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DeepPulse-Embedding-m3
WangLong213/DeepPulse-Embedding-m3
DeepPulse-Embedding-m3 is a machine learning model from WangLong213. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
DeepPulse (深度把脉) is a member of 心语心言's open-source TCM series models. This project includes two dense retrieval (Embedding) models fine-tuned on different base models:
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At a glance
- License
- apache-2.0
- Model type
- xlm-roberta
- Access
- Public
- Created
- Mar 19, 2026
- Updated
- Mar 19, 2026
- SHA
- e9c0cb97
Base models
Model card
From the Hugging Face model README
DeepPulse-Embedding Dense Retrieval Model Series
DeepPulse (深度把脉) is a member of 心语心言's open-source TCM series models. This project includes two dense retrieval (Embedding) models fine-tuned on different base models:
- DeepPulse-Embedding-m3: Fine-tuned based on
BGE-m3. - DeepPulse-Embedding-0.6b: Fine-tuned based on
Qwen3-0.6B.
Both models are fine-tuned using a self-built medical dataset (especially TCM data) and are optimized for document chunk retrieval scenarios in the medical field.
Self-Built Dataset Evaluation Metrics
The evaluation results on the self-built medical dataset are as follows. It can be seen that the DeepPulse series models outperform the original base models on all metrics.
| Model Name | MRR | NDCG@10 | Recall@1 | Recall@5 | Recall@10 |
|---|---|---|---|---|---|
| Qwen3-0.6B | 0.9458 | 0.9566 | 0.9157 | 0.9822 | 0.99 |
| bge-m3 | 0.9418 | 0.9519 | 0.9109 | 0.98 | 0.9831 |
| <font color="red">DeepPulse-Embedding-0.6b</font> | <font color="red">0.9693</font> | <font color="red">0.9751</font> | <font color="red">0.9513</font> | <font color="red">0.9891</font> | <font color="red">0.9935</font> |
| <font color="red">DeepPulse-Embedding-m3</font> | <font color="red">0.9729</font> | <font color="red">0.9781</font> | <font color="red">0.957</font> | <font color="red">0.9896</font> | <font color="red">0.9948</font> |
Acknowledgement
DeepPulse-Embedding was trained by the algorithm team from 心语心言.
- ChenCh2002 completed the code implementation, model training, and evaluation work.
- ChenCh2002 and Night-Quiet completed the data collection and organization work together.
- The project was led by Night-Quiet.
- Type
- xlm-roberta
- License
- apache-2.0
- Created
- Mar 19, 2026
- Updated
- Mar 19, 2026