README.md

---
library_name: transformers
tags: []
---


## Model Description

<!-- Provide a longer summary of what this model is/does. -->
LoRA adapter weights from fine-tuning [BioMobileBERT](https://huggingface.co/nlpie/bio-mobilebert) on the MIMIC-III mortality prediction task. The [PEFT](https://github.com/huggingface/peft) was used and the model was trained for a maximum of 5 epochs with early stopping, full details can be found at the [github repo](https://github.com/nlpie-research/efficient-ml). 

<!-- - **Developed by:** Niall Taylor -->
<!-- - **Shared by [Optional]:** More information needed -->
- **Model type:** Language model LoRA adapter
- **Language(s) (NLP):** en
- **License:** apache-2.0
- **Parent Model:** BioMobileBERT
- **Resources for more information:** 
    - [GitHub Repo](https://github.com/nlpie-research/efficient-ml)
    - [Associated Paper](https://arxiv.org/abs/2402.10597)

<!-- # Uses -->

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<!-- ## Direct Use -->

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<!-- ## Downstream Use [Optional] -->

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# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>


```python
from peft import AutoPeftModelForCausalLM, AutoPeftModelForSequenceClassification
from transformers import AutoTokenizer

model_name = "NTaylor/bio-mobilebert-mimic-mp-lora"

# load using AutoPeftModelForSequenceClassification
model = AutoPeftModelForSequenceClassification.from_pretrained(lora_id)

# use base llama tokenizer
tokenizer = AutoTokenizer.from_pretrained("nlpie/bio-mobilebert")

# example input
text = "Clinical note..."
inputs = tokenizer(text, return_tensors="pt")
outputs = reloaded_model(**inputs)
# extract prediction from outputs based on argmax of logits
pred = torch.argmax(outputs.logits, axis = -1)
print(f"Prediction is: {pred}") # binary classification: 1 for mortality
```


</details>

## Out-of-Scope Use

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This model and LoRA weights were trained on the MIMIC-III dataset and are not intended for use on other datasets, nor be used in any real clinical setting. The experiments were conducted as a means of exploring the potential of LoRA adapters for clinical NLP tasks, and the model should not be used for any other purpose.


<!-- # Bias, Risks, and Limitations -->

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<!-- Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. -->


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# Training Details

## Training Data

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<!-- ## Training Procedure -->

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<!-- # Evaluation -->

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### Testing Data -->

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<!-- ### Factors -->

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<!-- ## Results 

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# Model Examination

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# Environmental Impact -->

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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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# Technical Specifications [optional]

## Model Architecture and Objective

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## Compute Infrastructure

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### Hardware

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### Software

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# Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
``````
@misc{taylor2024efficiency,
      title={Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks}, 
      author={Niall Taylor and Upamanyu Ghose and Omid Rohanian and Mohammadmahdi Nouriborji and Andrey Kormilitzin and David Clifton and Alejo Nevado-Holgado},
      year={2024},
      eprint={2402.10597},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
``````
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<!-- # Glossary [optional] -->

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