ProMiNER Russian BioNNE-L Retriever
Dense Sentence Transformers retriever for Russian biomedical entity linking in the BioNNE-L task.
This model is part of ProMiNER, a Russian-track biomedical entity-linking
system for BioNNE-L. The system links mentions from NEREL-BIO/BioNNE-L texts
to UMLS concepts by combining dense retrieval and cross-encoder reranking.
- GitHub:
https://github.com/bikingSolo/prominer
- Model type:
sentence-transformers
- Base/initializer:
andorei/BERGAMOT-multilingual-GAT
Training
Fine-tuned on Russian BioNNE-L mention-to-UMLS concept pairs with InfoNCE-style in-batch negatives and CUI-aware batching. This model is used to build candidate pools for the final reranker.
Hyperparameters:
model_name: andorei/BERGAMOT-multilingual-GAT
epochs: 10
train_batch_size: 64
learning_rate: 2e-05
weight_decay: 0.01
warmup_ratio: 0.1
max_seq_length: 64
num_train_pairs: 172593
selection_metric: Acc@20
According to the Acc@20 on dev, the best epoch is 10.
Full local metadata exported from MLflow is included in prominer_metadata/.
Evaluation
| split | Acc@1 | Acc@5 | Acc@10 | Acc@20 | MRR |
|---|
| dev | 0.6330442720219078 | 0.8156093108169785 | 0.8461889548151529 | 0.8667275216795983 | 0.7125038140627878 |
| test | 0.6980474198047419 | 0.8375174337517434 | | | 0.7573163644816359 |
Usage
1from sentence_transformers import SentenceTransformer
2
3model = SentenceTransformer("bikingSolo/prominer-ru-retriever")
4mention_embeddings = model.encode(
5 ["вестибулокохлеарный нерв"],
6 normalize_embeddings=True,
7)
Intended Use
This checkpoint is intended for research and reproducibility of the ProMiNER
BioNNE-L Russian entity-linking pipeline. For the full system, use:
prominer-ru-retriever to retrieve candidate UMLS concepts.
prominer-ru-reranker to rerank those candidates with candidate-context profiles.
The dictionary-pretrained cross-encoder is primarily an intermediate checkpoint
used to initialize the final reranker.
Data and Citation
Training and evaluation use BioNNE-L/NEREL-BIO resources and UMLS-derived
terminology available in this repository's data layout. Cite the relevant
NEREL-BIO and BioNNE-L papers when using this model.