Update README.md

README.md

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 ---
 license: mit
+language:
+- en
+tags:
+- medical
 ---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+Labrador is a pre-trained continuous Transformer model for masked ***lab*** modeling.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+Laboratory data are a rich source of information about a patient's health. They are often used to diagnose and monitor disease, and to guide treatment. However, lab values are continuous, often missing and therefore difficult to model with the Transformer architecture. Labrador solves this problem by jointly embedding lab values with a token for the lab test identifier so that the quantitative and qualitative information from each test is combined into a single representation. 
+
+Labrador is pre-trained on a large corpus of 100 million lab tests from over 260,000 patients. We rigorously evaluate Labrador on intrinsic and extrinsic tasks, including four real-world problems: cancer diagnosis, COVID-19 diagnosis, predicting elevated alcohol consumption and ICU mortality due to sepsis. We find that Labrador is superior to BERT across all evaluations but both are outperformed by XGBoost indicating that transfer learning from continuous EHR data is still an open problem. 
+
+We discuss the limitations of our approach and suggest future directions for research in the corresponding paper, [Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data](). 
+
+
+- **Developed by:** David Bellamy
+- **Model type:** BERT-style transformer
+- **License:** MIT
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+The base models can be used directly to impute lab values and/or MIMIC lab codes conditional on a set of lab values and lab codes. 
+
+### Downstream Use
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+The associated codebase includes a fine-tuning wrapper class that can be used to repurpose these base models for downstream regression or classification tasks. 
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+These models were solely pre-trained on patient data from [MIMIC-IV](https://mimic.mit.edu/). This population is not representative of all patients and therefore the statistical patterns that these models learned will not apply equally well to all individuals.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Caution should be used when applying these models to downstream prediction tasks. Be sure to include a fairness assessment in your evaluations in order to assess model bias.
+
+## How to Get Started with the Model
+
+See the [Get Started instructions]() with the associated codebase.
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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).
+
+- **Hardware Type:** A100 PCIe 40GB
+- **Hours used:** 240 (for pre-training, not counting fine-tuning evaluations)
+- **Cloud Provider:** Private infrastructure
+- **Compute Region:** N/A
+- **Carbon Emitted:** 50 kg CO2 eq.
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+