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PneumaAI/
DeepPulse-Embedding-0.6b

Name: DeepPulse-Embedding-0.6b
Availability: InStock

Qwen/Qwen3-Embedding-0.6B

finetune

apache-2.0

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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
DeepPulse-Embedding-0.6b	0.9693	0.9751	0.9513	0.9891	0.9935
DeepPulse-Embedding-m3	0.9729	0.9781	0.957	0.9896	0.9948

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.

PneumaAI/DeepPulse-Embedding-0.6b

DeepPulse-Embedding Dense Retrieval Model Series

Self-Built Dataset Evaluation Metrics

Acknowledgement

PneumaAI/
DeepPulse-Embedding-0.6b