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: full-panel top-k coverage (R3.5)
Browse files
README.md
CHANGED
|
@@ -105,10 +105,7 @@ recall-favouring edge of the plateau (0.9 -> 0.95 would trade 1.9 points of in-s
|
|
| 105 |
and 6 points of test recall for +1.6 points of benchmark precision and 13% fewer false
|
| 106 |
fires). Test-set P/R at 0.9: 0.938 / 0.900 (3-seed means).
|
| 107 |
|
| 108 |
-
Top-k coverage of the true entry (test positives): within each abstract's gene-gate
|
| 109 |
-
candidates (the deployment universe, median 2 candidates) top-1 = 0.797 ± 0.013; against the
|
| 110 |
-
279-entry test-set pool top-1 = 0.870, top-3 = 1.00. Full 2,861-entry panel numbers are in
|
| 111 |
-
the repository's `revision/crossencoder_topk_coverage.csv`.
|
| 112 |
|
| 113 |
## Provenance
|
| 114 |
|
|
|
|
| 105 |
and 6 points of test recall for +1.6 points of benchmark precision and 13% fewer false
|
| 106 |
fires). Test-set P/R at 0.9: 0.938 / 0.900 (3-seed means).
|
| 107 |
|
| 108 |
+
Top-k coverage of the true entry (held-out test positives, fp32): against the **full 2,861-entry panel** top-1 = 0.818, top-3 = 0.991, top-5 = 1.00 (previous release 0.818 / 0.991 / 1.00; untrained MedCPT 0.713 / 0.924 / 0.953; the submitted manuscript's Table 2 reported 0.76 / 0.97 / 0.99 as 5-fold CV means); against the 279-entry test-set pool top-1 = 0.870, top-3 = 1.00; within each abstract's own gene-gate candidates (the deployment universe, median 2 candidates) top-1 = 0.797 ± 0.013. Full table: the repository's `revision/crossencoder_topk_coverage.csv`.
|
|
|
|
|
|
|
|
|
|
| 109 |
|
| 110 |
## Provenance
|
| 111 |
|