Total runs:
23.9K
Run Growth:
-644
Growth Rate:
-2.63%
Sponsored by
YouArt
abhinand / MedEmbed-large-v0.1
Model's Last Updated: June 07 2025
Introduction of MedEmbed-large-v0.1
Model Details of MedEmbed-large-v0.1
MedEmbed: Specialized Embedding Model for Medical and Clinical Information Retrieval
Model Description
MedEmbed is a family of embedding models fine-tuned specifically for medical and clinical data, designed to enhance performance in healthcare-related natural language processing (NLP) tasks, particularly information retrieval.
GitHub Repo: https://github.com/abhinand5/MedEmbed
Technical Blog Post: https://huggingface.co/blog/abhinand/medembed-finetuned-embedding-models-for-medical-ir
Intended Use
This model is intended for use in medical and clinical contexts to improve information retrieval, question answering, and semantic search tasks. It can be integrated into healthcare systems, research tools, and medical literature databases to enhance search capabilities and information access.
Training Data
The model was trained using a novel synthetic data generation pipeline:
- Source: Clinical notes from PubMed Central (PMC)
- Processing: LLaMA 2 70B model used to generate query-response pairs
- Augmentation: Negative sampling for challenging examples
- Format: Triplets (query, positive response, negative response) for contrastive learning
Performance
MedEmbed consistently outperforms general-purpose embedding models across various medical NLP benchmarks:
- ArguAna
- MedicalQARetrieval
- NFCorpus
- PublicHealthQA
- TRECCOVID
Specific performance metrics (nDCG, MAP, Recall, Precision, MRR) are available in the full documentation.
Limitations
While highly effective for medical and clinical data, this model may not generalize well to non-medical domains. It should be used with caution in general-purpose NLP tasks.
Ethical Considerations
Users should be aware of potential biases in medical data and the ethical implications of AI in healthcare. This model should be used as a tool to assist, not replace, human expertise in medical decision-making.
Citation
If you use this model in your research, please cite:
@software{balachandran2024medembed,
author = {Balachandran, Abhinand},
title = {MedEmbed: Medical-Focused Embedding Models},
year = {2024},
url = {https://github.com/abhinand5/MedEmbed}
}
For more detailed information, visit our GitHub repository.
Runs of abhinand MedEmbed-large-v0.1 on huggingface.co
35.5K
Total runs
-2.2K
24-hour runs
-20.4K
3-day runs
-87.7K
7-day runs
-149.8K
30-day runs
More Information About MedEmbed-large-v0.1 huggingface.co Model
Url of MedEmbed-large-v0.1
MedEmbed-large-v0.1 huggingface.co Url
Provider of MedEmbed-large-v0.1 huggingface.co
Other API from abhinand
Updated:October 21 2024
Total runs:
15.4K
Run Growth:
-8.6K
Growth Rate:
-55.69%
Total runs:
1.8K
Run Growth:
1.8K
Growth Rate:
98.83%
Total runs:
874
Run Growth:
297
Growth Rate:
33.98%
Total runs:
715
Run Growth:
267
Growth Rate:
56.69%
Total runs:
473
Run Growth:
-248
Growth Rate:
-57.27%
Total runs:
244
Run Growth:
140
Growth Rate:
58.82%
Total runs:
168
Run Growth:
32
Growth Rate:
25.81%
Total runs:
148
Run Growth:
1
Growth Rate:
0.69%
Total runs:
135
Run Growth:
76
Growth Rate:
56.30%
Total runs:
121
Run Growth:
7
Growth Rate:
5.88%
Total runs:
117
Run Growth:
13
Growth Rate:
11.82%
Total runs:
105
Run Growth:
0
Growth Rate:
0.00%
Total runs:
97
Run Growth:
40
Growth Rate:
39.22%
Total runs:
95
Run Growth:
33
Growth Rate:
34.02%
Total runs:
89
Run Growth:
-87
Growth Rate:
-94.57%
Total runs:
84
Run Growth:
39
Growth Rate:
48.75%
Total runs:
78
Run Growth:
36
Growth Rate:
47.37%
Total runs:
70
Run Growth:
41
Growth Rate:
59.42%
Total runs:
57
Run Growth:
35
Growth Rate:
61.40%
Total runs:
57
Run Growth:
23
Growth Rate:
38.98%
Total runs:
55
Run Growth:
32
Growth Rate:
58.18%
Total runs:
27
Run Growth:
19
Growth Rate:
67.86%
Total runs:
26
Run Growth:
-11
Growth Rate:
-40.74%
Total runs:
25
Run Growth:
12
Growth Rate:
54.55%
Total runs:
19
Run Growth:
4
Growth Rate:
22.22%
Total runs:
19
Run Growth:
7
Growth Rate:
41.18%
Total runs:
16
Run Growth:
9
Growth Rate:
52.94%
Total runs:
14
Run Growth:
-19
Growth Rate:
-135.71%
Total runs:
14
Run Growth:
4
Growth Rate:
28.57%
Total runs:
13
Run Growth:
0
Growth Rate:
0.00%
Total runs:
12
Run Growth:
8
Growth Rate:
57.14%
Total runs:
9
Run Growth:
3
Growth Rate:
33.33%
Total runs:
7
Run Growth:
2
Growth Rate:
25.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%