://github.com/biomedicalinformaticsgroup/LitDD_mining))
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The previous release is preserved at tag [`v1-original`](../../tree/v1-original). It is
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superseded: on the current panel rendering it calls essentially every pair positive at 0.5
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Fine-tuned [`ncbi/MedCPT-Cross-Encoder`](https://huggingface.co/ncbi/MedCPT-Cross-Encoder)
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that scores a PubMed **title + abstract** against a **Gene2Phenotype (G2P) LGMDE thread**
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(one gene–disease entry rendered as a 15-field string) and returns a sigmoid relevance score
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in [0, 1]. It was the candidate-ranking stage of the LitDD pipeline
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([biomedicalinformaticsgroup/LitDD_mining](https://github.com/biomedicalinformaticsgroup/LitDD_mining)).
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> ### ⚠️ Not required by the current pipeline (2026-09-02)
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>
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> The revised pipeline is **screen → gene gate → LLM adjudication**: each abstract is restricted
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> to the Gene2Phenotype entries of the genes actually mentioned in its title+abstract (mean 2.4
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> candidates, median 2), and **all** of those candidates are shown to the LLM with no score
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> threshold. Measured on the held-out annotated test split, that gene gate offers every curated
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> entry for 606/606 curated abstracts (retrieval recall 1.000), and removing the cross-encoder
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> changes end-to-end performance by less than the noise (P 0.840 / R 0.848 without it vs
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> P 0.851 / R 0.850 with it, n = 2,731 abstracts, McNemar p = 0.86).
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>
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> This model is retained because it is **required to reproduce the published pipeline**, and it
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> remains useful as (i) an optional ranker when an abstract yields many candidates and a cap is
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> wanted, and (ii) a confidence signal for auditing LLM answers (keeping only answers whose
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> entries score ≥ 0.95 raises answer precision 0.927 → 0.936 while discarding 15% of answers).
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> It is no longer part of the deployed critical path, and no score gate is applied.
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The previous release is preserved at tag [`v1-original`](../../tree/v1-original). It is
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superseded: on the current panel rendering it calls essentially every pair positive at 0.5
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