readme: ---
  
  tags:
  - t5
  
  ---
  # Model Card for  diabetes-t5-small 
   
   
  # Model Details
   
  ## Model Description
   
  - **Developed by:** UCI NLP
  - **Shared by [Optional]:** More information needed
  - **Model type:** Text2Text Generation
  - **Language(s) (NLP):** More information needed
  - **License:** More information needed
  - **Related Models:** T5-small
    - **Parent Model:** T5
  - **Resources for more information:** 
      - [Associated Paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf)
   	 - [Google's T5 Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) 
   	 - [GitHub Repo](https://github.com/google-research/text-to-text-transfer-transformer)
    	- [Hugging Face T5 Docs](https://huggingface.co/docs/transformers/model_doc/t5)
    
   
   
  # Uses
   
   
  ## Direct Use
   
  This model can be used for the task of Text2Text Generation
   
  ## Downstream Use [Optional]
   
  More information needed
   
  ## Out-of-Scope Use
   
  The model should not be used to intentionally create hostile or alienating environments for people.
   
  # Bias, Risks, and Limitations
   
  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.
   
   
  ## Recommendations
   
  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
   
   
  # Training Details
   
  ## Training Data
   
  The model is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5.
   
  The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**.
  See the [T5-small model card](https://huggingface.co/t5-small?text=My+name+is+Wolfgang+and+I+live+in+Berlin) for further training data details.
   
  ## Training Procedure
   
   
  ### Preprocessing
   
  More information needed
   
  ### Speeds, Sizes, Times
   
  More information needed
   
  # Evaluation
   
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
   
  More information needed
   
  ### Factors
   
   
  ### Metrics
   
  More information needed
  ## Results 
   
  More information needed
   
  # Model Examination
   
  More information needed
   
  # Environmental Impact
   
   
  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:** More information needed
  - **Hours used:** More information needed
  - **Cloud Provider:** More information needed
  - **Compute Region:** More information needed
  - **Carbon Emitted:** More information needed
   
  # Technical Specifications [optional]
   
  ## Model Architecture and Objective
   
  More information needed
   
  ## Compute Infrastructure
   
  More information needed
   
  ### Hardware
   
  More information needed
   
  ### Software
  More information needed
   
  # Citation
   
   
  **BibTeX:**
  ```bibtex
  @article{2020t5,
    author  = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
    title   = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
    journal = {Journal of Machine Learning Research},
    year    = {2020},
    volume  = {21},
    number  = {140},
    pages   = {1-67},
    url     = {http://jmlr.org/papers/v21/20-074.html}
  }
  ```
   
   
  **APA:**
  - Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67.
   
   
  # Glossary [optional]
  More information needed
   
  # More Information [optional]
   
  More information needed
   
  # Model Card Authors [optional]
   
   
  UCI NLPin collaboration with Ezi Ozoani and the Hugging Face team
   
  # Model Card Contact
   
  More information needed
   
  # 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 transformers import AutoTokenizer, AutoModelForSeq2SeqLM
   
  tokenizer = AutoTokenizer.from_pretrained("ucinlp/diabetes-t5-small")
   
  model = AutoModelForSeq2SeqLM.from_pretrained("ucinlp/diabetes-t5-small")
   
  ```
   
  </details>
  
  