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---
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This is
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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language: en
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tags:
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- medembed
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- medical-embedding
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- clinical-embedding
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- information-retrieval
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- sentence-transformers
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license: apache-2.0
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datasets:
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- MedicalQARetrieval
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- NFCorpus
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- PublicHealthQA
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- TRECCOVID
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- ArguAna
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metrics:
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- nDCG
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- MAP
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- Recall
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- Precision
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- MRR
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base_model:
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- BAAI/bge-small-en-v1.5
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# MedEmbed: Specialized Embedding Model for Medical and Clinical Information Retrieval
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![benchmark-scores](https://cdn-uploads.huggingface.co/production/uploads/60c8619d95d852a24572b025/gTx5-m68LQ3eyNd6fLki2.png)
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## Model Description
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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.
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**GitHub Repo:** [https://github.com/abhinand5/MedEmbed](https://github.com/abhinand5/MedEmbed)
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**Technical Blog Post:** [https://huggingface.co/blog/abhinand/medembed-finetuned-embedding-models-for-medical-ir](https://huggingface.co/blog/abhinand/medembed-finetuned-embedding-models-for-medical-ir)
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## Intended Use
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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.
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## Training Data
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![synthetic-datagen-flow](https://cdn-uploads.huggingface.co/production/uploads/60c8619d95d852a24572b025/asaA5QDO_j0PWFQV9NXCu.png)
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The model was trained using a novel synthetic data generation pipeline:
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1. Source: Clinical notes from PubMed Central (PMC)
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2. Processing: LLaMA 2 70B model used to generate query-response pairs
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3. Augmentation: Negative sampling for challenging examples
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4. Format: Triplets (query, positive response, negative response) for contrastive learning
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## Performance
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MedEmbed consistently outperforms general-purpose embedding models across various medical NLP benchmarks:
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- ArguAna
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- MedicalQARetrieval
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- NFCorpus
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- PublicHealthQA
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- TRECCOVID
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Specific performance metrics (nDCG, MAP, Recall, Precision, MRR) are available in the full documentation.
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## Limitations
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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.
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## Ethical Considerations
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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.
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## Citation
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If you use this model in your research, please cite:
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```bibtex
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@software{balachandran2024medembed,
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author = {Balachandran, Abhinand},
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title = {MedEmbed: Medical-Focused Embedding Models},
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year = {2024},
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url = {https://github.com/abhinand5/MedEmbed}
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}
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```
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For more detailed information, visit our GitHub repository.
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