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nqd-301125/
bge-m3-medical-vi-dense

Name: bge-m3-medical-vi-dense
Availability: InStock

sentence-transformers

text-embeddings-inference

endpoints_compatible

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BGE-M3 Medical Vietnamese Dense Embedding

This model is a dense SentenceTransformers embedding model fine-tuned from BAAI/bge-m3 on nqd-301125/medical_data.

Important

This repository is intended to be loadable directly with:

python

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nqd-301125/bge-m3-medical-vi-dense")

The model was trained with PEFT LoRA. The best LoRA adapter was selected manually by dev cosine NDCG@10, then merged into the base weights before saving. The final uploaded artifact should not be adapter-only.

Best adapter selection

The best adapter is selected manually because Trainer.load_best_model_at_end can fail with PEFT-wrapped SentenceTransformers checkpoints due to state_dict key prefix mismatch.

json

{
  "metric_name": "eval_medical_dev_cosine_ndcg@10",
  "score": 0.9493321051495134,
  "step": 450,
  "epoch": 2.9605263157894735,
  "source": "trainer_eval",
  "state_path": "/content/bge_m3_medical_vi_dense_run/best_lora_adapter_state/best_adapter_state.pt"
}

Training setup

Base model: BAAI/bge-m3
Dataset: nqd-301125/medical_data
Loss: MultipleNegativesRankingLoss(scale=20.0)
Max sequence length: 6000
Epochs: 3
Batch size: 64
Gradient accumulation: 1
Learning rate: 3e-05
Warmup ratio: 0.03
Weight decay: 0.01
LoRA r: 16
LoRA alpha: 32
LoRA dropout: 0.0
LoRA target modules: ['query', 'key', 'value', 'dense']

Scope

This is a dense retrieval fine-tune. It should not be described as explicitly fine-tuning the sparse or multi-vector components of BGE-M3.

Internal evaluation

Evaluator: InformationRetrievalEvaluator Main metric: cosine NDCG@10 Split: train/dev/test = 90/5/5 after cleaning and exact deduplication.

Local merged metrics

Dev:

json

{
  "medical_dev_cosine_accuracy@1": 0.9276437847866419,
  "medical_dev_cosine_accuracy@3": 0.9517625231910947,
  "medical_dev_cosine_accuracy@5": 0.961038961038961,
  "medical_dev_cosine_accuracy@10": 0.974025974025974,
  "medical_dev_cosine_precision@1": 0.9276437847866419,
  "medical_dev_cosine_precision@3": 0.31725417439703146,
  "medical_dev_cosine_precision@5": 0.1922077922077922,
  "medical_dev_cosine_precision@10": 0.09740259740259741,
  "medical_dev_cosine_recall@1": 0.9276437847866419,
  "medical_dev_cosine_recall@3": 0.9517625231910947,
  "medical_dev_cosine_recall@5": 0.961038961038961,
  "medical_dev_cosine_recall@10": 0.974025974025974,
  "medical_dev_cosine_ndcg@10": 0.949333673165304,
  "medical_dev_cosine_mrr@10": 0.9415967252701946,
  "medical_dev_cosine_map@100": 0.9424855540110175
}

Test:

json

{
  "medical_test_cosine_accuracy@1": 0.9131238447319778,
  "medical_test_cosine_accuracy@3": 0.9537892791127541,
  "medical_test_cosine_accuracy@5": 0.9593345656192237,
  "medical_test_cosine_accuracy@10": 0.9685767097966729,
  "medical_test_cosine_precision@1": 0.9131238447319778,
  "medical_test_cosine_precision@3": 0.31792975970425136,
  "medical_test_cosine_precision@5": 0.1918669131238447,
  "medical_test_cosine_precision@10": 0.09685767097966728,
  "medical_test_cosine_recall@1": 0.9131238447319778,
  "medical_test_cosine_recall@3": 0.9537892791127541,
  "medical_test_cosine_recall@5": 0.9593345656192237,
  "medical_test_cosine_recall@10": 0.9685767097966729,
  "medical_test_cosine_ndcg@10": 0.9423110570787226,
  "medical_test_cosine_mrr@10": 0.933755831352874,
  "medical_test_cosine_map@100": 0.9350808229772267
}

Base model metrics

Dev:

json

{
  "medical_dev_cosine_accuracy@1": 0.8812615955473099,
  "medical_dev_cosine_accuracy@3": 0.935064935064935,
  "medical_dev_cosine_accuracy@5": 0.9461966604823747,
  "medical_dev_cosine_accuracy@10": 0.9591836734693877,
  "medical_dev_cosine_precision@1": 0.8812615955473099,
  "medical_dev_cosine_precision@3": 0.3116883116883116,
  "medical_dev_cosine_precision@5": 0.18923933209647495,
  "medical_dev_cosine_precision@10": 0.09591836734693879,
  "medical_dev_cosine_recall@1": 0.8812615955473099,
  "medical_dev_cosine_recall@3": 0.935064935064935,
  "medical_dev_cosine_recall@5": 0.9461966604823747,
  "medical_dev_cosine_recall@10": 0.9591836734693877,
  "medical_dev_cosine_ndcg@10": 0.9219673283268356,
  "medical_dev_cosine_mrr@10": 0.9098271343169301,
  "medical_dev_cosine_map@100": 0.911105170851867
}

Test:

json

{
  "medical_test_cosine_accuracy@1": 0.8983364140480592,
  "medical_test_cosine_accuracy@3": 0.9390018484288355,
  "medical_test_cosine_accuracy@5": 0.9519408502772643,
  "medical_test_cosine_accuracy@10": 0.9593345656192237,
  "medical_test_cosine_precision@1": 0.8983364140480592,
  "medical_test_cosine_precision@3": 0.3130006161429451,
  "medical_test_cosine_precision@5": 0.19038817005545283,
  "medical_test_cosine_precision@10": 0.09593345656192237,
  "medical_test_cosine_recall@1": 0.8983364140480592,
  "medical_test_cosine_recall@3": 0.9390018484288355,
  "medical_test_cosine_recall@5": 0.9519408502772643,
  "medical_test_cosine_recall@10": 0.9593345656192237,
  "medical_test_cosine_ndcg@10": 0.9293159253064707,
  "medical_test_cosine_mrr@10": 0.9195852771176244,
  "medical_test_cosine_map@100": 0.920600642478801
}

Limitations

This model is not a medical diagnosis system.
Retrieval results should be reviewed before any clinical or safety-critical use.
Internal evaluation is not a substitute for external benchmarks.
Dataset distribution may not represent all Vietnamese medical domains or real-world user queries.