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library_name: transformers |
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tags: [] |
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--- |
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## Model Description |
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<!-- Provide a longer summary of what this model is/does. --> |
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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). |
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<!-- - **Developed by:** Niall Taylor --> |
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<!-- - **Shared by [Optional]:** More information needed --> |
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- **Model type:** Language model LoRA adapter |
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- **Language(s) (NLP):** en |
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- **License:** apache-2.0 |
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- **Parent Model:** BioMobileBERT |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/nlpie-research/efficient-ml) |
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- [Associated Paper](https://arxiv.org/abs/2402.10597) |
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<!-- # Uses --> |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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<!-- ## Direct Use --> |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> |
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<!-- ## Downstream Use [Optional] --> |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> |
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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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<details> |
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<summary> Click to expand </summary> |
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```python |
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from peft import AutoPeftModelForCausalLM, AutoPeftModelForSequenceClassification |
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from transformers import AutoTokenizer |
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model_name = "NTaylor/bio-mobilebert-mimic-mp-lora" |
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# load using AutoPeftModelForSequenceClassification |
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model = AutoPeftModelForSequenceClassification.from_pretrained(lora_id) |
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# use base llama tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("nlpie/bio-mobilebert") |
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# example input |
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text = "Clinical note..." |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = reloaded_model(**inputs) |
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# extract prediction from outputs based on argmax of logits |
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pred = torch.argmax(outputs.logits, axis = -1) |
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print(f"Prediction is: {pred}") # binary classification: 1 for mortality |
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``` |
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</details> |
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## Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> |
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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. |
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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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<!-- 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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<!-- ## 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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# Training Details |
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## Training Data |
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<!-- This should link to a Data 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 on training data 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 |
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More information needed --> |
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<!-- ### Speeds, Sizes, Times --> |
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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 Data 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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<!-- ## Results |
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More information needed |
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# Model Examination |
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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 |
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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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`````` |
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@misc{taylor2024efficiency, |
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title={Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks}, |
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author={Niall Taylor and Upamanyu Ghose and Omid Rohanian and Mohammadmahdi Nouriborji and Andrey Kormilitzin and David Clifton and Alejo Nevado-Holgado}, |
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year={2024}, |
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eprint={2402.10597}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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`````` |
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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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<!-- # Model Card Authors [optional] --> |
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<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. --> |
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<!-- # Model Card Contact --> |
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